Data decision-making method and apparatus

Abstract

The present disclosure relates to the technical field of computers. Disclosed are a data decision-making method and apparatus, which mainly aim to solve the problem of existing data decision-making methods failing to provide a processing method when the data type of service data is inconsistent with a data type in a decision-making tree. The main technical solution of the present disclosure comprises: acquiring service data; determining whether a data type corresponding to the service data is consistent with a data type in a decision tree; if the data type corresponding to the service data is not consistent with the data type in the decision tree, calling a transformation function to transform the service data into specified service data, wherein a data type corresponding to the specified service data is consistent with the data type in the decision tree; determining, in the decision tree, whether the specified service data meets a task hit condition; and if the specified service data meets the task hit condition, generating a decision-making task corresponding to the specified service data. The present disclosure is used for data decision-making.

Description

Data decision method and device

[0001] This disclosure claims priority to Chinese patent application No. 202311244286.3 filed on September 25, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0002] The present disclosure relates to the field of computer technology, and in particular to a data decision method and device. Background Art

[0003] With the advent of the big data era, enterprises will use data decision-making methods to make task decisions on business data. That is, they will determine whether there are task hit conditions corresponding to the business data in the decision tree. If so, they will generate decision tasks corresponding to the business data, such as generating an intelligent outbound call task corresponding to a certain number.

[0004] At present, the premise for judging whether there are task hit conditions corresponding to business data in the decision tree is that the data type of the business data is consistent with the data type in the decision tree. The current method has no way to deal with the inconsistency between the data type of the business data and the data type in the decision tree, resulting in the inability to judge whether there are task hit conditions corresponding to the business data in the decision tree, and it is difficult to generate the corresponding decision tasks, causing certain losses to the enterprise.

[0005] Summary of the Invention

[0006] In view of the above problems, the present disclosure provides a data decision method and device, the main purpose of which is to solve the problem that the current data decision method has no solution when the data type of business data is inconsistent with the data type in the decision tree.

[0007] To solve the above technical problems, the present disclosure proposes the following solutions:

[0008] In a first aspect, the present disclosure provides a data decision method, the method comprising:

[0009] Obtain business data;

[0010] Determine whether the data type corresponding to the business data is consistent with the data type in the decision tree;

[0011] If they are inconsistent, a conversion function is called to convert the business data into specified business data, where the data type corresponding to the specified business data is consistent with the data type in the decision tree;

[0012] Determining whether the specified business data meets the task hit condition in the decision tree;

[0013] If the conditions are met, a decision task corresponding to the specified business data is generated.

[0014] In a second aspect, the present disclosure provides a data decision device, the device comprising:

[0015] A data acquisition unit, used to acquire business data;

[0016] A data type determination unit, configured to determine whether the data type corresponding to the business data acquired by the data acquisition unit is consistent with the data type in the decision tree;

[0017] a data conversion unit, configured to call a conversion function to convert the business data into specified business data if the data type determination unit determines that the business data is inconsistent, wherein the data type corresponding to the specified business data is consistent with the data type in the decision tree;

[0018] A data condition judgment unit, configured to judge in the decision tree whether the specified business data obtained by the data conversion unit meets the task hit condition;

[0019] The task generating unit is configured to generate a decision-making task corresponding to the specified business data if the data condition judging unit determines that the data condition is satisfied.

[0020] In order to achieve the above-mentioned purpose, according to the third aspect of the present disclosure, a storage medium is provided, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the data decision method of the above-mentioned first aspect.

[0021] In order to achieve the above-mentioned objective, according to a fourth aspect of the present disclosure, a processor is provided, wherein the processor is used to run a program, wherein the data decision method of the above-mentioned first aspect is executed when the program is run.

[0022] By means of the above technical solution, the present disclosure provides a data decision method and device, which can first obtain business data, and then detect the data type of the business data and the data type in the decision tree to determine whether the data type of the business data is consistent with the data type in the decision tree. If they are inconsistent, the conversion function can be called in time to convert the data type of the business data into the data type in the decision tree, and the specified business data that complies with the processing rules can be obtained. The data type of the specified business data obtained in this way is consistent with the data type in the decision tree, and a subsequent judgment can be made as to whether the task hit condition is met. If it is determined that the specified business data meets the task hit condition in the decision tree, a decision task corresponding to the specified business data can be generated. In this way, even if the data types are inconsistent, data decisions on the business data can be continued through type conversion, and the decision task of the business data will not be missed due to inconsistent data types, thereby avoiding certain losses to the enterprise to a certain extent.

[0023] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the specific implementation methods of the present disclosure are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present disclosure. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0025] FIG1 shows a flow chart of a data decision method provided by an embodiment of the present disclosure;

[0026] FIG2 shows a flow chart of another data decision method provided by an embodiment of the present disclosure;

[0027] FIG3 shows a block diagram of a data decision-making device provided by an embodiment of the present disclosure;

[0028] FIG4 shows a block diagram of another data decision-making device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0030] Current data decision methods cannot process data when the data type of the business data is inconsistent with the data type in the decision tree. The inventors have found that when the data type corresponding to the business data is inconsistent with the data type in the decision tree, the data type corresponding to the business data can be converted to obtain specified business data that is consistent with the data type in the decision tree. The decision tree can then determine whether the specified business data of the consistent type meets the task hit condition, solving the problem of being unable to process tasks when the types are inconsistent. The specific execution steps are shown in Figure 1 and include:

[0031] 101. Obtain business data.

[0032] In this disclosure, the execution subject is a data decision system. The data decision system uses a decision flow structure. When there is business data to be processed, the business data can be input into the data decision system, and the data decision system can obtain the business data.

[0033] Specifically, since there are multiple business categories, different business data may come from different business categories. Therefore, it is possible to establish a connection between the business data source corresponding to each business category and the data decision system, so that the data decision system can pull each business data in real time. In addition, business data can also be manually input into the data decision system.

[0034] 102. Determine whether the data type corresponding to the business data is consistent with the data type in the decision tree.

[0035] In this step, after obtaining the business data, the data type of the business data and the data type in the decision tree can be further detected, and then compared to see if the two are consistent.

[0036] Specifically, the business data includes data fields and corresponding data values, and the decision tree also includes multiple data fields and the data values ​​corresponding to each data field. Therefore, when comparing the data type of the business data with the data type in the decision tree, it is possible to first determine whether there is a data field in the decision tree that is identical to the data field in the business data. If so, the data value type corresponding to the data field in the decision tree is compared with the data value type corresponding to the data field in the business data. If the comparison does not match, step 103 can be executed; if they do match, no type conversion is required, and the decision tree can be directly used to determine whether the business data meets the task hit condition.

[0037] Furthermore, when comparing the data value type corresponding to the data field in the business data with the data value type in the decision tree, a preset comparison function may be called, through which the result can be directly output; or a manual comparison may be performed.

[0038] 103. Call the conversion function to convert the business data into specified business data.

[0039] In this step, when the data value type corresponding to the data field in the business data is inconsistent with the data value type corresponding to the same data field in the decision tree, a pre-set conversion function can be called to convert the data value type corresponding to the data field in the business data into the data value type corresponding to the same data field in the decision tree, thereby obtaining the specified business data.

[0040] For example, if the data field in the business data is a date and the specific data value is 20231011, and the data field in the decision tree is also a date, but the specific data value is 2023-10-11, then 20231011 needs to be converted to 2023-10-11.

[0041] 104. Determine whether the specified business data meets the task hit condition in the decision tree.

[0042] 105. Generate a decision task corresponding to the specified business data.

[0043] After converting the business data to obtain the specified business data, the data fields and corresponding data values ​​included in the specified business data of the same type can be compared with the data values ​​corresponding to the same data fields in the decision tree to determine whether the two data values ​​are consistent. If they are consistent, it can be directly determined that the specified business data in the decision tree meets the task hit condition. If it is met, the decision task corresponding to the specified business data is directly generated in step 105, and the hit result of the generated decision task is returned at the same time; it can also be further detected whether the node where the same data field in the decision tree is located is a leaf node when it is determined that the two data values ​​are consistent. The leaf node is the final node in the entire decision tree, that is, it is determined whether the entire decision tree has been traversed. The final result can be generated after the entire decision tree has been traversed to avoid omissions.

[0044] Based on the implementation method of Figure 1 above, it can be seen that the data decision method provided by the present disclosure can first obtain business data, and then detect the data type of the business data and the data type in the decision tree to determine whether the data type of the business data is consistent with the data type in the decision tree. When it is inconsistent, the conversion function can be called in time to convert the data type of the business data into the data type in the decision tree to obtain the specified business data that meets the processing rules. The data type of the specified business data obtained in this way is consistent with the data type in the decision tree, and a subsequent judgment can be made as to whether the task hit condition is met. If it is judged that the specified business data meets the task hit condition in the decision tree, a decision task corresponding to the specified business data can be generated. In this way, even if the data types are inconsistent, data decisions on the business data can be continued through type conversion, and the decision task of the business data will not be missed due to inconsistent data types, thereby avoiding certain losses to the enterprise to a certain extent.

[0045] Furthermore, as a refinement and extension of the embodiment shown in FIG1 , the embodiment of the present disclosure also provides another data decision method, as shown in FIG2 , and the specific steps are as follows:

[0046] 201. Obtain business data.

[0047] Among them, the implementation method of step 201 is the same as that of step 101, and can achieve the same technical effect and solve the same technical problem, so it will not be repeated here.

[0048] 202. Determine a designated decision tree based on the business category of the business data, and judge whether the data type corresponding to the business data is consistent with the data type in the designated decision tree.

[0049] In the present disclosure, the data decision system uses a decision flow structure, wherein the decision flow structure can point to a decision tree or a rule set. If the secondary structure is a decision tree, no nesting will occur. However, if the secondary structure is a rule set, the rules and / or decision trees can continue to be connected, with multiple layers of nesting.

[0050] In this step, the secondary structure is used as the rule set, and the rule set is connected to the decision tree as an example.

[0051] In this disclosure, there are multiple business categories, and a decision tree includes data corresponding to multiple business categories. If a decision tree were to expand infinitely as the amount of data increases, it would affect the initialization time of subsequent page loads, affect the efficiency of business operations, and the database would not be able to support it. Therefore, the decision tree can be split according to the business category to obtain a small decision tree corresponding to each business category. For example, among mobile operators, operator A corresponds to one decision tree, operator B corresponds to one decision tree, and operator C corresponds to one decision tree.

[0052] After splitting to obtain the small decision trees corresponding to each business category, when using it, you can first use the preset rule expression to filter the business categories that the business data meets, and then determine the designated decision tree pointed to by the business category.

[0053] Among them, the preset rule expression supports the "and" logical operator and the "or" logical operator. This can support more business scenarios. In addition, each business data corresponds to an attribute and an attribute value, and each business data includes at least one data field and the data value corresponding to the data field. You can first use the preset rule expression to filter the business category that the attribute value corresponding to the business data conforms to. For example, if the attribute corresponding to the business data is a mobile phone number, and the attribute value is the specific value of the mobile phone number, you can filter in the rule set through the preset rule expression (the preset rule expression in this case can be an "or" operation rule expression). The mobile phone number is operator A, operator B, or operator C. In this way, if one of the three is met, the result can be returned as true. If the mobile phone number is obtained as operator A, the specified decision tree corresponding to operator A can be searched. If there is only the "and" logical operator, the correct operator corresponding to the mobile phone number may be missed (for example, if the mobile phone number is operator B, using the "and" operator to determine whether the mobile phone number is operator A will return a negative result).

[0054] Specifically, after selecting the designated decision tree, the business data fields included in the business data can be first determined, and then the designated decision tree can be traversed to determine whether there is a target data field in the designated decision tree that is identical to the business data field. If not, processing of the business data can be stopped. If so, the business data value corresponding to the business data field and the target data value corresponding to the target data field can be further determined, and then whether the types of the business data value and the target data value are consistent can be determined. If not, step 203 is executed; if consistent, it can be directly determined whether the business data in the designated decision tree meets the task hit condition.

[0055] For example, the attributes corresponding to the business data are: mobile phone number, attribute value: specific numerical value; the business data fields included in the business data are: call date, business data value: 20231011. After filtering the mobile phone number with a preset rule expression, it can be obtained that the mobile phone number belongs to a certain operator category, and then the specified decision tree pointed to by the operator can be filtered out, and then the field with the call date as the data field can be filtered out in the specified decision tree, and then the target data value corresponding to the call date field can be further determined, which can be 2023-10-11, and then 2023-10-11 is compared with 20231011.

[0056] That is to say, in the present disclosure, based on the attribute values ​​corresponding to the business data, the preset rule expressions are used to filter out the data to be compared in the rule set, that is, the specified decision tree corresponding to the business category that the attribute value meets is filtered out, and then it is determined whether there are conditions that can be compared with this specified decision tree, that is, whether there are the same data fields between the two. If there are the same data fields, the data values ​​corresponding to the same data fields of the two are compared.

[0057] 203. Call the conversion function to convert the business data value in the business data into the specified business data value to obtain the specified business data.

[0058] In this step, if the determined business data value type is inconsistent with the target data value type, at least one format to be converted can be determined based on the data type corresponding to the target data value, and then the business data value can be converted according to at least one format to be converted to obtain the converted specified business data value.

[0059] For example, if the target data value is 2023-10-11 and the type is date, the formats to be converted corresponding to the date can be yyyy-MM-dd, yyyy / MM / dd, yyyy year MM month dd day, etc., to reflect that the data is a date. At this time, the business data value is 20231011, and 20231011 can be converted according to the formats of yyyy-MM-dd, yyyy / MM / dd, yyyy year MM month dd day, etc. If the conversion is unsuccessful within a certain period of time, an error message can be sent to the display terminal. If the conversion is successful, the specified business data value is obtained, and the specified business data is obtained, and the specified business data includes the specified business data value.

[0060] 204. Determine whether the specified business data meets the task hit condition in the specified decision tree.

[0061] 205. Generate a decision task corresponding to the specified business data.

[0062] In steps 204-205, after converting the business data value into the specified business data value, it can be further determined whether the specified business data value and the target data value are the same. If they are the same, it can be further determined whether the target data field corresponding to the target data value is a leaf node in the specified decision tree. If so, it can be determined that the specified business data in the specified decision tree meets the task hit condition, that is, the decision task corresponding to the specified business data can be generated; if the specified business data value and the target data value are not the same, it is determined that the specified business data does not meet the task hit condition in the specified decision tree.

[0063] Next, the complete technical solution of the present disclosure is described with reference to examples:

[0064] If the attribute corresponding to the business data is: mobile phone number, attribute value: specific mobile phone number value, the data field included in the business data is: call date, data value: 20231011.

[0065] You can first filter whether the mobile phone number meets the following operators in the rule set based on the preset rule expression (preferably the "or" logical operator, followed by the "and" logical operator) according to "attribute is: mobile phone number, attribute value: specific mobile phone number value" (if there are only three operators ABC in the database, you can determine whether the mobile phone number meets operator A, operator B, or operator C). For example, if it meets operator A, determine the designated decision tree corresponding to operator A. After finding the designated decision tree, you can further determine whether the "data field: call date" exists in the designated decision tree.

[0066] If it exists, the data value corresponding to the "Data Field: Call Date" in the specified decision tree can be further determined, for example: "2023-10-11", and then the "2023-10-11" in the specified decision tree is compared with the "20231011" in the business data to determine whether the data types of the two are consistent. If they are inconsistent, the data type of "20231011" in the business data is converted, but the conversion only needs to reflect its date format, and it does not need to be exactly the "2023-10-11" style in the specified decision tree, it can be "2023 / 10 / 11".

[0067] After obtaining the converted "2023 / 10 / 11", it can be compared with the value "2023-10-11" in the specified decision tree. If the two are consistent, the node in the specified decision tree where the data field contains the date is a leaf node. If so, the specified business data is determined to meet the task hit condition, and a decision task corresponding to the specified business data can be generated. In other words, a smart outbound call task corresponding to the specified business data (data field: call date, data value: "2023 / 10 / 11") is generated.

[0068] In addition, it should be noted that the data decision system disclosed in the present invention is a visual system, and users can configure and call decision trees and rule sets on the front end. The configuration includes the configuration of the data and data fields supported therein.

[0069] Furthermore, as an implementation of the method shown in FIG1 above, the embodiment of the present disclosure also provides a data decision device for implementing the method shown in FIG1 above. This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not further describe the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can implement all the contents of the aforementioned method embodiment. As shown in FIG3, the device includes:

[0070] A data acquisition unit 301 is used to acquire business data;

[0071] A data type determination unit 302 is configured to determine whether the data type corresponding to the business data acquired by the data acquisition unit 301 is consistent with the data type in the decision tree;

[0072] The data conversion unit 303 is configured to call a conversion function to convert the business data into specified business data if the data type determination unit 302 determines that the business data is inconsistent, and the data type corresponding to the specified business data is consistent with the data type in the decision tree;

[0073] A data condition judgment unit 304 is used to judge in the decision tree whether the specified business data obtained by the data conversion unit 303 meets the task hit condition;

[0074] The task generating unit 305 is configured to generate a decision-making task corresponding to the specified business data if the data condition judging unit 304 judges that the data condition is satisfied.

[0075] Furthermore, as an implementation of the method shown in FIG. 2 , the present disclosure also provides another data decision device for implementing the method shown in FIG. 2 . This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not further detail the aforementioned method embodiment. However, it should be clear that the device in this embodiment can implement all of the aforementioned method embodiments. As shown in FIG. 4 , the device includes:

[0076] A data acquisition unit 301 is used to acquire business data;

[0077] A data type determination unit 302 is configured to determine whether the data type corresponding to the business data acquired by the data acquisition unit 301 is consistent with the data type in the decision tree;

[0078] The data conversion unit 303 is configured to call a conversion function to convert the business data into specified business data if the data type determination unit 302 determines that the business data is inconsistent, and the data type corresponding to the specified business data is consistent with the data type in the decision tree;

[0079] A data condition judgment unit 304 is used to judge in the decision tree whether the specified business data obtained by the data conversion unit 303 meets the task hit condition;

[0080] The task generating unit 305 is configured to generate a decision-making task corresponding to the specified business data if the data condition judging unit 304 judges that the data condition is satisfied.

[0081] In an optional embodiment, before the data type determination unit 302 determines whether the data type corresponding to the business data is consistent with the data type in the decision tree, the device further includes a designated decision tree determination unit 306, and the designated decision tree determination unit 306 includes:

[0082] A category determination module 3061 is configured to determine a business category to which an attribute value corresponding to the business data corresponds based on a preset rule expression, wherein the business data corresponds to an attribute and an attribute value, and the preset rule expression supports an AND operator and an OR operator;

[0083] a designated decision tree determination module 3062, configured to determine, from a plurality of decision trees, a designated decision tree corresponding to the service category determined by the category determination module 3061;

[0084] The first data type determination module 3063 is configured to determine whether the data type corresponding to the business data is consistent with the data type in the designated decision tree determined by the designated decision tree determination module 3062 .

[0085] In an optional implementation, before the first data type determination module 3063 determines whether the data type corresponding to the business data is consistent with the data type in the specified decision tree, the device further includes a field determination unit 307, and the field determination unit 307 includes:

[0086] A first field determination module 3071 is configured to determine a service data field included in the service data;

[0087] A second field determination module 3072 is configured to traverse the designated decision tree and determine whether there is a target data field in the designated decision tree that is the same as the business data field determined by the first field determination module 3071;

[0088] A second data type determination module 3073 is configured to determine whether the data type corresponding to the business data is consistent with the data type in the specified decision tree if the second field determination module 3072 determines that the data type exists;

[0089] The task stop processing module 3074 is used to stop processing the business data if the second field determination module 3072 determines that the second field does not exist.

[0090] In an optional implementation, the second data type determination module 3073 is specifically configured to:

[0091] Determining a business data value corresponding to the business data field;

[0092] Determining a target data value corresponding to the target data field;

[0093] Determine whether the data type corresponding to the business data value is consistent with the data type corresponding to the target data value.

[0094] In an optional embodiment, the data conversion unit 303 includes

[0095] A format determination module 3031 is configured to determine at least one format to be converted based on a data type corresponding to the target data value;

[0096] The data conversion module 3032 is used to convert the business data value according to at least one format to be converted determined by the format determination module 3031 to obtain a converted specified business data value, where the specified business data includes a business data field and a corresponding specified business data value.

[0097] In an optional implementation, the data condition judgment unit 304 includes:

[0098] A data value determination module 3041 is configured to determine whether the specified business data value is consistent with a target data value corresponding to a target data field in a specified decision tree;

[0099] A first condition determination module 3042 is configured to determine that the specified business data in the specified decision tree does not meet the task hit condition if the data value determination module 3041 determines that the data is inconsistent;

[0100] A second condition determination module 3043 is configured to determine whether the node where the target data field is located is a leaf node if the data value determination module 3041 determines that the nodes are consistent;

[0101] The third condition determination module 3044 is configured to determine that the specified business data in the specified decision tree meets the task hit condition if the second condition determination module 3043 determines that the node is a leaf node.

[0102] In an optional embodiment, before the category determination module 3061 determines the business category to which the attribute value corresponding to the business data conforms based on a preset rule expression, the device further includes a decision tree splitting unit 308, wherein the decision tree splitting unit 308 includes:

[0103] A decision tree determination module 3081 is used to determine multiple business categories corresponding to the decision tree;

[0104] The decision tree splitting module 3082 is used to split the decision tree based on the multiple business categories determined by the decision tree determination module 3081 to obtain multiple decision trees corresponding to the multiple business categories.

[0105] Furthermore, an embodiment of the present disclosure also provides a storage medium, which is used to store a computer program, wherein when the computer program is running, it controls the device where the storage medium is located to execute the data decision method described in Figures 1-2 above.

[0106] Furthermore, an embodiment of the present disclosure also provides a processor, which is used to run a program, wherein the data decision method described in Figures 1-2 is executed when the program is run.

[0107] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] It is understood that the relevant features of the above methods and devices can be referenced to each other. In addition, the terms "first" and "second" in the above embodiments are used to distinguish between the embodiments, and do not represent the advantages and disadvantages of the embodiments.

[0109] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0110] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems may also be used together with the teachings herein. Based on the above description, it is apparent that the structure required for constructing such systems is suitable. In addition, the present disclosure is not directed to any specific programming language. It should be understood that various programming languages ​​may be utilized to implement the present disclosure described herein, and the description of specific languages ​​above is provided for the purpose of disclosing the best mode of implementation of the present disclosure.

[0111] In addition, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0112] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0114] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0116] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0117] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0118] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0119] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0120] Those skilled in the art will appreciate that embodiments of the present disclosure may be provided as methods, systems, or computer program products. Thus, the present disclosure may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] The above are merely examples of the present disclosure and are not intended to limit the present disclosure. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure are intended to be included within the scope of the claims of the present disclosure.

Claims

1. A data decision method, characterized in that: The method comprises: Get business data; Determine whether the data type corresponding to the business data is consistent with the data type in the decision tree; If they are inconsistent, calling a conversion function to convert the business data into specified business data, the data type corresponding to the specified business data is consistent with the data type in the decision tree; Determining in the decision tree whether the specified business data meets the task hit condition; If satisfied, a decision task corresponding to the specified business data is generated.

2. The method according to claim 1, characterized in that: Before determining whether the data type corresponding to the business data is consistent with the data type in the decision tree, the method further includes: Determine the business category to which the attribute value corresponding to the business data conforms based on a preset rule expression, wherein the business data corresponds to an attribute and an attribute value, and the preset rule expression supports an AND operator and an OR operator; Determining a designated decision tree corresponding to the business category from among a plurality of decision trees; Determine whether the data type corresponding to the business data is consistent with the data type in the specified decision tree.

3. The method according to claim 2, characterized in that Before determining whether the data type corresponding to the business data is consistent with the data type in the specified decision tree, the method further includes: Determining the business data fields included in the business data; Traversing the designated decision tree to determine whether there is a target data field identical to the business data field in the designated decision tree; If so, determining whether the data type corresponding to the business data is consistent with the data type in the specified decision tree; If it does not exist, stop processing the business data.

4. The method according to claim 3, characterized in that: Determining whether the data type corresponding to the business data is consistent with the data type in the specified decision tree includes: Determine the business data value corresponding to the business data field; Determine a target data value corresponding to the target data field; Determine whether the data type corresponding to the business data value is consistent with the data type corresponding to the target data value.

5. The method according to claim 4, characterized in that Calling a conversion function to convert the business data into specified business data includes: Determine at least one format to be converted based on the data type corresponding to the target data value; The service data value is converted according to the at least one format to be converted to obtain a converted designated service data value, wherein the designated service data includes a service data field and a corresponding designated service data value.

6. The method according to claim 5, characterized in that Judging whether the specified business data meets the task hit condition in the decision tree includes: Determine whether the specified business data value is consistent with the target data value corresponding to the target data field in the specified decision tree; If not, determining that the specified business data in the specified decision tree does not meet the task hit condition; If they are consistent, determining whether the node where the target data field is located is a leaf node; If it is a leaf node, it is determined that the specified business data in the specified decision tree meets the task hit condition.

7. The method according to claim 2, characterized in that: Before determining, based on a preset rule expression, the business category to which the attribute value corresponding to the business data corresponds, the method further includes: Determine multiple business categories corresponding to the decision tree; The decision tree is split based on the multiple business categories to obtain multiple decision trees corresponding to the multiple business categories respectively.

8. A data decision device, characterized in that: The device comprises: A data acquisition unit, used for acquiring business data; A data type determination unit, used to determine whether the data type corresponding to the business data acquired by the data acquisition unit is consistent with the data type in the decision tree; A data conversion unit is used to call a conversion if the data type judgment unit judges that the data type is inconsistent. The conversion function converts the business data into specified business data, and the data type corresponding to the specified business data is consistent with the data type in the decision tree; A data condition judgment unit, used to judge in the decision tree whether the specified business data obtained by the data conversion unit meets the task hit condition; The task generating unit is used to generate a decision task corresponding to the specified business data if the data condition judging unit judges that the data condition is satisfied.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the data decision method as described in any one of claims 1 to claim 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program executes the data decision method according to any one of claims 1 to claim 7 when running.

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