Business processing methods, devices, computer equipment and storage media

Abstract

This application provides a business processing method, apparatus, computer device, and storage medium, relating to the field of business processing technology. The business processing method includes: acquiring user business information; traversing a pre-set set of business rules based on the user business information to obtain a target business rule expression corresponding to the user business information; wherein the set of business rules includes business rule expressions corresponding to multiple business activities, and the business rule expressions are used to describe the constraints of the corresponding business activities; and executing the business activity corresponding to the target business rule expression based on the user business information. This method can suppress the expansion of business rule data storage, improve business rule matching efficiency, and maintain the high flexibility of business rules.

Description

Technical Field

[0001] This application relates to the field of business processing technology, and in particular to a business processing method, apparatus, computer equipment, and storage medium. Background Technology

[0002] In modern business systems, especially in complex decision-making scenarios involving risk control and marketing, it is often necessary to match rules based on user information and multi-dimensional conditions to trigger corresponding business activities.

[0003] In multi-dimensional business rule systems, when a business rule involves numerous dimensions, each with a massive number of selectable values ​​(e.g., city codes, product codes, user tags, etc. across the country), directly storing all selectable values ​​for all dimensions within the business rule would result in an exponential, Cartesian product-like growth in the number of business rules. This leads to an enormous data volume for a single business rule, causing significant waste of storage space and making data maintenance, updates, and backups extremely difficult. Summary of the Invention

[0004] This application provides a business processing method, apparatus, electronic device, and storage medium that can suppress the expansion of business rule data storage, improve business rule matching efficiency, and maintain the high flexibility of business rules.

[0005] The technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a business processing method, the method comprising: Obtain user business information; Based on the user business information, the pre-set set of business rules is traversed to obtain the target business rule expression corresponding to the user business information; wherein, the set of business rules includes business rule expressions corresponding to multiple business activities, and the business rule expressions are used to describe the constraints of the corresponding business activities; Execute the business activity corresponding to the target business rule expression based on the user's business information.

[0006] Based on the aforementioned technical means, user business information is first obtained, and the target business rule expression is matched from a pre-configured set of business rules based on this information, thereby determining the business activity to be executed. The business rule expression provided in this application embodiment can greatly compress the storage space of each rule, achieving the goal of saving storage space. Compared with the storage expansion and computational complexity problems caused by full enumeration or logical inversion of massive value dimensions in the prior art, this method can suppress the data storage expansion of business rules, improve the matching efficiency of business rules, and maintain the high flexibility of business rules.

[0007] In one embodiment, the method further includes: For the first business activity, obtain multiple attributes corresponding to the first business activity; where the first business activity is any one of the multiple business activities; Constraints are determined for each of the multiple attributes corresponding to the first business activity to obtain the business rule expression corresponding to the first business activity; wherein, the constraint of each attribute is used to determine the parameter range of the attribute, and the memory usage of the constraint of the attribute is less than the memory usage of the parameter limited by the parameter range of the attribute.

[0008] Based on the aforementioned technical methods, by setting constraints for each attribute of a business activity individually, its corresponding business rule expression can be constructed flexibly and accurately. Since the attribute constraints only describe the parameter range, rather than listing all possible parameters, they consume less memory compared to listing the parameters limited by the attribute's parameter range, thus saving storage space. This attribute-based modeling approach avoids redundancy and inconsistency in traditional rule definitions, making rule expressions clearer and easier to maintain, while also improving the scalability and adaptability of the rule system.

[0009] In one embodiment, constraints are determined for multiple attributes corresponding to the first business activity, including: For the first attribute, the operation type and value list corresponding to the first attribute are determined according to the business activity; where the first attribute is any one of multiple attributes, the operation type represents the operation logic on the value list, and the value list represents the optional or non-optional values ​​corresponding to the first attribute.

[0010] Based on the aforementioned technical methods, by encapsulating operation types and value lists into intelligent set expressions, efficient expression of massive value dimensions is achieved. This approach not only saves storage space but also simplifies the parsing process of the rule engine, making the matching logic more direct and efficient, and avoiding the performance loss caused by logic transformation in existing technologies.

[0011] In one embodiment, the method further includes: serializing the constraint conditions corresponding to each business rule expression into a structured expression object and preloading it into memory.

[0012] Based on the above technical means, since the business rule expressions provided in this application embodiment can greatly compress the storage space of each rule, it is possible to cache the entire set of active business rules in memory, and lays the foundation for high-speed matching.

[0013] In one embodiment, the user business information includes multiple business attributes. Based on the user business information, a pre-set set of business rules is traversed to obtain the target business rule expression corresponding to the user business information, including: The user's business information includes multiple business attributes, which are compared with each business rule expression. The business rule expression that matches multiple business attributes is determined as the target business rule expression.

[0014] Based on the aforementioned technical methods, by comparing each business attribute with the constraints in the business rule expression, rules applicable to the current business scenario can be accurately identified. This comparison mechanism, combined with the structural characteristics of intelligent set expressions, effectively reduces the computational overhead in the matching process and improves the system's response speed.

[0015] In one embodiment, the user business information includes multiple business attributes, which are compared with each business rule expression, including: Detect whether the first business rule expression contains multiple business attributes included in the user business information; wherein, the first business rule expression is any one of the business rules in the set of business rules; If the first business rule expression contains multiple business attributes of the user business information, determine whether the parameter values ​​of the multiple business attributes of the user business information meet the constraints corresponding to the first business rule expression.

[0016] Based on the aforementioned technical methods, efficient rule filtering is achieved by first determining whether the business rule expression involves the current business attribute, and then further verifying whether the parameter values ​​meet the corresponding constraints. This approach reduces unnecessary calculation steps and improves matching efficiency, especially when dealing with a large number of rules.

[0017] In one embodiment, executing the business activity corresponding to the target business rule expression includes: When there are multiple target business rule expressions, the business activity corresponding to the target business rule expression with the highest priority is executed.

[0018] Based on the aforementioned technical means, when multiple matching target business rule expressions exist, the rule with the highest priority is selected for execution according to a preset priority, ensuring the rationality and consistency of business processing. Compared to the potential rule conflicts or uncertain execution order issues in traditional methods, this solution enhances the controllability and reliability of the rule system by introducing a priority mechanism.

[0019] Secondly, embodiments of this application also provide a business processing apparatus, the apparatus comprising: The receiving module is used to acquire user business information; The traversal module is used to traverse a pre-set set of business rules based on user business information to obtain the target business rule expression corresponding to the user business information; wherein, the set of business rules includes business rule expressions corresponding to multiple business activities, and the business rule expressions are used to describe the constraints of the corresponding business activities; The execution module is used to execute the business activities corresponding to the target business rule expressions based on user business information.

[0020] Thirdly, embodiments of this application also provide a computer device, including a processor and a memory, wherein the memory stores a program or instructions that can run on the processor, and the program or instructions, when executed by the processor, implement the steps of the business processing method as described in any of the above embodiments.

[0021] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program or computer-executable instructions, wherein when the computer program or computer-executable instructions are executed by a processor, the steps of the business processing method as described in any of the above embodiments are implemented.

[0022] Fifthly, embodiments of this application provide a computer program product, including a computer program or computer executable instructions, which, when executed by a processor, implement the steps of the business processing method as described in any of the above embodiments.

[0023] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of this application. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a business processing method provided in an embodiment of this application. Figure 1 ; Figure 2 This is a flowchart illustrating a method for constructing business rule expressions provided in an embodiment of this application; Figure 3 This is a flowchart illustrating a business processing method provided in an embodiment of this application. Figure 2 ; Figure 4 This is a flowchart illustrating a business processing method provided in an embodiment of this application. Figure 3 ; Figure 5 This is a schematic diagram of a business rule expression provided in an embodiment of this application; Figure 6 A logic block diagram of a business processing device provided in an embodiment of this application; Figure 7This is a schematic diagram of the hardware structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0025] In order to gain a more detailed understanding of the features and technical content of the embodiments of this application, the implementation of the embodiments of this application will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for reference and illustration only and are not intended to limit the embodiments of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0027] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0028] It should also be noted that the terms "first, second, and third" used in the embodiments of this application are only used to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0029] Furthermore, the reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] In modern business systems, especially in complex decision-making scenarios involving risk control and marketing, it is often necessary to configure complex business rules based on multiple dimensions such as user information (e.g., type, gender, region) and user behavior (e.g., order time, order type) to trigger corresponding business activities.

[0031] Currently, traditional business rule systems based on multi-dimensional conditions mainly suffer from the following technical shortcomings: 1. Storage Expansion and Redundancy: When business rules involve numerous dimensions, each with a massive number of optional values—for example, codes for over 300 cities nationwide, product codes, and user tags—storing all optional values ​​for all dimensions directly within the business rules would result in an exponential, Cartesian product-like growth in the number of rules, leading to enormous data volumes for individual rules. Consequently, massive amounts of highly redundant business rule data would need to be stored in memory, resulting in significant waste of storage space and making data maintenance, updates, and backups extremely difficult.

[0032] For example, a business rule might include a "city" dimension, with a list of 100 possible city IDs. Every business rule that includes the "city" dimension would need to repeatedly store this "list of 100 city IDs." Assuming there are 100 business rules, each including the "city" dimension, and each requiring the storage of this "list of 100 city IDs," this single dimension's list would need to be stored 100 times. This data redundancy problem is extremely serious.

[0033] 2. Poor query performance: Large amounts of business rule data can easily cause database indexes to become inefficient or even ineffective. For example, when matching business rules, if the business rules involve many dimensions and each dimension has a massive number of possible values, then when filtering conditions, it is necessary to traverse many dimensions and the numerous possible values ​​of each dimension. This will result in excessively long query response times, failing to meet the business requirements of high concurrency and real-time response.

[0034] 3. Poor flexibility and maintainability: In traditional solutions, adding a new dimension or modifying the value range of a dimension requires batch modification or addition of a massive number of rule conditions, resulting in high operational risks and difficulty in ensuring data consistency. The logic of the rules is hard-coded in the data records, lacking a unified and interpretable expression, making it difficult for business personnel to understand and for developers to debug.

[0035] For example, if a new dimension of "user gender" is added, and there were originally 100 business rules, then in response to the addition of this new dimension, 100 more business rules need to be added, ultimately resulting in 200 business rules. Furthermore, these 200 business rules contain a large number of duplicate lists of optional values.

[0036] Another traditional approach involves implicitly representing a large set by "excluding a few exceptions," such as: all except city A are affected. While commonly used in business applications, existing rule engines (such as Drools) or database queries do not readily support this "anti-logic." Therefore, it usually needs to be converted into complex logical statements or the complement of a full enumeration, a process that is complex and also sacrifices storage advantages.

[0037] Therefore, a technical solution is needed that can fundamentally suppress data storage expansion, improve rule matching efficiency, and maintain high flexibility.

[0038] In view of the above-mentioned technical problems, this application provides a business processing method that suppresses the expansion of multi-dimensional rule storage and matching schemes from the source of data storage structure. The core of the business processing method provided by this application lies in the creative proposal of a rule condition expression, especially the intelligent set expression designed for the massive value dimension, which transforms the definition of rules from "enumeration of record rows" to "compact description of conditions", thereby achieving fundamental savings in storage space while ensuring the integrity of expressiveness.

[0039] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0040] In the embodiments of this application, the business processing methods provided in the various embodiments of this application can be executed by electronic devices, such as servers, cloud computing, computer equipment, terminals, wearable devices, etc. The business processing methods provided in the various embodiments of this application can also be executed by processors or chips with processing capabilities.

[0041] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a business processing method provided in an embodiment of this application. Figure 1 The following example, using an electronic device as the executing entity, illustrates this business processing method. Figure 1 As shown, the method may include steps 101 to 103.

[0042] Step 101: Obtain user business information.

[0043] In this embodiment, user business information refers to data generated by users during their use of the business system, specifically including user account information, order information, and behavioral patterns. User business information typically includes multiple business attributes, such as user type (member / non-member), gender, age, region, order time, and order amount.

[0044] For example, when a user places an order on an e-commerce platform, the user's business information, such as the user's level (member), city (Beijing), order type, order quantity, and order amount, may be used in the subsequent business rule expression matching process.

[0045] In this embodiment, user service information can originate from user terminals, such as mobile applications or web browsers, or from third-party system interfaces, and be transmitted over the network to the server of the business system for processing. The user service information can be received in real-time or imported in batches.

[0046] Step 102: Based on the user business information, traverse the pre-set set of business rules to obtain the target business rule expression corresponding to the user business information.

[0047] The business rule set includes business rule expressions corresponding to multiple business activities, and each business rule expression is used to describe the constraints of the corresponding business activity.

[0048] Business rules refer to a set of logical conditions used to control or trigger specific business activities, and are usually associated with multiple dimensions such as user attributes (e.g., gender, region) and behaviors (e.g., order time, order type).

[0049] In this embodiment, the business rule set is a set of structured business rule expressions configured by the system administrator or developers, with each business rule expression corresponding to a specific business activity. A business rule expression is a dynamic description of the conditional logic applicable to a specific business activity. Defined by developers or system administrators, it does not use the traditional enumeration record format but rather a hierarchical data format (such as JSON or XML), thus avoiding the rule redundancy problem caused by multi-dimensional combinations in traditional methods.

[0050] In this embodiment, one business activity corresponds to one business rule expression. This business rule expression describes and specifies all attributes involved in the business activity, clearly defining the parameter range of each attribute within the activity.

[0051] In one implementation, the process of constructing a set of business rules includes: for each business activity, constructing a corresponding business rule expression for that activity. Please refer to [reference needed]. Figure 2 , Figure 2 This is a flowchart illustrating a method for constructing business rule expressions provided in an embodiment of this application.

[0052] Step 201: Obtain multiple attributes corresponding to the first business activity.

[0053] The first business activity can be any one of multiple business activities. Each business activity can be defined as the first business activity when constructing the business rule expression.

[0054] Business activities refer to operational behaviors or scenarios within a system that can be configured with rules, such as user registration, order placement, product browsing, and check-in / check-out. Each business activity typically involves a specific set of business objects and operational logic.

[0055] Attributes are dimensional information used to describe the characteristics of business activities, including but not limited to user type (e.g., member user, regular user), gender, age, region, order amount, timestamp, device type, etc. Attribute values ​​can be discrete (e.g., gender is male / female), continuous (e.g., order time is a specific time period), or have a large number of values ​​(e.g., all prefecture-level cities in China). Each business activity typically involves a set of attributes. Different business activities usually involve different sets of attributes. This difference can refer to differences in attribute dimensions; for example, the attribute set for business activity A includes attribute 1, while the attribute set for business activity B does not. This difference can also refer to differences in the parameter range of the attributes. For example, the parameter range defined by attribute 2 for business activity A is 1-100, while the parameter range defined by attribute 2 for business activity B is 120-150.

[0056] In this embodiment of the application, when processing the first business activity, all attributes of the first business activity are first obtained from the relevant data source. These attributes are the basis for constructing the business rule expression corresponding to the first business activity.

[0057] Through the above-mentioned technical means, in this embodiment of the application, attributes can be extracted for each business activity, providing a data foundation for the subsequent construction of business rule expressions corresponding to the business activities. This ensures that the business rule expressions closely correspond to specific business behaviors, thereby improving the accuracy of the business rule expressions.

[0058] Step 202: Determine the constraints for the multiple attributes corresponding to the first business activity to obtain the business rule expression corresponding to the first business activity.

[0059] Among them, the constraints of an attribute are used to determine the parameter range of the attribute. Constraints refer to the restrictions imposed on the value range or behavioral pattern of an attribute, such as stipulating that the user type must be a member user, or that the order amount must be greater than 500 yuan. Constraints can be a single value, a range value (such as [18,60] representing an age between 18 and 60 years old), or a set (such as a list containing certain city codes).

[0060] The parameter range is a specific range of values ​​or an enumeration set defined according to the constraints, used to limit the conditions under which a certain attribute meets the rule requirements. For example, the parameter range of order amount might be [500, 1000], in yuan. Then, during subsequent rule matching, if the order amount is 800, which is exactly between 500 yuan and 1000 yuan, it means that the order amount attribute meets the parameter range of the order amount attribute in the business rule expression.

[0061] In this embodiment, the memory usage of the constraint condition for each attribute is less than the memory usage of the parameters defined by the parameter range of that attribute. For example, the constraint condition for a certain attribute might be: the parameter range of the order amount is [500, 1000] units. This constraint condition can be clearly expressed using only a small number of bits. However, all parameters defined by the parameter range of this attribute refer to 500, 501, 502, 503, ..., 999, 1000. Listing all parameters defined by the parameter range of this attribute would require a large number of bits. Therefore, in this embodiment, specifying the constraints of each attribute through business rule expressions can greatly compress the storage space of each rule, achieving the goal of saving storage space.

[0062] A business rule expression is a structured data format (such as JSON or XML) used to combine the constraints of various attributes into a complete rule description. A business rule expression not only includes the names and value ranges of each attribute, but may also contain logical operators (such as AND and OR) to connect multiple conditions, thus forming complex business rules.

[0063] It should be noted that the constraints of each attribute corresponding to the first business activity are determined based on the business needs or business behaviors of the first business activity itself.

[0064] In one implementation, staff can determine the multiple attributes associated with the first business activity and the constraints of each attribute based on the content of the first business activity, and then determine the constraints corresponding to each attribute.

[0065] In one implementation, staff can pre-set constraints for multiple attributes. Then, for the first business activity, based on the multiple attributes involved in the first business activity, the constraints corresponding to each attribute are obtained from the preset data. The constraints corresponding to each attribute are combined to obtain the business rule expression corresponding to the first business activity.

[0066] For example, the process of determining the constraints for each attribute is described below using the first attribute as an example. For the first business activity, the first attribute can be any one of multiple attributes corresponding to the first business activity. The process of determining the constraints for the first attribute includes: determining the operation type and value list corresponding to the first attribute based on the business activity.

[0067] The operation type represents the operation logic on the value list, and the value list represents the optional or non-optional value corresponding to the first attribute.

[0068] The first attribute can be a specific attribute involved in the first business activity, such as user type, gender, city, or order amount. As the basic unit for rule configuration, each first attribute has its own value range or restrictions. For example, in a user operations scenario, the city can be a first attribute, and the list of values ​​corresponding to this first attribute might include specific prefecture-level city names or identifiers such as Beijing, Shanghai, and Guangzhou.

[0069] The operation type describes how the system filters the list of values. Common operation types include include and exclude. When the operation type is include, the system only selects the values ​​specified in the value list as matching conditions. When the operation type is exclude, the system excludes the values ​​specified in the value list, and all values ​​outside the value list are considered to meet the conditions. For example, when the operation type is exclude, and the value list contains Hangzhou and Nanjing, all cities other than Hangzhou and Nanjing will meet the conditions.

[0070] A value list is a set of candidate values ​​associated with a specific primary attribute, used to express the scope of application of the primary attribute in a rule. The value list can be discrete (e.g., [male, female]), continuous range (e.g., order time intervals), or a massive intelligent set of values ​​(e.g., [330100, 330200], representing the administrative division codes of Hangzhou and Ningbo in Zhejiang Province). By introducing value lists and operation types, the system can accurately express complex business rules without enumerating all possible combinations.

[0071] There is a clear logical relationship between the operation type and the value list. The operation type determines how the contents of the value list are processed, thus affecting the final rule matching result. For example, when the operation type is `include`, only the specific values ​​contained in the value list will be matched; while when the operation type is `exclude`, the values ​​listed in the value list will be ignored, and only the values ​​not listed will be matched. The flexible control provided by the logical relationship between the operation type and the value list allows the system to adapt to the rule requirements of different business scenarios.

[0072] For example, suppose a business activity aims to push promotional information to all users nationwide except those in Hangzhou and Nanjing. In this case, the city can be one of several attributes corresponding to the business activity. During configuration, the city can be set as the first attribute, the operation type set to `exclude`, and the value list set to `[Hangzhou, Nanjing]`. By including the city as an attribute and setting the operation type and value list, users in these two cities can be automatically filtered out in practical applications, and messages can only be sent to users in other cities. For attributes with a large number of possible values ​​(such as city or product category), only a small number of actually selected or excluded values ​​need to be stored to achieve accurate matching. This avoids the redundant storage problems caused by configuring rules separately for each city, significantly reducing storage space usage and improving operational efficiency and maintenance convenience.

[0073] For example, for the user check-in business activity, constraints can be set such that the user type is a member and the number of check-in days is greater than or equal to 7 days. Accordingly, the business rule expression includes two dimensions: user type and number of check-in days. In the constraint for user type, the operation type is `include`, and the value list can be discrete: `[Member]`. In the constraint for number of check-in days, the operation type is `include`, and the value list can be a continuous range: greater than or equal to 7 days. In this way, the business rule can be explicitly defined with a single business rule expression without enumerating the number of check-in days.

[0074] In this embodiment, by configuring operation types and value lists for each attribute involved in the first business activity, business rule expressions that meet business requirements can be flexibly constructed, and efficient modeling and flexible configuration of business rules can be achieved. This method avoids the data bloat problem caused by traditional enumeration rules, thereby reducing database storage pressure and improving query performance. In this embodiment, each business rule expression can be preloaded into a memory cache. Based on the method of constructing business rule expressions provided in this embodiment, the storage space of each business rule expression can be greatly compressed. This compression effect makes it possible to cache the entire set of business rules in memory and lays the foundation for high-speed matching.

[0075] In this embodiment of the application, after constructing and preloading the business rule set, each business rule expression in the business rule set can be traversed according to the user's business information to determine the target business rule expression that matches the user's business information.

[0076] Please refer to the following: Figure 3 , Figure 3 This is a flowchart illustrating a business processing method provided in an embodiment of this application. Figure 2 The process of traversing each business rule expression in the business rule set based on user business information includes: Step 301: Compare the multiple business attributes included in the user business information with each of the aforementioned business rule expressions.

[0077] Step 302: Determine the business rule expression that matches multiple business attributes as the target business rule expression.

[0078] User business information includes multiple business attributes, which are characteristic parameters that describe a user's business behavior or status, such as the user's gender, age, region, consumption amount, order type, and purchase time.

[0079] The business rule set can be preloaded into a memory cache to improve the matching speed of business rule expressions. In this embodiment, the constraints in the business rule expressions are serialized into structured expression objects for easy dynamic matching.

[0080] During the comparison, the rule interpreter can be invoked to compare multiple business attributes in the user's business information with each business rule expression one by one, determining whether the multiple business attributes in the user's business information meet the constraints corresponding to the multiple attributes in the currently found business rule expressions. Once a matching business rule expression is found, the target business rule expression can be determined and the corresponding business activity can be prepared for execution.

[0081] For each business rule expression, the rule interpreter reads the constraints of each attribute in the business rule expression, dynamically interprets and executes the execution logic corresponding to the constraints of each attribute, and compares it with the user's business information attribute by attribute.

[0082] In one implementation, it can be detected whether the first business rule expression contains multiple business attributes corresponding to user business information. The first business rule expression can be any business rule expression from the set of business rules.

[0083] For example, if the user's business information corresponds to multiple business attributes A, B, C, and D, and the first business rule expression involves multiple attributes A, C, D, and E, then the first business rule expression does not include the multiple business attributes corresponding to the user's business information. If the first business rule expression involves multiple attributes A, B, C, and D, then the first business rule expression includes the multiple business attributes corresponding to the user's business information.

[0084] If the first business rule expression contains multiple business attributes of the user business information, determine whether the parameter values ​​of the multiple business attributes of the user business information meet the constraints corresponding to the first business rule expression.

[0085] Continuing with the example above, let's say the user's business information corresponds to multiple business attributes, A, B, C, and D, and the first business rule expression involves multiple attributes, also A, B, C, and D. We can then determine, attribute by attribute, whether each business attribute in the user's business information meets the constraints of its corresponding attribute in the first business rule expression. That is, we determine whether business attribute A meets the constraints corresponding to attribute A in the first business rule expression. If it does, we continue to determine whether business attribute B meets the constraints corresponding to attribute B in the first business rule expression, and so on, until all business attributes are compared. If all comparisons are successful, then the first business rule expression is determined as the target business rule expression. If none of the comparisons are successful, it means the first business rule expression comparison failed, and a new first business rule expression is determined, and the comparison process is repeated.

[0086] In one implementation, during the traversal of the business rule set, the traversal can end when the target business rule expression is determined for the first time, and the remaining business rule expressions that have not been compared will no longer be compared.

[0087] In another implementation, during the traversal of the business rule set, all business rule expressions in the set can be traversed once. In this case, one or more target business rule expressions may be determined.

[0088] By introducing an attribute-rule expression comparison mechanism, this method avoids the redundancy problems associated with traditional enumeration-based rules. Compared to directly creating independent rule records for each possible combination of conditions, the business processing method provided in this application adopts a structured expression approach, allowing the same rule conditions to be reused in multiple different business scenarios, thereby significantly reducing the number of rules and improving the maintainability and performance of the system.

[0089] Step 103: Execute the business activity corresponding to the target business rule expression based on the user business information.

[0090] In this embodiment, a business activity refers to a specific operation triggered by a matched business rule expression, such as issuing coupons, modifying user levels, sending notification emails, or adjusting inventory. The execution of a business activity can be synchronous or asynchronous, depending on business requirements and system architecture design.

[0091] For example, suppose the business activity corresponding to the target business rule expression of the user's business information is to send a promotional email to the user. Then, the business activity is executed, that is, the email service module is called to determine the email template, extract the variable information from the email template from the user's business information, fill the variable information into the email template to obtain the email content, and then send the email content to the user's registered email address.

[0092] For example, if the business activity corresponding to the target business rule expression of the user's business information is to deduct points, then the user account can be determined based on the user's business information, the user account's points balance can be updated, and relevant operation logs can be recorded.

[0093] In one embodiment of this application, when there are multiple target business rule expressions, the business activity corresponding to the target business rule expression with the highest priority is executed.

[0094] When user business information matches multiple target business rule expressions, the priority fields pre-configured for these multiple target business rule expressions can be obtained, and the target business rule expression with the highest priority can be selected for execution.

[0095] For example, the priority field can be an integer value, with smaller values ​​indicating higher priority (or vice versa; the specific implementation depends on the actual design). By executing the business activity corresponding to the target business rule expression with the highest priority, it can be ensured that when multiple rules apply to the current user's business information, the final rule to be executed can be determined according to a clear priority order.

[0096] In this application embodiment, the priority of each business rule expression is usually set by the staff through the configuration interface and stored in a structured data format, such as the priority field in a JSON object. The priority field is not only used for sorting, but also for conflict detection and arbitration, ensuring that consistent and predictable decisions can still be made when multiple rules coexist.

[0097] By introducing a priority mechanism, uncertainties caused by overlapping rules can be avoided. For example, in a marketing campaign, multiple business rule expressions may apply to the current user's business information, such as new user discounts, member-exclusive offers, and holiday promotions. In this case, the most suitable business rule expression can be automatically selected for execution based on its priority field, thus ensuring the consistency and controllability of business logic.

[0098] In summary, the business processing method provided in this application embodiment achieves efficient management and rapid response of multi-dimensional business rules by introducing a structured business rule expression model. Compared with the traditional enumeration-based rule configuration method, the business processing method provided in this application embodiment not only significantly reduces storage costs but also improves the efficiency of business rule expression matching, enhances flexibility and maintainability, and is suitable for various complex business scenarios.

[0099] The following describes the business processing method provided in the embodiments of this application in conjunction with practical applications.

[0100] First, the technical concept of the business processing method provided in the embodiments of this application will be described. The business processing method provided in the embodiments of this application is a method for data storage, matching, and management of multi-dimensional dynamic rules. The core lies in the creative proposal of a dimensional condition expression (i.e., business rule expression) storage model and an interpreted real-time matching engine (i.e., rule interpreter), which transforms the definition of rules from the enumeration of data rows to the dynamic description of conditions, thereby technically achieving effective control over data expansion and a significant improvement in query performance.

[0101] This application creatively defines a dedicated data structure for intelligent set expressions. The core of intelligent set expressions lies in encapsulating the operation logic as part of the data, integrated with the value list. Specifically, an intelligent set expression is created for each dimension, which can be represented by a data structure tuple containing two indivisible fields: operator (op) (operation type) and value (value list).

[0102] For example, the data structure tuple is defined as follows: {"op":"IN","values":[v1,v2]} indicates that the operation type is "contains": the dimension values ​​must appear in values ​​(the list of values).

[0103] {"op":"NOT_IN","values":[v3,v4]} indicates that the operation type is "exclusion" mode: the dimension value must not appear in values ​​(the list of values).

[0104] When op="IN", the values ​​list should not be empty.

[0105] When op="NOT_IN", the values ​​list can be empty, indicating that no values ​​are excluded (i.e., the entire set is valid).

[0106] In this embodiment of the application, the operation type and value list corresponding to each dimension (attribute) can be expressed in JSON format, for example: json { “dimension_name”:{ "op":"IN"|"NOT_IN", "values":["value_identifier_1","value_identifier_2",...] }} For the "exclusion" mode, only a few exception values ​​(such as [v3,v4]) need to be stored to represent the rule conditions of a massive set minus a few exceptions. This reduces the storage overhead from being related to the size of the entire set to being related only to the number of exception values, thus solving the problem of data storage expansion.

[0107] The business processing method provided in this application embodiment provides explicit, non-conversion-required constraints in the business rule expression. When performing comparison, the rule interpreter is called to select "search within the list" or "no search within the list" for the value list based on the operation type (op) of each dimension. The logic is simple and constant, laying the foundation for high-speed matching in memory.

[0108] Secondly, the specific implementation methods of the business processing methods provided in the embodiments of this application will be described.

[0109] Please refer to Figure 4 , Figure 4 This is a flowchart illustrating a business processing method provided in an embodiment of this application. Figure 3 .

[0110] Step 401, rule object construction.

[0111] In this embodiment, a dedicated configuration interface can be used to define multi-dimensional attributes for each business activity. These multi-dimensional attributes include one or more of the following dimensions: user, order, time, and geographic. Constraints are then defined for each dimension based on the business activity, and the multi-dimensional attributes are serialized with their respective constraints into a structured dimension condition expression object (i.e., a business rule expression) for storage.

[0112] The dimension condition expression object (i.e., business rule expression) can be defined using a hierarchical data format, such as JSON, XML, or Protocol Buffers. The core characteristics of the dimension condition expression object (i.e., business rule expression) are: for enumerable discrete dimensions (such as user type or gender), the range of values ​​for these discrete dimensions is expressed using a list of values ​​or key-value pairs. For continuous or range-based dimensions (such as order time), range expressions (e.g., {"start": "2025-01-01", "end": "2025-12-31"}) are used for description. For dimensions with a massive number of values ​​(such as those involving hundreds of prefecture-level cities), intelligent set expressions are used. These intelligent set expressions can be represented using a data structure tuple, which contains two indivisible fields: operator (op) (operation type) and values ​​(value list). This approach, by employing a pattern that includes or excludes a small number of values, can accurately describe the filtering conditions for a massive number of possible values ​​by storing only the small number of values ​​that are actually selected or need to be excluded. This completely avoids the problem of creating redundant records for all possible values ​​from the perspective of the storage mechanism.

[0113] A complete business rule expression can be represented as a "rule object", which can contain multiple attributes of different dimensions. Each attribute corresponds to different constraints. Smart set expressions can be used to describe massive value-dimensional attributes.

[0114] Please refer to Figure 5 , Figure 5 This is a schematic diagram of a business rule expression provided in an embodiment of this application.

[0115] The business rule expression is also called a dimension condition expression object. Each business rule expression includes a rule number, a rule name, a set of conditions, and a business activity number. The rule number is represented by "id", the rule name by "name", the set of conditions by "conditions" (which refers to the set of constraints corresponding to the multi-dimensional attributes), and the business activity number by "action".

[0116] For example, a business rule expression can be represented as follows: json { “id”:“rule_001”, "name": "VIP User Promotion Rules for Beijing, Shanghai, Guangzhou and Shenzhen" “conditions”:{ “user_tier”:{“op”:“IN”,“values”[“vip”]}, / / Other types of dimension conditions (such as simple lists) “city”:{“op”:“IN”,“values”: [“110000”,“310000”,“440100”,“440300”] / / Beijing, Shanghai, Guangzhou, Shenzhen}, / / Smart set expression: containing pattern “blacklisted_city”:{“op”:“NOT_IN”,“values”:[“330300”] / / Wenzhou} / / Smart set expression: exclusion pattern, } Figure 5 The example demonstrates the following attributes: User Tier (user_tier), City (city), Blacklisted City (blacklisted_city), and Order Amount (order_amount). The User Tier (user_tier) operation type includes a list of values, such as VIP, structured as an array. The City smart set expression includes an operation type and a list of values, where the operation type is IN and the list of values ​​is, for example, ["110000", "310000", "440100", "440300"]. The Blacklisted City smart set expression includes an operation type and a list of values, where the operation type is NOT_IN and the list of values ​​is, for example, ["330300"]. The Order Amount (order_amount) range field describes [100, 500].

[0117] Please refer to Figure 5 As shown, the business activity corresponding to this business rule expression is represented as follows: The action type is: send a coupon with a value of 50.

[0118] In this embodiment of the application, the above-mentioned business rule expression is stored in the database as an independent record. One business rule expression always corresponds to only one storage record, which fundamentally avoids the Cartesian product explosion.

[0119] Step 402, Obtain business facts.

[0120] In this embodiment, a business fact can be constructed based on real-time data such as current user attributes and order information. The business fact is user business information. For example, a business fact could be: {user_tier: “vip”, city:“110000”}.

[0121] Step 403, fact matching.

[0122] In this embodiment, all business rule expressions (i.e., the set of stored business rules) can be loaded into the memory of the matching engine. Since the business rule expressions provided in this embodiment can significantly compress the storage space of each rule, it becomes possible to cache the entire set of active business rules in memory, laying the foundation for high-speed matching.

[0123] When processing a business fact, the rule interpreter can be invoked to traverse the set of business rules in memory. For each business rule expression, the rule interpreter dynamically interprets and executes the conditional logic defined in the business rule expression, comparing it dimension-by-dimensionally with the business fact. The traversal process includes: Dimensional matching: For each condition in the rule object: If this dimension does not exist in reality, skip it.

[0124] If the condition is a simple list (such as user_tier), check if the fact value is in the list.

[0125] If the condition is a smart set expression (such as city or blacklisted_city): Extract the value of the corresponding dimension from the facts (fact_value).

[0126] Read the op field.

[0127] If op == "IN": the match is successful if and only if fact_value exists in the values ​​list.

[0128] If op == “NOT_IN”: The match is successful if and only if fact_value does not exist in the values ​​list.

[0129] Rule hit: If all dimension attributes of a business rule expression match successfully, then the business rule expression is hit, and the business activity corresponding to the business rule expression is executed, that is, its action is executed.

[0130] Furthermore, embodiments of this application also include a rule conflict detection and priority management module. This module is used for arbitration based on a pre-configured priority field for each business rule expression.

[0131] In summary, the beneficial effects of the business processing method provided in this application are reflected in the following aspects: By employing a dimensional conditional expression model, particularly the design of intelligent set expressions, the rule storage requirement is reduced from a product relationship with the number of dimension values ​​to a linear relationship with the number of rules. For example, when processing the city dimension with 300 cities, traditional solutions might require generating 300 sub-records for a select-all operation, while this solution only needs to store one record in the same scenario, and by setting the constraint to all cities on that dimension, data storage efficiency is improved by hundreds of times compared to traditional solutions.

[0132] By storing the set of business rules in memory and interpreting and executing the matching process in memory, complex database join queries and disk I / O operations are avoided. Thanks to this mechanism, matching speeds can reach the millisecond level, thus supporting high-concurrency, real-time business scenarios.

[0133] It greatly enhances the system's flexibility and maintainability: When a new dimension is added to a business activity, you only need to add the new dimension field to the business rule expression corresponding to the business activity and configure the constraints for the new dimension. There is no need to change the database table structure or add massive amounts of data.

[0134] It should be noted that the business processing solution provided in this application embodiment organically combines structured sequences, memory caching strategies, and rule interpreters to jointly serve the unified technical objective of solving the storage and query performance bottlenecks caused by multi-dimensional combined rules.

[0135] It should be understood that the steps in the aforementioned accompanying drawings are not necessarily performed in the order indicated in the drawings. Unless otherwise expressly stated herein, there is no strict order restriction on the execution of these steps, and they may be performed in other orders. Moreover, at least some of the steps in these drawings may include multiple sub-steps or multiple stages, which are not necessarily completed at the same time, but may be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0136] In another embodiment of this application, a business processing apparatus is provided, please refer to... Figure 6 , Figure 6A logic block diagram of a service processing apparatus provided in an embodiment of this application. The service processing apparatus 600 may include: a receiving module 601, a traversal module 602, and an execution module 603, wherein: Receiver module 601 is used to obtain user service information; The traversal module 602 is used to traverse a pre-set set of business rules based on user business information to obtain the target business rule expression corresponding to the user business information; wherein, the set of business rules includes business rule expressions corresponding to multiple business activities, and the business rule expressions are used to describe the constraints of the corresponding business activities. The execution module 603 is used to execute the business activities corresponding to the target business rule expression based on the user's business information.

[0137] In one embodiment, the execution module 603 is further configured to: For the first business activity, obtain multiple attributes corresponding to the first business activity; where the first business activity is any one of the multiple business activities; Constraints are determined for each of the multiple attributes corresponding to the first business activity to obtain the business rule expression corresponding to the first business activity; wherein, the constraint of each attribute is used to determine the parameter range of the attribute, and the memory usage of the constraint of the attribute is less than the memory usage of the parameter limited by the parameter range of the attribute.

[0138] In one embodiment, the execution module 603 is further configured to: For the first attribute, the operation type and value list corresponding to the first attribute are determined according to the business activity; where the first attribute is any one of multiple attributes, the operation type represents the operation logic on the value list, and the value list represents the optional or non-optional values ​​corresponding to the first attribute.

[0139] In one embodiment, the execution module 603 is further configured to: The constraints corresponding to each business rule expression are serialized into a structured expression object and preloaded into memory.

[0140] In one embodiment, the user service information includes multiple service attributes, and the traversal module 602 is further used for: The user's business information includes multiple business attributes, which are compared with each business rule expression. The business rule expression that matches multiple business attributes is determined as the target business rule expression.

[0141] In one embodiment, the traversal module 602 is further configured to: Detect whether the first business rule expression contains multiple business attributes included in the user business information; wherein, the first business rule expression is any one of the business rules in the set of business rules; If the first business rule expression contains multiple business attributes of the user business information, determine whether the parameter values ​​of the multiple business attributes of the user business information meet the constraints corresponding to the first business rule expression.

[0142] In one embodiment, the execution module 603 is further configured to: When there are multiple target business rule expressions, the business activity corresponding to the target business rule expression with the highest priority is executed.

[0143] Each module in the aforementioned business processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, 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.

[0144] In another embodiment of this application, please refer to Figure 7 , Figure 7 This is a schematic diagram of the hardware structure of a computer device provided in an embodiment of this application. The computer device may include a communication interface 701, a memory 702, and a processor 703; the various components are coupled together through a bus system 704. It is understood that the bus system 704 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 704 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 7 The general designated all buses as Bus System 704.

[0145] In this embodiment, the communication interface 701 is used to send and receive information with other external devices; the memory 702 is used to store computer programs that can run on the processor 703; the processor 703 is used to execute the steps of the business processing method described in any of the foregoing embodiments when running the computer program.

[0146] It is understood that the memory 702 in this embodiment can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous DRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 702 of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0147] The processor 703 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 703 or by software instructions. The processor 703 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 702, and the processor 703 reads the information in memory 702 and, in conjunction with its hardware, completes the steps of the above method.

[0148] It is also understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0149] For software implementation, the techniques described herein can be implemented through modules (e.g., procedures, functions, etc.) that perform the functions described herein. Software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or externally. Wherein, if implemented as a software functional module and not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0150] In another embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the business processing method described in the foregoing embodiments.

[0151] In another embodiment of this application, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps of the business processing method as described in the foregoing embodiments.

[0152] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, devices, or computer program products. Therefore, this application can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage) containing computer-usable program code.

[0153] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0154] The sequence numbers of the embodiments in this application are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The features disclosed in the several product embodiments provided in this application can be arbitrarily combined to obtain new product embodiments without conflict. Similarly, the features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined to obtain new method or device embodiments without conflict. The above descriptions are merely specific implementations of this application, but the protection scope of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the protection scope of this application.

Claims

1. A service processing method characterized by, The method includes: Obtain user business information; The target business rule expression corresponding to the user business information is obtained by traversing a pre-set set of business rules based on the user business information; wherein, the set of business rules includes business rule expressions corresponding to multiple business activities, and the business rule expressions are used to describe the constraints of the corresponding business activities; The business activity corresponding to the target business rule expression is executed based on the user business information.

2. The method of claim 1, wherein, The method further includes: For the first business activity, obtain multiple attributes corresponding to the first business activity; wherein, the first business activity is any one of the multiple business activities; Constraints are determined for multiple attributes corresponding to the first business activity to obtain the business rule expression corresponding to the first business activity; wherein, the constraint of each attribute is used to determine the parameter range of the attribute, and the memory usage of the constraint of the attribute is less than the memory usage of the parameter limited by the parameter range of the attribute.

3. The method of claim 2, wherein, The step of determining constraints for the multiple attributes corresponding to the first business activity includes: For the first attribute, the operation type and value list corresponding to the first attribute are determined according to the business activity; wherein, the first attribute is any one of the plurality of attributes, the operation type represents the operation logic on the value list, and the value list represents the optional or non-optional value corresponding to the first attribute.

4. The method according to claim 2 or 3, characterized in that, The method further includes: The constraints corresponding to each business rule expression are serialized into a structured expression object and preloaded into memory.

5. The method of claim 1, wherein, The user service information includes multiple service attributes. The step of traversing a pre-set set of service rules based on the user service information to obtain the target service rule expression corresponding to the user service information includes: The user service information includes multiple service attributes, which are compared with each of the service rule expressions. The business rule expression that matches all of the aforementioned business attributes is determined as the target business rule expression.

6. The method of claim 5, wherein, The step of comparing the multiple business attributes included in the user business information with each of the business rule expressions includes: Detect whether the first business rule expression contains multiple business attributes included in the user business information; wherein, the first business rule expression is any one of the business rule set; If the first business rule expression includes multiple business attributes of the user business information, determine whether the parameter values ​​of the multiple business attributes of the user business information meet the constraints corresponding to the first business rule expression.

7. The method of claim 1, wherein, The business activity corresponding to the target business rule expression includes: When there are multiple target business rule expressions, the business activity corresponding to the target business rule expression with the highest priority is executed.

8. A business processing apparatus, characterized in that, The device includes: The receiving module is used to acquire user business information; The traversal module is used to traverse a pre-set set of business rules based on the user business information to obtain the target business rule expression corresponding to the user business information; wherein, the set of business rules includes business rule expressions corresponding to multiple business activities, and the business rule expressions are used to describe the constraints of the corresponding business activities; The execution module is used to execute the business activity corresponding to the target business rule expression based on the user business information.

9. A computer device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the steps of the business processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or computer-executable instructions, which, when executed by a processor, implement the steps of the business processing method as described in any one of claims 1 to 7.

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