CN116934398A - Decision generation method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a decision generation method, a device, equipment and a storage medium, which relate to the technical field of computers and comprise the following steps: splicing and configuring the obtained preset decision nodes through a preset canvas component to obtain a decision engine rule tree; acquiring user behavior event data through a preset data channel, and transmitting the user behavior event data to the decision engine rule tree; when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, judging whether the user behavior event data meets corresponding preset node judgment conditions in the decision engine rule tree or not so as to obtain a corresponding judgment result; and generating corresponding decision results based on all the judgment results corresponding to the user behavior event data. In this way, the personalized marketing strategy is generated by the decision result corresponding to the user behavior event data obtained through the generated decision engine rule tree.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for generating a decision.
Background
With the rapid development of the internet and the mobile internet, the number of users and the amount of user behavior data are increasing. To better understand and analyze user behavior, enterprises need to mine and analyze these data in order to formulate more efficient marketing strategies. The traditional manual report and data analysis method often requires a great deal of manpower and time investment, and accurate results are difficult to acquire in time. Therefore, how to quickly and efficiently formulate a more effective marketing strategy is needed to be addressed.
Disclosure of Invention
In view of the above, an object of the present application is to provide a decision generation method, apparatus, device and storage medium, which can automatically generate personalized marketing strategies according to behavior events and personal information of users. The specific scheme is as follows:
in a first aspect, the application discloses a decision generation method, comprising:
splicing and configuring the obtained preset decision nodes through a preset canvas component to obtain a decision engine rule tree;
acquiring user behavior event data through a preset data channel, and transmitting the user behavior event data to the decision engine rule tree;
when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, judging whether the user behavior event data meets corresponding preset node judgment conditions in the decision engine rule tree or not so as to obtain a corresponding judgment result;
and generating corresponding decision results based on all the judgment results corresponding to the user behavior event data.
Optionally, the acquiring the user behavior event data through a preset data channel and transmitting the user behavior event data to the decision engine rule tree includes:
user behavior event data of a preset client data center are transmitted to a preset flink stream engine through a preset kafka message middleware;
and carrying out data filtering, matching and checking on the user behavior event data by using the preset link flow engine, and transmitting the checked user behavior event data into the decision engine rule tree.
Optionally, when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, the determining whether the user behavior event data meets the corresponding preset node determining condition in the decision engine rule tree includes:
when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, a query function of a preset JAVA system is called by the preset flink flow engine to perform corresponding data query on the user behavior event data so as to obtain a behavior event data query result corresponding to the any preset decision node;
judging whether the behavior event data query result meets a preset node judgment condition corresponding to any preset decision node in the decision engine rule tree.
Optionally, the generating a corresponding decision result based on all the judgment results corresponding to the user behavior event data includes:
and calling the preset JAVA system by the preset flink streaming engine to execute the touch actions respectively corresponding to the judgment results in the decision engine rule tree so as to generate corresponding decision results based on the touch actions.
Optionally, the acquiring the user behavior event data through a preset data channel and transmitting the user behavior event data to the decision engine rule tree includes:
and acquiring current user behavior event data through a preset data channel, and transmitting the current user behavior event data into the decision engine rule tree.
Optionally, the determining whether the user behavior event data meets a corresponding preset node determining condition in the rule tree of the decision engine includes:
taking the first preset decision node in the decision engine rule tree as a current node, and judging whether current user behavior event data meets preset node judgment conditions of the current node or not;
taking the next preset decision node corresponding to the judgment result in the decision engine rule tree as a new current node, acquiring new current user behavior event data through the preset data channel, and jumping to the step of judging whether the current user behavior event data meets the preset node judgment condition of the current node or not until the current node is the last preset decision node in the decision engine rule tree.
Optionally, the method further comprises:
and storing the decision result into a preset flow water meter according to a preset time period so as to inquire the decision result through the preset flow water meter.
In a second aspect, the present application discloses a decision making device comprising:
the decision rule tree generation module is used for splicing and configuring the obtained preset decision nodes through the preset canvas component to obtain a decision engine rule tree;
the behavior data acquisition module is used for acquiring user behavior event data through a preset data channel and transmitting the user behavior event data to the decision engine rule tree;
the node judging module is used for judging whether the user behavior event data meets the corresponding preset node judging conditions in the decision engine rule tree or not when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree so as to obtain a corresponding judging result;
and the marketing result acquisition module is used for generating corresponding decision results based on all the judgment results corresponding to the user behavior event data.
In a third aspect, the present application discloses an electronic device, comprising:
a memory for storing a computer program;
and a processor for executing the computer program to implement the aforementioned decision making method.
In a fourth aspect, the present application discloses a computer readable storage medium storing a computer program which, when executed by a processor, implements the aforementioned decision generation method.
In the application, the acquired preset decision nodes are spliced and configured through the preset canvas component to obtain a decision engine rule tree; acquiring user behavior event data through a preset data channel, and transmitting the user behavior event data to the decision engine rule tree; when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, judging whether the user behavior event data meets corresponding preset node judgment conditions in the decision engine rule tree or not so as to obtain a corresponding judgment result; and generating corresponding decision results based on all the judgment results corresponding to the user behavior event data. Therefore, the decision engine rule tree obtained through unified configuration can enable marketing rule scenes with large differences to be realized by using one set of marketing decision engine. Personalized marketing strategies may be automatically generated based on the user's behavioral events and personal information. Each business requirement does not need to independently create a marketing rule system, so that resource investment and manpower investment for software development are reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a decision making method of the present disclosure;
FIG. 2 is a flow chart of a specific decision making method of the present disclosure;
FIG. 3 is a flow chart of a specific decision making method of the present disclosure;
FIG. 4 is a flowchart of a specific decision making method disclosed in the present application;
FIG. 5 is a flowchart of a specific decision making method disclosed in the present application;
FIG. 6 is a schematic diagram of a decision making device according to the present disclosure;
fig. 7 is a block diagram of an electronic device according to the present disclosure.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
To better understand and analyze user behavior, enterprises need to mine and analyze these data in order to formulate more efficient marketing strategies. In this embodiment, a process of decision engine generation for generating a marketing strategy will be specifically described.
Referring to fig. 1, the embodiment of the application discloses a decision generation method, which comprises the following steps:
step S11: and splicing and configuring the obtained preset decision nodes through a preset canvas component to obtain a decision engine rule tree.
In this embodiment, the obtained preset decision nodes are configured in a splicing manner through a preset canvas component to obtain a decision engine rule tree, that is, the obtained preset decision nodes are configured in a splicing manner through a UI (User Interface) canvas component dragging manner to obtain the decision engine rule tree. The UI canvas component is a decision rule editing tool, and constructs decision rules in a dragging mode through the page component to realize rule data editing. The decision engine rule tree is provided with a plurality of preset decision nodes for combining and realizing the whole rule logic, and each preset decision node basically comprises the following steps:
start-judge-route-touch-end.
Starting: user behavior data is received.
Judging: event matching and attribute judging.
And (3) routing: and judging an upstream result, and delaying for waiting.
Touching: delay touch and result callback.
Ending: the flow ends.
Step S12: and acquiring user behavior event data through a preset data channel, and transmitting the user behavior event data into the decision engine rule tree.
In this embodiment, the obtaining the user behavior event data through the preset data channel and transmitting the user behavior event data to the decision engine rule tree includes: user behavior event data of a preset client data center are transmitted to a preset flink stream engine through a preset kafka message middleware; and carrying out data filtering, matching and checking on the user behavior event data by using the preset link flow engine, and transmitting the checked user behavior event data into the decision engine rule tree. The whole decision engine rule tree is characterized in that a link flow link is realized by taking a link flow engine as a brain of a flow, taking kafka message middleware as a data channel and taking a java system as tentacles. As shown in fig. 2, the user behavior event data is transmitted from a preset client data center to a preset link flow engine through a preset kafka message middleware, data filtering and matching are performed, and then the user behavior event data passing verification is transmitted to the decision engine rule tree.
Step S13: when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, whether the user behavior event data meets the corresponding preset node judgment condition in the decision engine rule tree is judged, so that a corresponding judgment result is obtained.
In this embodiment, when the user behavior event data is transmitted to any one of the preset decision nodes in the decision engine rule tree, determining whether the user behavior event data meets the corresponding preset node determination condition in the decision engine rule tree includes: when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, a query function of a preset JAVA system is called by the preset flink flow engine to perform corresponding data query on the user behavior event data so as to obtain a behavior event data query result corresponding to the any preset decision node; judging whether the behavior event data query result meets a preset node judgment condition corresponding to any preset decision node in the decision engine rule tree. After the data enter the decision engine rule tree, the data are traversed from the first node in sequence. And (5) conditional judgment is carried out on each node on the tree, and delay waiting and touch configuration are carried out. And the data inflow node calls a query function of a preset JAVA system through the preset flink stream engine to judge whether the condition is met. Wherein the node comprises: the system comprises a head node, an attribute judging node, a behavior event node, a touch-up node, an A/Btust node and an ending node. The head node comprises a ring and a behavior event entry, and can be used for flow validity period, periodic participation limitation and user filtering. The attribute judging node is used for judging the attributes of the user, such as: whether a member. The behavior event node is used for accepting entries of behavior event data, such as: purchase goods, pay attention to public numbers, and the like. The A/Btust node is used as an AB shunt test node. The touch node is used for sending touch information nodes, such as: sending short messages, sending coupons, labeling and the like. The end node is used for ending the marking node.
Step S14: and generating corresponding decision results based on all the judgment results corresponding to the user behavior event data.
In this embodiment, the generating the corresponding decision result based on all the judgment results corresponding to the user behavior event data includes: and calling the preset JAVA system by the preset flink streaming engine to execute the touch actions respectively corresponding to the judgment results in the decision engine rule tree so as to generate corresponding decision results based on the touch actions. If the condition is met, the preset flink streaming engine invokes the preset JAVA system capability to perform corresponding touch actions through touch configuration of the nodes. And sequentially going to the next node, finally reaching the end node, and realizing the decision flow. In this embodiment, the decision result is stored in a preset flow meter according to a preset time period, so as to query the decision result through the preset flow meter.
In this embodiment, the obtained preset decision nodes are spliced and configured through the preset canvas component to obtain a decision engine rule tree; acquiring user behavior event data through a preset data channel, and transmitting the user behavior event data to the decision engine rule tree; when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, judging whether the user behavior event data meets corresponding preset node judgment conditions in the decision engine rule tree or not so as to obtain a corresponding judgment result; and generating corresponding decision results based on all the judgment results corresponding to the user behavior event data. Therefore, the decision engine rule tree obtained through unified configuration can enable marketing rule scenes with large differences to be realized by using one set of marketing decision engine. Personalized marketing strategies may be automatically generated based on the user's behavioral events and personal information. Each business requirement does not need to independently create a marketing rule system, so that resource investment and manpower investment for software development are reduced.
The above embodiments introduce a decision engine generation process for generating marketing strategies. The present embodiment will specifically describe a process of making decisions using a decision engine rule tree.
Referring to fig. 3, the embodiment of the application discloses a specific decision generation method, which comprises the following steps:
step S21: and splicing and configuring the obtained preset decision nodes through a preset canvas component to obtain a decision engine rule tree.
Step S22: and acquiring current user behavior event data through a preset data channel, and transmitting the current user behavior event data into the decision engine rule tree.
Step S23: and taking the first preset decision node in the decision engine rule tree as a current node, and judging whether the current user behavior event data meets preset node judgment conditions of the current node.
Step S24: taking the next preset decision node corresponding to the judgment result in the decision engine rule tree as a new current node, acquiring new current user behavior event data through the preset data channel, and jumping to the step of judging whether the current user behavior event data meets the preset node judgment condition of the current node or not until the current node is the last preset decision node in the decision engine rule tree.
In this embodiment, two or more user behavior event data nodes have complex logic, and logic such as delay waiting and upstream and downstream judgment is required to implement this function. First, as shown in fig. 4, after the user's behavior event M' occurs, whether the user meets the condition is determined by the attribute. If the flow is met, continuing to go down, waiting for N minutes. At this time, it is determined whether the user has "behavior event N", has received no message within N minutes, has checked that LineBC has not been executed, and LineBD has not been executed. If true, the flow is interrupted, false executes lineBD and reaches node D. When the user's behavior event N' occurs, a message is received within N minutes. At this time, it is judged whether LineAB exists, lineBC has not been executed, and LineBD has not been executed. When the result characterizes true: executing the LineBC and touching the node C; the pointer false interrupts the flow. Namely, taking the first preset decision node in the decision engine rule tree as a current node, judging whether the current user behavior event data meets the preset node judgment condition of the current node, if so, jumping to the next node, acquiring new current user behavior event data through the preset data channel, and judging whether the current user behavior event data corresponding to the node meets the preset node judgment condition of the current node.
Step S25: and generating corresponding decision results based on all the judgment results corresponding to the user behavior event data.
The embodiment is widely applied to scenes such as personal promotion, coupon gifting by specific groups, point gifting, marketing short messages, weChat information and the like in an e-commerce system, improves the operation efficiency for users, creates different marketing strategies and provides an efficient tool. Next, as shown in fig. 5, the implementation method of the present application is described using a mall merchant as a case how to implement a new user to pull new promotions. The rule of the decision engine rule tree is as follows, when a new user pays attention to the public number of a merchant, if the new user browses to a commodity sales promotion page behavior event, but does not issue a bill, the decision engine judges whether the user purchases a commodity under the system bill or not after 24 hours, and if the user does not issue a bill, the right platform is triggered to push coupons so as to achieve the effect of drawing a new bill; other promotional links are recommended to facilitate the secondary purchase. Through the management system of the rule tree of the decision engine, namely the management system of the back end of the decision engine, a rule of the decision engine is realized according to business logic and activity rules in a dragging mode on a page. After the new user pays attention to the public number behavior, the upstream message platform broadcasts behavior information of the user pays attention to the public number, and the decision engine receives the information and stores the information. And initiates an action of judging whether the user has placed an order after 24 hours. When a user browses commodity pages of the mall, the upstream mall system broadcasts behavior information of the user browsing the commodity pages of the merchant, and the decision engine rule tree receives and stores the information. When the user makes a purchase, the upstream order platform broadcasts behavior information of the user order, and the decision engine receives the information and stores the information. After 24 hours, the decision engine rule tree initiates actions to judge the behavior of the user of browsing the promotion activity page. If yes, continuing to judge whether the purchasing behavior exists, and if not, ending. If the browsing behavior exists, the query order platform does not have the behavior of 'ordering', if the query order platform does not have the behavior of 'ordering', the equity platform is triggered to push coupons, and if the query order platform does not have the behavior of 'ordering', the message platform is triggered to recommend other promotion links, so that secondary purchase is promoted. The decision engine system pushes coupons through the integrating platform or sends recommended information of the sales promotion and other touch actions through the information platform, and the touch actions are recorded to the preset water meter. And checking the touch result every day through a flow chart, and drawing new effects by a new user.
In this embodiment, according to the prediction result and the classification information of the user, the business rule and the knowledge base are combined to generate the personalized marketing strategy. Related goods or services may be recommended according to the user's preferences. Different marketing plans are formulated for different user groups, etc. Through dragging the component, the self-defined marketing strategy is realized, and a plurality of factors such as user portraits, product characteristics and the like are flexibly considered to generate the self-defined marketing strategy.
As described with reference to fig. 6, the embodiment of the present application further correspondingly discloses a decision generating device, including:
the decision rule tree generation module 11 is configured to splice and configure the obtained preset decision nodes through the preset canvas component to obtain a decision engine rule tree;
the behavior data acquisition module 12 is configured to acquire user behavior event data through a preset data channel, and transmit the user behavior event data to the decision engine rule tree;
the node judging module 13 is configured to judge whether the user behavior event data meets a corresponding preset node judging condition in the decision engine rule tree when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, so as to obtain a corresponding judging result;
marketing results acquisition module 14 for generating corresponding decision results based on all the judgment results corresponding to the user behavior event data.
In this embodiment, the obtained preset decision nodes are spliced and configured through the preset canvas component to obtain a decision engine rule tree; acquiring user behavior event data through a preset data channel, and transmitting the user behavior event data to the decision engine rule tree; when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, judging whether the user behavior event data meets corresponding preset node judgment conditions in the decision engine rule tree or not so as to obtain a corresponding judgment result; and generating corresponding decision results based on all the judgment results corresponding to the user behavior event data. Therefore, the decision engine rule tree obtained through unified configuration can enable marketing rule scenes with large differences to be realized by using one set of marketing decision engine. Personalized marketing strategies may be automatically generated based on the user's behavioral events and personal information. Each business requirement does not need to independently create a marketing rule system, so that resource investment and manpower investment for software development are reduced.
In some specific embodiments, the behavior data acquisition module 12 may specifically include:
the data transmission unit is used for transmitting user behavior event data of a preset client data center to a preset flink streaming engine through a preset kafka message middleware;
and the data matching unit is used for carrying out data filtering, matching and checking on the user behavior event data by utilizing the preset flink stream engine, and transmitting the checked user behavior event data into the decision engine rule tree.
In some specific embodiments, the node determining module 13 may specifically include:
the data query unit is used for calling a query function of a preset JAVA system through the preset flink streaming engine to perform corresponding data query on the user behavior event data when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree so as to obtain a behavior event data query result corresponding to any preset decision node;
and the result judging unit is used for judging whether the behavior event data query result meets the preset node judging condition corresponding to any preset decision node in the decision engine rule tree.
In some specific embodiments, the marketing result obtaining module 14 may be specifically configured to invoke, by the preset link streaming engine, the preset JAVA system to execute the touchdown actions corresponding to the judgment results in the rule tree of the decision engine, so as to generate the corresponding decision result based on the touchdown actions.
In some specific embodiments, the behavior data acquisition module 12 may be specifically configured to acquire current user behavior event data through a preset data channel, and transmit the current user behavior event data to the decision engine rule tree.
In some specific embodiments, the node determining module 13 may specifically include:
the node selection unit is used for taking the first preset decision node in the decision engine rule tree as a current node and judging whether the current user behavior event data meets preset node judgment conditions of the current node or not;
and the step jump unit is used for taking the next preset decision node corresponding to the judgment result in the decision engine rule tree as a new current node, acquiring new current user behavior event data through the preset data channel, and jumping to the step of judging whether the current user behavior event data meets the preset node judgment condition of the current node or not until the current node is the last preset decision node in the decision engine rule tree.
In some specific embodiments, the decision generating apparatus may further include:
and the result storage module is used for storing the decision result into a preset flow water meter according to a preset time period so as to inquire the decision result through the preset flow water meter.
Further, the embodiment of the present application further discloses an electronic device, and fig. 7 is a block diagram of an electronic device 20 according to an exemplary embodiment, where the content of the figure is not to be considered as any limitation on the scope of use of the present application.
Fig. 7 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is configured to store a computer program that is loaded and executed by the processor 21 to implement the relevant steps of the decision making method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon may include an operating system 221, a computer program 222, and the like, and the storage may be temporary storage or permanent storage.
The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and computer programs 222, which may be Windows Server, netware, unix, linux, etc. The computer program 222 may further comprise a computer program capable of performing other specific tasks in addition to the computer program capable of performing the decision making method performed by the electronic device 20 as disclosed in any of the previous embodiments.
Further, the application also discloses a computer readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the previously disclosed decision making method. For specific steps of the method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing has outlined rather broadly the more detailed description of the application in order that the detailed description of the application that follows may be better understood, and in order that the present principles and embodiments may be better understood; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (10)
1. A method of decision generation comprising:
splicing and configuring the obtained preset decision nodes through a preset canvas component to obtain a decision engine rule tree;
acquiring user behavior event data through a preset data channel, and transmitting the user behavior event data to the decision engine rule tree;
when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, judging whether the user behavior event data meets corresponding preset node judgment conditions in the decision engine rule tree or not so as to obtain a corresponding judgment result;
and generating corresponding decision results based on all the judgment results corresponding to the user behavior event data.
2. The method of claim 1, wherein the obtaining the user behavior event data through a preset data channel and inputting the user behavior event data into the decision engine rule tree comprises:
user behavior event data of a preset client data center are transmitted to a preset flink stream engine through a preset kafka message middleware;
and carrying out data filtering, matching and checking on the user behavior event data by using the preset link flow engine, and transmitting the checked user behavior event data into the decision engine rule tree.
3. The method according to claim 2, wherein when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, determining whether the user behavior event data satisfies a corresponding preset node determination condition in the decision engine rule tree comprises:
when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree, a query function of a preset JAVA system is called by the preset flink flow engine to perform corresponding data query on the user behavior event data so as to obtain a behavior event data query result corresponding to the any preset decision node;
judging whether the behavior event data query result meets a preset node judgment condition corresponding to any preset decision node in the decision engine rule tree.
4. The decision-making method according to claim 3, wherein said generating a corresponding decision result based on all the judgment results corresponding to the user behavior event data comprises:
and calling the preset JAVA system by the preset flink streaming engine to execute the touch actions respectively corresponding to the judgment results in the decision engine rule tree so as to generate corresponding decision results based on the touch actions.
5. The method of claim 1, wherein the obtaining the user behavior event data through a preset data channel and inputting the user behavior event data into the decision engine rule tree comprises:
and acquiring current user behavior event data through a preset data channel, and transmitting the current user behavior event data into the decision engine rule tree.
6. The decision-making method of claim 5, wherein said determining whether the user behavior event data satisfies a corresponding preset node determination condition in the decision engine rule tree comprises:
taking the first preset decision node in the decision engine rule tree as a current node, and judging whether current user behavior event data meets preset node judgment conditions of the current node or not;
taking the next preset decision node corresponding to the judgment result in the decision engine rule tree as a new current node, acquiring new current user behavior event data through the preset data channel, and jumping to the step of judging whether the current user behavior event data meets the preset node judgment condition of the current node or not until the current node is the last preset decision node in the decision engine rule tree.
7. The decision-making method according to any one of claims 1 to 6, characterized by further comprising:
and storing the decision result into a preset flow water meter according to a preset time period so as to inquire the decision result through the preset flow water meter.
8. A decision making apparatus, comprising:
the decision rule tree generation module is used for splicing and configuring the obtained preset decision nodes through the preset canvas component to obtain a decision engine rule tree;
the behavior data acquisition module is used for acquiring user behavior event data through a preset data channel and transmitting the user behavior event data to the decision engine rule tree;
the node judging module is used for judging whether the user behavior event data meets the corresponding preset node judging conditions in the decision engine rule tree or not when the user behavior event data is transmitted to any preset decision node in the decision engine rule tree so as to obtain a corresponding judging result;
and the marketing result acquisition module is used for generating corresponding decision results based on all the judgment results corresponding to the user behavior event data.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the decision generation method of any of claims 1 to 7.
10. A computer readable storage medium for storing a computer program which, when executed by a processor, implements the decision generation method of any of claims 1 to 7.
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| CN117435367A (en) * | 2023-11-07 | 2024-01-23 | 上海鱼尔网络科技有限公司 | User behavior processing method, device, equipment, storage medium and program product |
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