Entity determination method, electronic device and computer program product

Abstract

The invention provides an entity determination method, electronic equipment and a computer program product. The entity determination method comprises the steps that a first rule set is determined according to a first coding interval corresponding to a first attribute in first data, the first data comprises a plurality of attributes used for screening target entities, the first attribute is used for determining service dimensions of the entities, and the first coding interval is used for determining the entities with the first attribute; the first rule set records rule data of an entity with a first attribute; according to a second coding interval of the entity, multiple pieces of rule data of the entity are determined in the first rule set, the second coding interval is located in the first coding interval, the second coding interval is used for determining the rule data belonging to the entity, and the rule data comprise rule items and an expected value range; and taking the entity as a target entity under the condition that the second attribute of the first data falls into the expected value range of the corresponding rule item.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing and the like. In particular, the present disclosure relates to an entity determination method, an electronic device, and a computer program product. Background Art

[0002] In various industries and fields, matching rules play a crucial role. Through the matching rules of each entity, target entities that meet specific conditions can be screened out from a large number of entities. However, traditional rule management methods often rely on hard-coded logic, writing preset rules into the management system in an encoded form, resulting in the system lacking flexibility in rule iteration and being difficult to adapt to rapidly changing business requirements.

[0003] Although some existing rule engines can strip the matching rules from the system, making the rules clearer, the learning cost of these tools is relatively high. Moreover, rule iteration still requires the formulator to have a certain programming background and an in-depth understanding of the internal processing logic of the rule engine. Obviously, the flexibility of system configuration is still difficult to meet the speed of rule iteration. Summary of the Invention

[0004] The present disclosure provides an entity determination method, an electronic device, and a computer program product.

[0005] According to one aspect of the present disclosure, an entity determination method is provided, including: determining a first rule set according to a first coding range corresponding to a first attribute in first data, the first data including multiple attributes for screening target entities, the first attribute being used to determine the business dimension of the entity, the first coding range being used to determine entities having the first attribute, and the first rule set recording rule data of entities having the first attribute; determining multiple rule data of the entity in the first rule set according to a second coding range of the entity, the second coding range being within the first coding range and being used to determine rule data belonging to the entity, the rule data including rule items and expected value ranges; and taking the entity as a target entity when a second attribute of the first data falls within the expected value range of the corresponding rule item, the second attribute including actual values of at least some of the rule items.

[0006] In some embodiments, before determining the first rule set, it includes: generating multiple first rule sets according to the entity data of each entity, the entity data including at least the rule data and the business dimension of the entity, the first rule sets having first coding ranges, and the first coding ranges not overlapping and being set in coding order.

[0007] In some embodiments, multiple first rule sets are generated according to the entity data of each of the entities, including: sorting the entities belonging to the same business dimension and the rule data of the entities into an original set; configuring the first coding range for the business dimension in the original set; configuring the second coding range for each of the entities belonging to the business dimension, where the second coding range is within the first coding range; and configuring a third coding range for each of the rule data in the order of configuration of the rule data of the entities, to form the first rule set having the first coding range, the second coding range, and the third coding range, where the third coding range is within the second coding range.

[0008] In some embodiments, after new rule data is added to the entity data, the configuration order of the new rule data is determined; when the configuration order of the new rule data is not the first, according to the configuration order of the new rule data in the entity data, the associated rule data adjacent and before the new rule data is determined; the product of the quantity of the new rule data and the interval span of the third coding range is used as the first interval shift value, and each of the third coding ranges has the same interval span; in each of the first rule sets, the first interval shift value is added to each coding range whose value is greater than the third coding range of the associated rule data, and the new coding range is formed after each coding range is added with the first interval shift value; and in the coding order, the coding range having the interval span after the third coding range of the associated rule data is used as the third coding range of the new rule data, to form a new first rule set including the new rule data.

[0009] In some embodiments, after determining the configuration order of the new rule data, it further includes: when the configuration order of the new rule data is the first, using the third coding range of the original first rule data of the entity as the third coding range of the new rule data; using the product of the quantity of the new rule data and the interval span as the first interval shift value; and adding the first interval shift value to the coding ranges greater than or equal to the third coding range of the new rule data in the first rule set, to form a new first rule set including the new rule data.

[0010] In some embodiments, after generating a plurality of first rule sets, it includes: when deleting rule data from the entity data, taking the product of the number of deleted rule data and the interval span of the third coding interval as the second interval shift value, where each of the third coding intervals has the same interval span; and according to the configuration order of the deleted rule data in the entity data, in each of the first rule sets, subtracting the second interval shift value from each coding interval whose value is greater than the third coding interval of the deleted rule data to form a new first rule set that does not include the deleted rule data, and each coding interval forms a new coding interval after subtracting the second interval shift value.

[0011] In some embodiments, before taking the entity as the target entity, it further includes: comparing each second attribute of the first data with each rule data of the entity to determine whether each second attribute falls within the expected value range of the corresponding rule item.

[0012] In some embodiments, after comparing each second attribute of the first data with each rule data of the entity to determine whether each second attribute falls within the expected value range of the corresponding rule item, it further includes: when there is at least one second attribute that does not fall within the expected value range of the corresponding rule item for the rule data of each entity in the first rule set, determining the service dimension range of the compensation entity, where the expected value range of at least some rule items of the compensation entity is greater than the expected value range of the corresponding rule items of the entity; in the case where the service dimension falls within the service dimension range, determining the rule data of the compensation entity according to the fourth coding interval of the compensation entity; and comparing each second attribute of the first data with each rule data of the compensation entity to determine whether each second attribute falls within the expected value range of the corresponding rule item.

[0013] In some embodiments, after comparing each second attribute of the first data with each rule data of the compensation entity to determine whether each second attribute falls within the expected value range of the corresponding rule item, it further includes: in the case where each second attribute of the first data falls within the expected value range of the corresponding rule item, taking the compensation entity as the target entity.

[0014] According to one aspect of the present disclosure, there is provided an electronic device, including: a memory that stores execution instructions; and a processor that executes the execution instructions stored in the memory, such that the processor executes the entity determination method according to any embodiment of the present disclosure.

[0015] According to one aspect of the present disclosure, there is provided a readable storage medium storing execution instructions, which when executed by a processor are used to implement the entity determination method according to any embodiment of the present disclosure.

[0016] According to one aspect of the present disclosure, there is provided a computer program product including a computer program, which when executed by a processor implements the entity determination method according to any embodiment of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, are used to explain the principles of the present disclosure. The drawings are included to provide a further understanding of the present disclosure and are incorporated in this specification and form a part of this specification.

[0018] Figure 1 It is a schematic diagram of an application scenario of the entity determination method according to an embodiment of the present disclosure.

[0019] Figure 2 It is a flowchart of the entity determination method according to an embodiment of the present disclosure.

[0020] Figure 3 It is a schematic diagram of the process of determining a target entity according to an embodiment of the present disclosure.

[0021] Figure 4 It is a visualization graph of a first rule set according to an embodiment of the present disclosure.

[0022] Figure 5 It is a schematic diagram of adding new rule data according to an embodiment of the present disclosure.

[0023] Figure 6 It is a schematic block diagram of the structure of an entity determination device according to an embodiment of the present disclosure.

[0024] Figure 7 It is a schematic block diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The present disclosure will be further described in detail below with reference to the drawings and examples. It can be understood that the specific examples described herein are only for explaining the relevant content and are not intended to limit the present disclosure. Additionally, it should be noted that for the sake of description, only parts related to the present disclosure are shown in the drawings.

[0026] It should be noted that, without conflict, the embodiments in the present disclosure and the features in the embodiments may be combined with each other. The technical solutions of the present disclosure will be described in detail below with reference to the drawings and in combination with the embodiments.

[0027] In various industries and fields, matching rules play a crucial role. Taking the real estate transaction scenario as an example, theoretically, there are multiple banks that can be used as payment channels when the payer makes a payment. However, due to the different capabilities of each bank (for example, Bank A supports minor customers while Bank B does not), it is necessary to match the payer's information with the capabilities of the same bank to screen out the banks that can provide services for the payer. The traditional method is to write the capabilities of the bank as rules in the form of code into the management system to automatically determine the target bank. This method is not flexible enough when the rules are iterated. It requires the formulator to recode at least part of the new rules and the system, with high rule iteration costs and high requirements for the formulator. Although some existing rule engines can strip the matching rules from the system to make the rules clearer, the learning costs of these tools are relatively high. Moreover, the iteration of the rules still requires the formulator to have a certain programming background and in-depth understanding of the internal processing logic of the rule engine. Obviously, the flexibility of system configuration is still difficult to meet the iteration speed of the rules.

[0028] To this end, the present disclosure proposes an entity determination method.

[0029] Figure 1 It is a schematic diagram of the application scenario of the entity determination method according to the embodiments of the present disclosure. As Figure 1 shown, in this application scenario, it may include a server 100 and a terminal device 200. The server 100 and the terminal device 200 can be connected through a network or Bluetooth for data interaction. The server 100 can be a cloud server or a physical server, and the terminal device 200 can be an intelligent device such as a computer, a mobile phone, or a tablet. The server 100 is used to provide the basic data required for running the entity determination method, and the terminal device 200 executes the entity determination method of the present disclosure based on the basic data provided by the server 100.

[0030] For the convenience of description and to make the technical solutions of the specific embodiments of the present disclosure easier to understand, before describing the entity determination method implemented in the present disclosure, the technical terms involved in the specific embodiments of the present disclosure are explained as follows: First data: It contains multiple attributes for screening target entities, such as a first attribute and multiple second attributes, etc. In the real estate transaction scenario, the first data can be the personal information of the payer.

[0031] First attribute: It is a business dimension for determining an entity. In the real estate transaction scenario, the first attribute can be the city where the payer is located.

[0032] First rule set: It records the rule data of entities with the first attribute, as well as the corresponding business dimensions, each entity, and the coding intervals of various rule data of the entity.

[0033] Business dimension: It is a common attribute of an entity, such as the city involved in the business.

[0034] Coding range: It includes the first coding range of the business dimension, the second coding range of the entity, and the third coding range of the rule data. The first coding range contains the second coding range, and the second coding range contains the third coding range.

[0035] Rule data: It includes rule items and expected value ranges. In the real estate transaction scenario, the rule data can be the capabilities of the bank. The rule items can be document types, and the expected value ranges can be ID cards or Hong Kong, Macao, and Taiwan passports, etc.

[0036] Second attribute: It contains the actual values of at least some rule items, which can be the personal information of the payer, such as the document type is an ID card.

[0037] Figure 2 It is a flowchart of the entity determination method according to the embodiments of the present disclosure. As Figure 2 shown, the present disclosure provides an entity determination method M200. Through steps S210 to S230 and the coding range, multiple rule data of each entity can be efficiently and accurately determined in the first rule set, and then the first data is compared with the rule data of each entity respectively, and the target entity can be quickly and conveniently screened out. The basic data required for the operation of the entity determination method M200 can be stored in Figure 1 server 100 and run by the terminal device 200.

[0038] In step S210, according to the first coding range corresponding to the first attribute in the first data, the first rule set is determined.

[0039] The first data includes the attributes for screening the target entity, including the main body characteristics of the main body related to the first data. Among them, the first attribute in the first data is associated with the business dimension of each entity. When determining the first rule set with different business dimensions, different main body characteristics can be selected as the first attribute; the remaining main body characteristics other than the first attribute are used as the second attribute.

[0040] According to the first attribute, the business dimension concerned by the main body related to the first data can be determined. Each business dimension is configured with a first coding range, and the business dimension and the first coding range are recorded in the "Business Dimension - Coding Range" table in a one-to-one correspondence. By looking up the table, the involved first coding range can be determined when the first attribute is used as the business dimension.

[0041] The first coding range is usually a numerical range consisting of 1 to positive infinity, which is used to label each entity belonging to the corresponding business dimension. Each entity will be assigned a second coding range. When the second coding range of the entity falls within the first coding range, it indicates that the entity has this business dimension. Each first coding range is set in coding order and does not overlap. For example, if the first coding range of business dimension A is 1 to 10, then business dimension B can be a coding range that does not contain any number from 1 to 10, such as 11 to 15, etc., to avoid the contradiction problem that an entity cannot match a unique business dimension.

[0042] The first rule set includes business dimensions, each entity belonging to the business dimension, and the rule data of each entity. Of course, in the first rule set, each business dimension has a first coding range, each entity has a second coding range, and each rule data has a third coding range. In this way, when matching the first data with the rule data of each entity, multiple rule data belonging to the corresponding entity can be quickly located through the second coding range and the third coding range, which can save the time for locating the rule data of the entity in the dataset and improve the matching efficiency.

[0043] In step S220, according to the second coding range of the entity, in the first rule set, multiple rule data of the entity are determined.

[0044] The second coding ranges of different entities are different and do not overlap, and are arranged in coding order. For example, a certain business dimension has two entities, entity A and entity B, in the configuration order. The first coding range of the business dimension is 1 to 18. Then the second ranges of entity A and entity B should be between 1 and 18 and do not overlap. In the configuration order, entity A is preferentially configured with a second coding range, which can be 2 to 9, and the second coding range of entity B can be 10 to 17. It can be seen that both 2 to 9 and 10 to 17 fall within the first coding range, and the two second coding ranges do not overlap.

[0045] The rule data is the allocation rule of each entity, including rule items and the corresponding expected value range. Each entity can have multiple rule data at the same time. Each rule data is configured with a third coding range. The different third coding ranges do not overlap and all fall within the range of the corresponding second coding range. In this way, when determining whether the first data conforms to the usage rule of the entity, the second coding ranges that fall within it can be found using the second coding range, and then multiple rule data corresponding to the second coding range can be determined. It saves the time for locating the rule data of the entity and improves the efficiency and accuracy of determining the target entity.

[0046] Among them, the rule item is the item to be verified for the relevant subject of the entity. In the field of real estate transactions, the rule item can be "signing method", "document type", "supported fund item", etc. The expected value range is the standard of the rule item. If each second attribute of the relevant subject falls within the expected value range of the corresponding rule item, it means that the second data of the relevant subject meets the usage conditions of the entity, and the entity can be assigned to the relevant subject as the target entity. The quantity and types of rule data for each entity can be different.

[0047] In step S230, when the second attribute of the first data falls within the expected value range of the corresponding rule item, the entity is used as the target entity.

[0048] Generally, the first data and the rule data of each entity are compared in parallel. Observe whether each second attribute of the first data falls within the expected value range of the corresponding rule item of each entity. If there is an entity such that each second attribute of the first data falls within the expected value range of its corresponding rule item, then the entity is the target entity. In other words, the first data meets the requirements of the entity for the user, and the entity has various capabilities to support the use of the relevant subject. The target entity can be single or multiple, which is not limited here.

[0049] Compared with the high-cost and low-flexibility methods of hard-coding rules into the system or training a rule engine in the traditional way, the first rule set proposed in the present disclosure improves the efficiency and flexibility of rule iteration. And a matching method using the first rule set is given, that is, coding intervals are respectively configured for the business dimension, entity, and data rule. The coding intervals at different levels have an inclusive relationship with each other. Therefore, it is possible to efficiently determine the entities belonging to the same business dimension and multiple rule data belonging to the same entity through the coding intervals, making the first rule set feasible. Through the foregoing method, the time loss for locating rule data for the entity is also reduced, and the accuracy and efficiency of determining the target entity are improved.

[0050] Figure 3 is a schematic diagram of the process of determining the target entity according to the embodiment of the present disclosure. Refer to Figure 3 , through steps 301 to 310, the complete process of determining the target entity is shown.

[0051] In step 301, the entities belonging to the same business dimension and their rule data are sorted into the original set.

[0052] According to the entity data of the entity, all business dimensions involved in each entity are determined. Then, each business dimension, each entity belonging to the business dimension, and multiple rule data of each entity are respectively sorted into multiple original sets, and each original set corresponds to one business dimension.

[0053] In the original set, it includes various entities involved in the business dimension, as well as the rule data of each entity. However, each node does not have a coding range. By using the original set, the usage rules of each entity are effectively stripped out of the programs of the relevant systems, and it also avoids the investment in learning costs when using the rule engine. However, if the original set is used to compare with the first data, a large amount of time is required to determine the rule data of each entity, and there will also be cases of incorrect classification of rule data.

[0054] In the housing transaction scenario, obtain the ability rule data of each bank in different cities. For example, the signing methods supported by Bank A in City M are offline and online; the signing method supported by Bank B in City M is offline. The business dimension of the original set can be City M, the entities are Bank A and Bank B, the rule data of Bank A is: rule item "signing method", expected value range "online and offline"; the rule data of Bank B is: rule item "signing method", expected value range "offline". Of course, each business dimension can correspond to multiple entities, and each entity can correspond to multiple rule data, which is not restricted here.

[0055] To solve the problems of the large amount of time spent in determining the rule data of each entity and the incorrect classification of rule data when comparing the original set with the first data, step 302 is executed to configure the first coding range, the second coding range, and the third coding range for the business dimension, the entity, and the rule data respectively, to form the first rule set.

[0056] By configuring hierarchical parameters for each node, the efficiency of locating entities for the business dimension and locating rule data for entities is improved.

[0057] Different business dimensions have different first coding ranges, and these first coding ranges are set in sequence according to the configuration order of the business dimension. Moreover, there will be no overlap of numbers in each first coding range, ensuring the accuracy of entity classification. Similarly, according to the configuration order of the entities, the second coding ranges of each entity are set in sequence, and there is no overlap of numbers between the second coding ranges. Similarly, according to the configuration order of the data parameters, the third coding ranges of each entity are set in sequence, and there is no overlap of numbers between the third coding ranges.

[0058] The second coding range should fall within the range of the first coding range; the third coding range should fall within the range of the second coding range. Moreover, to ensure the convenience of subsequent addition and deletion of rule data, the interval span of each third coding range should be fixed, usually 2. The interval span is the number of characters occupied by the left value and the right value of the coding range and the numbers between them.

[0059] After writing the first coding range, the second coding range, and the third coding range into the original set, the first rule set is obtained.

[0060] Figure 4 It is a visualization graph of a first rule set according to an embodiment of the present disclosure.

[0061] Reference Figure 4 , which shows the case where the first rule set is in a tree structure. The first coding range of the business dimension is from 1 to 18, the second coding range of entity A included therein is from 2 to 9, and the second coding range of entity B is from 10 to 17. The third coding range of the rule data aa associated with entity A is from 3 to 4, the third coding range of the rule data ab is from 5 to 6, and the third coding range of the rule data ac is from 7 to 8; the third coding range of the rule data ba associated with entity B is from 11 to 12, the third coding range of the rule data bb is from 13 to 14, and the third coding range of the rule data bc is from 15 to 16.

[0062] The left value of the coding range of entity A is the second number of the first coding range; the left value of entity B is the number after the left value of entity A in the coding order, and the right value of entity B is the second-to-last number of the first coding range. The second coding range of entity A and the second coding range of entity B do not overlap, and are arranged in sequence according to the coding order, and both fall within the first coding range.

[0063] Similarly, the rule data aa, the rule data ab, and the rule data ac are all the rule data of entity A. Therefore, the third coding ranges of each rule data are all entered into the second coding range of entity A from 2 to 9, and each third coding range is arranged in sequence according to the coding order and does not overlap. The rule data ba, the rule data bb, and the rule data bc are all the rule data of entity B. Therefore, the third coding ranges of each rule data are all entered into the second coding range of entity B from 10 to 17, and each third coding range is arranged in sequence according to the coding order and does not overlap. The span of the third coding range is 2, that is, the number of characters of the left value, the right value of each rule data and the numbers involved therebetween.

[0064] Before comparing the first data with entity A, all rule data whose third coding range falls within 2 to 9 can be located through the second coding range of entity A, that is, the rule data aa with the third coding range from 3 to 4, the rule data ab with the third coding range from 5 to 6, and the rule data ac with the third coding range from 7 to 8.

[0065] Similarly, before comparing the first data with entity B, all rule data whose third coding range falls within 10 to 17 can be located through the second coding range of entity B, that is, the rule data ba with the third coding range from 11 to 12, the rule data bb with the third coding range from 13 to 14, and the rule data bc with the third coding range from 15 to 16.

[0066] The way to determine whether the third coding interval falls within the second coding interval is to check whether the left value of the third coding interval is greater than the left value of the second coding interval and whether the right value of the third coding interval is less than the right value of the second coding interval.

[0067] In some embodiments, after adding new rule data to the entity data, when the configuration order of the new rule data is not the first, determine the associated rule data that is before and adjacent to the new rule data; take the product of the number of new rule data and the interval span of the third coding interval as the first interval shift value, and each third coding interval has the same interval span; in each first rule set, add the first interval shift value to each coding interval whose value is greater than the third coding interval of the associated rule data, and each coding interval forms a new coding interval after adding the first interval shift value; and in coding order, take the coding interval that is after the third coding interval of the associated rule data and has an interval span as the third coding interval of the new rule data, and form a new first rule set that includes the new rule data.

[0068] Figure 5 is a schematic diagram of adding new rule data according to an embodiment of the present disclosure. Refer to Figure 5 , the first rule set is in a tree structure. Assume that the first coding interval of the business dimension in the original first rule set is from 1 to 18, the second coding interval of entity A it contains is from 2 to 9, and the second coding interval of entity B is from 10 to 17. The third coding interval of the rule data aa associated with entity A is from 3 to 4, the third coding interval of the rule data ab is from 5 to 6, and the third coding interval of the rule data ac is from 7 to 8; the third coding interval of the rule data ba associated with entity B is from 11 to 12, the third coding interval of the rule data bb is from 13 to 14, and the third coding interval of the rule data bc is from 15 to 16.

[0069] After adding the rule data ai, according to the configuration order, the rule data ab is after the rule data ai and the two are adjacent. Therefore, use the rule data ab as the associated rule data of the rule data ai.

[0070] The number of new rule data that can be after the rule data ab and adjacent to it is 1, and there is only one new rule data, the rule data ai. And the interval span of the third coding interval is 2, that is, the number of characters of the left value, right value, and the numbers involved between each rule data. Then, the first interval shift value is the product of the number of new rule data and the interval span, that is, 1×2, and the first interval shift value is 2.

[0071] In the first rule set, each coding interval with a value greater than the third coding interval of the associated rule data is respectively superimposed with the first interval shift value. The third coding interval of the associated rule data ab is from 5 to 6. Then, in the first rule set, each coding interval greater than 6 is respectively superimposed with the first interval shift value. For example, the right value of the business dimension changes from 18 to 20 after being superimposed with 2, and its first coding interval becomes from 1 to 20. Similarly, the right value of the second coding interval of entity A becomes 11, the second coding interval of entity B becomes from 12 to 19, the third coding interval of rule data ac becomes from 9 to 10, the third coding interval of rule data ba becomes from 13 to 14, the third coding interval of rule data bb becomes from 15 to 16, and the third coding interval of rule data bc becomes from 17 to 18.

[0072] Meanwhile, according to the coding order and the configuration order, the third coding interval from 7 to 8 is configured for rule data ai.

[0073] Of course, if entity C is added between entity B and entity A in the business dimension, then the first interval shift value is obtained by multiplying the interval span of the second coding interval of entity C by the number of new entities between entity B and entity A. Then, taking entity A as the associated entity of entity C, each coding interval in the first rule set with a value greater than the second coding interval of the associated entity is respectively superimposed with the first interval shift value.

[0074] In some embodiments, when the configuration order of the new rule data is the first, the third coding interval of the original first rule data of this entity is used as the third coding interval of the new rule data. Meanwhile, the product of the number of new rule data and the interval span of the third coding interval is used as the first interval shift value. The first interval shift value is superimposed on the coding intervals in the first rule set that are greater than or equal to the third coding interval of the new rule data to form a new first rule set.

[0075] In some embodiments, after generating multiple first rule sets, including: when deleting rule data from entity data, the product of the number of deleted rule data and the interval span of the third coding interval is used as the second interval shift value, and each third coding interval has the same interval span; and according to the configuration order of the deleted rule data in the entity data, in each first rule set, each coding interval with a value greater than the third coding interval of the deleted rule data is subtracted by the second interval shift value to form a new first rule set that does not include the deleted rule data, and each coding interval forms a new coding interval after being subtracted by the second interval shift value.

[0076] It should be noted that after adding or deleting rule data or entities, the range of adding the first interval movement value or subtracting the second interval movement value is all the coding intervals in the system, not limited to the first rule set where the rule data or entity addition and deletion occur. If there is a second rule set or other first rule sets in the system, they are all treated as the processing range for adding the first interval movement value or subtracting the second interval movement value.

[0077] In step 303, determine whether the first data-related entity has the usage right of the compensation entity.

[0078] Specifically, in each business dimension, only when the rule data of the first data and the entity match can the corresponding entity be assigned as the target entity to the associated entity of the first data. However, there are some associated entities with special permissions. For example, in the real estate transaction scenario, only when the relevant entity applies and the existing banks are unable to support its transaction can the usage right of a certain compensation transaction channel be granted to it. However, if the transaction volume of the relevant entity is large, special permissions may be configured for it to facilitate the transaction.

[0079] At this time, step 308 can be directly executed to determine whether the business dimension (city) associated with the first attribute (such as the city where it is located) of the relevant entity falls within the business dimension range of the compensation entity (such as the compensation transaction channel). Further, if its relevant business dimension falls within the business dimension range of the compensation entity, execute step 309 to determine whether all the second attributes of the first data of the relevant entity fall within the corresponding expected value range of the compensation entity. If all the second attributes of the first data of the relevant entity fall within the corresponding expected value range of the compensation entity, then execute step 310, and use the compensation entity as the target entity.

[0080] If the first data-related entity does not have the usage right of the compensation entity, then execute step 304 to determine the first rule set according to the first coding interval corresponding to the first attribute; then, execute step 305 to determine the rule data of each entity according to the second coding interval of each entity. The explanations for these two steps can refer to the descriptions of Figure 2 steps S210 and S220 in, which will not be elaborated here.

[0081] Further, execute step 306 to compare the rule data with the first data to determine whether each second attribute of the first data falls within the expected value range of the corresponding rule item.

[0082] For example, the second attribute of the first data is that the signing method is online, the document type is ID card, the number of payers is 2, and the funded item paid is commission. Comparing the signing method with the expected value range of the signing method of Bank A, which is "supporting both online and offline", it is found that "offline" in the second attribute falls within the range of "supporting both online and offline"; at the same time, the number of payers "2 people" falls within the range of the number of payers supported by Bank A, which is "less than or equal to 3 people"; the funded item paid "commission" falls within the range of the funded items supported by Bank A, which is "commission and house payment". And there are only these three rule data in Bank A: "signing method - supporting both online and offline", "number of payers - less than or equal to 3 people", "supported funded items - commission and house payment". Then Bank A is the target entity.

[0083] In the foregoing manner, various rule data of each entity are respectively compared with the second attribute of the first data. Then step 307 is executed to determine whether there is an entity such that all the second attributes fall within the corresponding expected value ranges.

[0084] If there is, then step 310 is executed to use the entity that makes all the second attributes fall within the corresponding expected value ranges as the target entity, and the number of target entities is not limited.

[0085] If not, then step 308 is executed to determine whether the business dimension falls within the business dimension range of the compensating entity. The compensating entity has the same function as the entity but the expected value range of at least some rule items is larger than the expected value range of the corresponding rule items of the entity. In other words, the rules of the compensating entity are easier to satisfy, are applicable to most of the first data, and are essentially fallback entities.

[0086] If the business dimension falls within the business dimension range of the compensating entity, then step 309 is executed to determine whether all the second attributes fall within the expected value ranges of the corresponding rule items of the compensating entity. If so, then step 310 is executed to use the compensating entity as the target entity.

[0087] The entity determination method of the present disclosure, compared with the high-cost and low-flexibility method of hard-coding rules into the system or training a rule engine in the traditional manner, the first rule set proposed by the present disclosure improves the efficiency and flexibility of rule iteration. Moreover, a matching method using the first rule set is given, that is, coding intervals are respectively configured for the business dimension, entity, and data rules, and the coding intervals at different levels have an inclusion relationship with each other. Therefore, entities belonging to the same business dimension and multiple rule data belonging to the same entity can be efficiently determined through the coding intervals, making the first rule set feasible. Through the foregoing manner, the time loss for locating rule data for the entity is also reduced, and the accuracy and efficiency of determining the target entity are improved.

[0088] Figure 6It is a schematic block diagram of an entity determination device according to an embodiment of the present disclosure.

[0089] Referring to Figure 6 , the present disclosure provides an entity determination device 600, including: a first rule set determination module 610, configured to determine a first rule set according to a first coding range corresponding to a first attribute in first data, the first data including multiple attributes for screening target entities, the first attribute being used to determine the business dimension of the entity, the first coding range being used to determine entities having the first attribute, and the first rule set recording rule data of entities having the first attribute; a rule data determination module 620, configured to determine multiple rule data of an entity in the first rule set according to a second coding range of the entity, the second coding range being within the first coding range, the second coding range being used to determine rule data belonging to the entity, and the rule data including rule items and expected value ranges; and a target entity determination module 630, configured to use the entity as a target entity when a second attribute of the first data falls within the expected value range of a corresponding rule item, the second attribute including actual values of at least some rule items.

[0090] The entity determination device 600 of the present disclosure may be in the form of computer software, and each module of the entity determination device 600 may be in the form of a computer software module.

[0091] Each module of the entity determination device 600 of the present disclosure is provided to implement each step of the entity determination method, and its execution principle and steps can be referred to the foregoing, and will not be elaborated herein.

[0092] Figure 7 It is a schematic block diagram of an electronic device according to an embodiment of the present disclosure. As Figure 7 shown, the present disclosure further provides an electronic device 1000, including: a memory 1200 and a memory 1300, the memory 1300 storing execution instructions; a processor 1200 executing the execution instructions stored in the memory 1300, so that the processor 1200 executes the entity determination method.

[0093] The hardware structure of the electronic device 1000 can be implemented using a bus architecture. The bus architecture can include any number of interconnected buses and bridges, depending on the specific application and overall design constraints of the hardware. The bus 1100 connects various circuits including one or more processors 1200, a memory 1300, and / or hardware modules together. The bus 1100 can also connect various other circuits 1400 such as peripheral devices, voltage regulators, power management circuits, external antennas, etc.

[0094] The bus 1100 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, only one connection line is used in this figure, but it does not mean that there is only one bus or one type of bus.

[0095] The present disclosure also provides a readable storage medium storing a computer program, which is used to implement the above method when executed by a processor. The "readable storage medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples of the readable storage medium include the following: an electrical connection part (electronic device) having one or more wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM), etc.

[0096] The present disclosure also provides a computer program product. The method of the present disclosure can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed, the processes or functions of the present disclosure are executed in whole or in part.

[0097] The computer program or instructions can be stored in a readable storage medium, or transmitted from one readable storage medium to another. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The readable storage medium can be any available medium that can be accessed, or a data storage device such as a server or data center integrating one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it can also be an optical medium, such as a digital video disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or can include both volatile and non-volatile types of storage media.

[0098] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as methods, electronic devices, readable storage media, or computer program products. Therefore, the present disclosure can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program code.

[0099] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the present disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0100] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0102] In the description of this specification, the descriptions referring to terms such as "one embodiment / way", "some embodiments / ways", "example", "specific example", or "some examples", etc., mean that the specific features, structures, or characteristics described in connection with that embodiment / way or example are included in at least one embodiment / way or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment / way or example. Moreover, the specific features, structures, or characteristics described can be combined in a suitable manner in any one or more embodiments / ways or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments / ways or examples described in this specification and the features of different embodiments / ways or examples.

[0103] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0104] Those skilled in the art should understand that the above embodiments are only for clearly explaining the present disclosure and are not intended to limit the scope of the present disclosure. For those skilled in the art, other changes or modifications can be made on the basis of the above disclosure, and these changes or modifications are still within the scope of the present disclosure.

Claims

1. A method for determining an entity, characterized in that: include: determining a first rule set according to a first coding interval corresponding to a first attribute in first data, wherein the first data includes a plurality of attributes for screening target entities, the first attribute is used to determine a business dimension of the entity, the first coding interval is used to determine an entity having the first attribute, and the first rule set records rule data of the entity having the first attribute; According to a second coding interval of the entity, in the first rule set, a plurality of rule data of the entity is determined, the second coding interval being in the first coding interval, the second coding interval being used to determine the rule data belonging to the entity, the rule data including a rule item and an expected value range; and In the case that the second attribute of the first data falls within the expected value range of the corresponding rule item, the entity is taken as the target entity, and the second attribute contains at least part of the actual value of the rule item.

2. The entity determination method according to claim 1, characterized in that: Before determining the first rule set, including: A plurality of first rule sets are generated according to entity data of each of the entities, wherein the entity data at least includes rule data of the entity and business dimensions of the entity, and the first rule set has a first coding interval, and each of the first coding intervals does not overlap and is arranged in a coding order.

3. The entity determination method according to claim 2, characterized in that: Generate a plurality of first rule sets according to the entity data of each of the entities, including: Organize entities belonging to the same business dimension and rule data of the entities into an original set; Configuring the first coding interval for the business dimension in the original set; configuring the second coding interval for each of the entities belonging to the business dimension, the second coding interval being within the first coding interval; and, According to the configuration order of each rule data of the entity, a third coding interval is configured for each rule data to form the first rule set having the first coding interval, the second coding interval and the third coding interval, and the third coding interval is in the second coding interval.

4. The entity determination method according to claim 2, characterized in that: After generating a plurality of first rule sets, including: After adding new rule data to the entity data, determining a configuration order of the new rule data; When the configuration order of the new rule data is not the first, determining the associated rule data that is before and adjacent to the new rule data according to the configuration order of the new rule data in the entity data; The product of the number of the new rule data and the interval span of the third coding interval is used as the first interval movement value, and each of the third coding intervals has the same interval span; In each of the first rule sets, the first interval shift value is added to each coding interval whose value is greater than the third coding interval of the association rule data, and each coding interval forms a new coding interval after the first interval shift value is added; and According to the coding order, the coding interval after the third coding interval of the association rule data and having the interval span is used as the third coding interval of the new rule data to form a new first rule set including the new rule data.

5. The entity determination method according to claim 4, characterized in that: After determining the configuration order of the new rule data, the method further includes: When the configuration order of the new rule data is first, the third encoding interval of the original first rule data of the entity is used as the third encoding interval of the new rule data; taking the product of the amount of the new rule data and the interval span as the first interval movement value; and A first interval shift value is added to the encoding intervals in the first rule set that are greater than or equal to the third encoding interval of the new rule data to form a new first rule set containing the new rule data.

6. The entity determination method according to claim 2, characterized in that: After generating a plurality of first rule sets, including: When deleting the rule data of the entity data, the product of the amount of the deleted rule data and the interval span of the third coding interval is used as the second interval shift value, and each of the third coding intervals has the same interval span; and According to the configuration order of the deleted rule data in the entity data, in each of the first rule sets, each coding interval whose value is greater than the third coding interval of the deleted rule data is subtracted from the second interval movement value to form a new first rule set that does not contain the deleted rule data, and each coding interval forms a new coding interval after subtracting the second interval movement value.

7. The entity determination method according to claim 1, characterized in that: Prior to targeting the entity, also include: Each second attribute of the first data is compared with each rule data of the entity to determine whether each second attribute falls within an expected value range of a corresponding rule item.

8. The entity determination method according to claim 7, characterized in that: After comparing each second attribute of the first data with each rule data of the entity to determine whether each second attribute falls within the expected value range of the corresponding rule item, the method further includes: When, for the rule data of each entity of the first rule set, there is at least one second attribute that does not fall within the expected value range of the corresponding rule item, determining the business dimension range of the compensation entity, and the expected value range of at least some rule items of the compensation entity is greater than the expected value range of the corresponding rule items of the entity; In a case where the business dimension falls within the business dimension range, determining the rule data of the compensation entity according to the fourth coding interval of the compensation entity; and Each second attribute of the first data is compared with each rule data of the compensation entity to determine whether each second attribute falls within the expected value range of the corresponding rule item.

9. The entity determination method according to claim 8, characterized in that: After comparing each second attribute of the first data with each rule data of the compensation entity to determine whether each second attribute falls within the expected value range of the corresponding rule item, the method further includes: In the case that each second attribute of the first data falls within the expected value range of the corresponding rule item, the compensation entity is used as the target entity.

10. An electronic device, characterized in that: include: A memory storing execution instructions; as well as A processor, wherein the processor executes the execution instructions stored in the memory, so that the processor executes the entity determination method according to any one of claims 1 to 9.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the entity determination method according to any one of claims 1 to 9 is implemented.

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