User behavior data processing method and device, computer equipment, and storage medium

Abstract

The present invention relates to artificial intelligence technology, which can be applied to the field of smart cities, and discloses a method and device for processing user behavior data, computer equipment, and storage medium, wherein the method is applied to a software as a service (SaaS) platform, the SaaS platform is installed in a server, and the SaaS platform at least includes a configuration module and a Flink engine, and the method includes: obtaining user behavior data operated on an operation interface of a client; predefining user behavior data through a configuration module to obtain predefined data; processing predefined data through a Flink engine to determine business rules that match user behavior data; pushing target business to a client based on business rules, and displaying the target business on an operation interface of the client. The present invention also relates to blockchain technology, and the user behavior data is written into a blockchain node. Through the present invention, the technical problem of low efficiency of business docking between different business parties in the related technology is solved.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a method and device for processing user behavior data, computer equipment, and storage media. Background Art

[0002] At present, in order to meet the various needs of users, the needs of operators are also changing. The Dpp (Digital Production Platform) department supports the business of other departments. However, based on the user behavior judgment, the time of grouping and triggering action is t+1, not real-time. The core of grouping is data preloading and then processing. Then, what users need is a real-time triggering scenario. Operators will analyze these user behaviors and send various card coupons and points to activate our own App (Application); then, most of them are currently based on case by case (specific analysis of specific problems), and it is impossible to make a general tool for use by various departments, resulting in cumbersome and inefficient docking between departments, consuming huge communication costs and manpower, and leading to time-consuming and labor-intensive technical problems in analyzing user behavior data, which cannot meet user needs.

[0003] With respect to the above-mentioned problems existing in the related technologies, no effective solutions have been found so far. Summary of the invention

[0004] The embodiments of the present invention provide a method and apparatus for processing user behavior data, a computer device, and a storage medium, so as to at least solve the technical problem of low efficiency of business connection between different business parties in the related art.

[0005] According to an embodiment of the present invention, a method for processing user behavior data is provided, which is applied to a software as a service (SaaS) platform, wherein the SaaS platform is installed in a server, and the SaaS platform at least includes a configuration module and a Flink engine, and the method includes: obtaining user behavior data operated on an operation interface of a client; predefining the user behavior data through the configuration module to obtain predefined data; processing the predefined data through the Flink engine to determine business rules that match the user behavior data; pushing a target business to the client based on the business rules, and displaying the target business on the operation interface of the client.

[0006] Optionally, obtaining user behavior data operated on the operation interface of the client includes: obtaining encrypted buried data reported in real time by the client, wherein a preset program is embedded in the operation interface of the client, and the preset program is used to collect user behavior data in real time; decrypting the buried data to obtain the user behavior data.

[0007] Optionally, the user behavior data is written into a blockchain node, and the user behavior data is predefined by the configuration module to obtain the predefined data, including: predefining a data source of the user behavior data by the configuration module; predefining a scenario of the user behavior data according to the data source of the user behavior data by the configuration module, and predefining the data source selected for use by the scenario according to the scenario; predefining a data source associated with the original variable corresponding to the scenario based on the data source selected for use by the scenario by the configuration module; and predefining predefined business rules in the data source associated with the original variable corresponding to the scenario by the configuration module to obtain the predefined data.

[0008] Optionally, before processing the predefined data through the Flink engine to determine business rules that match the user behavior data, the method also includes one of the following: storing the predefined data in a cache area of ​​the SaaS platform through an application programming interface (API) of the SaaS platform; writing the predefined data into a cache area of ​​the SaaS platform through a message cache queue of the SaaS platform.

[0009] Optionally, the predefined data is processed by the Flink engine to determine business rules that match the user behavior data, including: reading the predefined data in real time through the application programming interface API or message cache queue of the SaaS platform; based on the target business party connected, parsing the predefined business rules contained in the predefined data by using regular expressions through the parsing engine in the Flink engine to adapt the predefined business rules to the execution engine of the business party; matching the predefined business rules with the target business rules of the target business party to determine the business rules that match the user behavior data.

[0010] Optionally, the predefined business rules are matched with the target business rules of the target business party to determine the business rules that match the user behavior data, including: determining whether a first threshold corresponding to the predefined business rules is the same as a second threshold corresponding to the target business rules; if the first threshold is different from the second threshold, rewriting the first threshold to the second threshold through the rewriting engine in the Flink engine, and using the rewritten predefined business rules as the business rules that match the user behavior data; if the first threshold is the same as the second threshold, using the predefined business rules as the business rules that match the user behavior data; and outputting the business rules that match the user behavior data to the execution engine in the Flink engine through the API interface or the message queue.

[0011] Optionally, pushing the target business to the client based on the business rules includes: executing the business rules matching the predefined data by the Flink engine using logical AND and logical OR operations; and pushing the target business corresponding to the business rules to the client; or, if the business rules have multiple rules, filtering one or more rules with higher priorities from the multiple rules by a merging engine in the Flink engine; and pushing the target business corresponding to the one or more rules to the client.

[0012] According to one embodiment of the present invention, there is provided a device for processing user behavior data, which is applied to a software as a service (SaaS) platform. The SaaS platform is installed in a server. The SaaS platform includes at least a configuration module and a Flink engine. The device includes: an acquisition module, which is used to acquire user behavior data operated on an operation interface of a client; a definition module, which is used to predefine the user behavior data through the configuration module to obtain predefined data; a processing module, which is used to process the predefined data through the Flink to determine business rules that match the user behavior data; and a push module, which is used to push target business to the client based on the business rules and display the target business on the operation interface of the client.

[0013] Optionally, the acquisition module includes: an acquisition unit, used to acquire the encrypted buried data reported by the client in real time, wherein a preset program is embedded in the operation interface of the client, and the preset program is used to collect the user's behavior data in real time; a decryption module, used to decrypt the buried data to obtain the user behavior data.

[0014] Optionally, the user behavior data is written into a blockchain node, and the definition module includes: a first definition unit, used to predefine the data source of the user behavior data through the configuration module; a second definition unit, used to predefine the scenario of the user behavior data according to the data source of the user behavior data through the configuration module, and predefine the data source selected for use by the scenario according to the scenario; a third definition unit, used to predefine the data source associated with the original variable corresponding to the scenario based on the data source selected for use by the scenario through the configuration module; and a fourth definition unit, used to obtain the predefined data by predefining business rules in the data source associated with the original variable corresponding to the scenario through the configuration module.

[0015] Optionally, the device also includes one of the following: a storage module, used to store the predefined data in a cache area of ​​the SaaS platform through the application programming interface API of the SaaS platform before processing the predefined data through the Flink engine to determine business rules that match the user behavior data; a writing module, used to write the predefined data into the cache area of ​​the SaaS platform through the message cache queue of the SaaS platform.

[0016] Optionally, the processing module includes: a reading unit, used to read the predefined data in real time through the application program interface API or the message cache queue of the SaaS platform; a parsing unit, used to parse the predefined business rules contained in the predefined data by using regular expressions through the parsing engine in the Flink engine based on the target business party connected, so as to adapt the predefined business rules to the execution engine of the business party; a matching unit, used to match the predefined business rules with the target business rules of the target business party to determine the business rules that match the user behavior data.

[0017] Optionally, the matching unit includes: a judgment subunit, used to judge whether a first threshold corresponding to the predefined business rule is the same as a second threshold corresponding to the target business rule; a determination unit, used to rewrite the first threshold to the second threshold through the rewrite engine in the Flink engine if the first threshold is different, and use the rewritten predefined business rule as the business rule that matches the user behavior data; if the first threshold is the same as the second threshold, use the predefined business rule as the business rule that matches the user behavior data; an output unit, used to output the business rule that matches the user behavior data to the execution engine in the Flink engine through the API interface or the message queue.

[0018] Optionally, the push module includes: an execution unit, used to execute business rules matching the predefined data through the Flink engine using logical AND and logical OR operations; and push target businesses corresponding to the business rules to the client; or, a screening unit, used to screen one or more rules with higher priorities from the multiple rules through a merging engine in the Flink engine if the business rules have multiple rules; and push target businesses corresponding to the one or more rules to the client.

[0019] According to another embodiment of the present invention, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0020] According to yet another embodiment of the present invention, a storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above-mentioned device embodiments when running.

[0021] Through the present invention, by installing a configuration module and a Flink engine in a SaaS platform, user behavior data on the client's operation interface is obtained; the configuration module predefines the user behavior data; the predefined data is then processed by the Flink engine to determine the business rules that match the user behavior data; the target business is pushed to the client based on the business rules, and the target business is displayed on the client's operation interface, and the business rules in the user behavior data are parsed by a general parsing engine, so that the parsed business rules are adapted to different business parties, thereby solving the technical problem of low business docking efficiency between different business parties in the related technology and greatly improving the business docking efficiency between different business parties. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 It is a hardware structure block diagram of a method for processing user behavior data according to an embodiment of the present invention applied to a computer terminal;

[0024] Figure 2 is a flow chart of a method for processing user behavior data according to an embodiment of the present invention;

[0025] Figure 3 is a flow chart of predefined data provided according to an embodiment of the present invention;

[0026] Figure 4 This is a system framework diagram of a rule engine integrated service platform developed based on a SaaS platform provided according to an embodiment of the present invention;

[0027] Figure 5 is a schematic diagram of a business rule provided according to an embodiment of the present invention;

[0028] Figure 6 It is a structural block diagram of a device for processing user behavior data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0031] Example 1

[0032] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a server, a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure diagram of a method for processing user behavior data according to an embodiment of the present invention applied to a computer terminal. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0033] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for processing user behavior data in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0034] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] In order to solve the above problems, a method for processing user behavior data is provided in this embodiment. The method is applied to a software as a service (SaaS) platform. The SaaS platform is installed in a server. The SaaS platform includes at least a configuration module and a Flink engine. Figure 2 is a flow chart of a method for processing user behavior data according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0036] Step S202, obtaining user behavior data operated on the operation interface of the client;

[0037] In this embodiment, user real-time behavior data refers to real-time behavior data generated by the user corresponding to the client through operations on the client display interface, such as user browsing web pages, searching, following, adding to cart, collecting, background operations and other data.

[0038] Step S204, pre-defining the user behavior data through the configuration module to obtain pre-defined data;

[0039] In this embodiment, the data source, scenario, label, rights, etc. are defined according to pre-set rules for the acquired user behavior data.

[0040] Step S206, processing the predefined data through the Flink engine to determine the business rules that match the user behavior data;

[0041] The Flink engine in this embodiment is a distributed processing engine for stream data and batch data. The code is mainly implemented in Java, and part of the code is in Scala. It can process bounded batch data sets as well as unbounded real-time data sets, thereby improving the processing efficiency of user behavior data.

[0042] Step S208: Push the target service to the client based on the service rules, and display the target service on the operation interface of the client.

[0043] Optionally, the target business in this embodiment may be ordering, payment, check-in, group buying, electronic vouchers, etc.

[0044] Through the above steps, by installing the configuration module and Flink engine in the SaaS platform, the user behavior data on the client's operation interface is obtained; the configuration module predefines the user behavior data; then the predefined data is processed by the Flink engine to determine the business rules that match the user behavior data; the target business is pushed to the client based on the business rules, and the target business is displayed on the client's operation interface. The business rules in the user behavior data are parsed by the general parsing engine, so that the parsed business rules are adapted to different business parties, which solves the technical problem of low business docking efficiency between different business parties in related technologies and greatly improves the business docking efficiency between different business parties.

[0045] In an optional embodiment of the present case, obtaining user behavior data operated on the operating interface of the client includes: obtaining encrypted buried data reported by the client in real time, wherein a preset program is embedded in the operating interface of the client, and the preset program is used to collect user behavior data in real time; decrypting the buried data to obtain user behavior data.

[0046] In this embodiment, by pre-setting tracking points at various locations on the client's operating interface, after detecting the user's operation, the user behavior data is collected and logged, and then the user behavior data is encrypted and reported to the server; the server decrypts the received tracking point data to obtain the user's real-time behavior data, thereby achieving the beneficial effect of real-time collection of user behavior data.

[0047] Optionally, the user behavior data is written into the blockchain node, and the user behavior data is predefined through the configuration module to obtain the predefined data, including: predefining the data source of the user behavior data through the configuration module; predefining the scenario of the user behavior data according to the data source of the user behavior data through the configuration module, and predefining the data source to be used according to the scenario; predefining the data source associated with the original variable corresponding to the scenario based on the data source selected for use by the configuration module based on the scenario; and predefining the predefined business rules in the data source associated with the original variable corresponding to the predefined scenario through the configuration module to obtain the predefined data.

[0048] The blockchain in this embodiment is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods, each of which contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the blockchain underlying platform, platform product service layer and application service layer.

[0049] The underlying blockchain platform can include processing modules such as user management, basic services, smart contracts, and operation monitoring. The user management module is responsible for the identity information management of all blockchain participants, including maintaining public and private key generation (account management), key management, and the corresponding relationship between the user's real identity and the blockchain address (authority management), etc., and, under authorization, supervises and audits the transactions of certain real identities and provides risk control rule configuration (risk control audit); the basic service module is deployed on all blockchain node devices to verify the validity of business requests, and records valid requests to storage after consensus is reached. For a new business request, the basic service first performs interface adaptation analysis and authentication processing (interface adaptation), and then encrypts the business information through the consensus algorithm (consensus management). The smart contract module is responsible for the registration and issuance of contracts, as well as contract triggering and contract execution. Developers can define the contract logic in a programming language and publish it to the blockchain (contract registration). According to the logic of the contract terms, the key or other events are called to trigger the execution and complete the contract logic. It also provides the function of contract upgrade and cancellation. The operation monitoring module is mainly responsible for the deployment, configuration modification, contract setting, cloud adaptation and real-time status visualization output of the product during the product release process, such as alarm, network monitoring, node equipment health monitoring, etc.

[0050] In this embodiment, pre-defining user behavior data includes: defining data sources; defining scenarios (i.e., scenario management, scenarios are used to bind data sources); defining original variables to associate with data sources; and then establishing rules (i.e., rule management), see Figure 3 , Figure 3 It is a flowchart of predefined data provided according to an embodiment of the present invention. Different business parties correspond to different business scenarios. After the application scenario is determined according to the business data, the target rule is selected from multiple business rules based on the process of executing the business rules, and the execution result of the target rule is fed back.

[0051] Optionally, before the predefined data is processed by the Flink engine to determine the business rules that match the user behavior data, one of the following is also included: storing the predefined data in the cache area of ​​the SaaS platform through the application program interface API of the SaaS platform; writing the predefined data into the cache area of ​​the SaaS platform through the message cache queue of the SaaS platform.

[0052] Figure 4 is a system framework diagram of a rule engine integrated service platform developed based on a SaaS platform according to an embodiment of the present invention, see Figure 4 The input layer is the data flow entrance of the rule engine integrated service platform, and all business parties access the rule engine integrated service platform through the input layer. Preferably, this embodiment provides two data access methods: API interface and kafka, so as to store the acquired user behavior data.

[0053] Preferably, if the access party needs to pass the information of the rules in its own business, it calls the API interface or writes to the Kafka message queue to provide the variables (i.e. variable management), tags (i.e. tag management, such as user tag data), and actions (i.e. action management, such as providing predefined data based on user behavior data) required by its own rules to the input layer; then outputs it to the cache area, and caches the rule information and configuration information of the input data in the cache layer.

[0054] In another optional embodiment of the present case, predefined data is processed through the Flink engine to determine business rules that match the user behavior data, including: reading the predefined data in real time through the application program interface API or message cache queue of the SaaS platform; based on the target business party connected, parsing the predefined business rules contained in the predefined data through the parsing engine in the Flink engine using regular expressions to adapt the predefined business rules to the execution engine of the business party; matching the predefined business rules with the target business rules of the target business party to determine the business rules that match the user behavior data.

[0055] In this embodiment, the predefined data is obtained through the API interface (full name: Application Programming Interface) of the input layer, and / or by reading the predefined data from Kafka (a distributed message queue based on the publish / subscribe mode). The API interface is a set of definitions, programs and protocols, the purpose of which is to provide application programs and developers with the ability to access a set of routines without having to access the source code or understand the details of the internal working mechanism, and to achieve mutual communication between computer software through the API interface.

[0056] See also Figure 4 ,Analysis of user behavior data is located in the system layer of the rule engine comprehensive service platform, which mainly includes: project configuration (such as data source address configuration, variable configuration, scenario configuration, rule configuration, label configuration, permission configuration, etc.), monitoring operation management (at least including aggregation query analysis, background parameter configuration, playback (restoring the event scene)), etc.), decision calculation using the Flink engine (including preheating processing clustering technology, offline processing QLExpress (a scripting language parsing tool), real-time processing flink).

[0057] In this embodiment, the acquired predefined data is put into the memory (cache layer), and then the data in the memory is filtered and analyzed according to the rules. When the data analysis in the memory is completed, the data in the memory is cleared, and the next batch of data is read in for a new round of analysis until all the data is processed.

[0058] Optionally, the DPP department will parse the regular expression, and the kernel uses qlexpress, a lightweight Java-like syntax rule engine, as an embedded rule engine used in the business system, making the business rule definition simple and flexible; business personnel can define business rules; support standard JAVA syntax; and can also support custom operator symbols, operator symbol overloading, function definition, macro definition, data delay loading, etc., parse parameters through the qlexpress rule engine, and match with the rules in the database. In another example, groovy (an agile development language based on JVM (Java Virtual Machine), a mature object-oriented programming language) and other languages ​​can also be used to implement rule parsing.

[0059] Since each department has different parsing requirements, the parsing of expressions needs to be uniformly adapted to its own execution engine. For example, if the insurance type = third-party liability insurance, it will be parsed into dutyCode (rule code) == sanzhexian, so that different business parties can identify the third-party liability insurance.

[0060] In an optional embodiment, the predefined business rules are matched with the target business rules of the target business party to determine the business rules that match the user behavior data, including: determining whether a first threshold corresponding to the predefined business rule is the same as a second threshold corresponding to the target business rule; if the first threshold is different from the second threshold, rewriting the first threshold to the second threshold through the rewriting engine in the Flink engine, and using the rewritten predefined business rules as the business rules that match the user behavior data; if the first threshold is the same as the second threshold, using the predefined business rules as the business rules that match the user behavior data; and outputting the business rules that match the user behavior data to the execution engine in the Flink engine through an API interface or a message queue.

[0061] In one example, assuming that the predefined rule is that if the user executes the event more than 3 times (i.e., the first threshold), then rewrite. For example, when it is detected that the user clicks on a web page 5 times (i.e., the second threshold), the event is parsed and rewritten to the actual number of times the user executes, i.e., to 5 times.

[0062] Optionally, part of the expression will be rewritten in the rule engine. For example, dutyCode(rule code)==sanzhexian will be rewritten as: sanzhexian==sanzhexian through assignment.

[0063] In an optional embodiment, pushing target business to customers based on business rules includes: executing business rules that match predefined data through the Flink engine using logical AND and logical OR operations; and pushing target business corresponding to the business rules to the client; or, if there are multiple business rules, filtering one or more rules with higher priority from the multiple rules through the merging engine in the Flink engine; and pushing target business corresponding to the one or more rules to the client.

[0064] Preferably, a true or false is finally fed back through the relationship of logical "and" and logical "or". When true is fed back, the corresponding action of true is also fed back. For example, (dutyCode (rule code) == sanzhexian)||(xxx == 111), this can be directly executed after parsing. In one example, the execution engine includes at least one of the following: rule execution order (such as priority order), synchronous asynchronous execution, skipping certain rules, rule set short circuit, etc.

[0065] In another embodiment of the present case, if there is an inclusion relationship between multiple rules, the merging engine is used to merge the multiple rules. For example, rule 1 is a subset of rule 2. When both rule 1 and rule 2 are matched, if rule 2 has a higher priority, only rule 2 will be executed. For example, when processing an order with a value of more than 50 yuan, the business rule executed is to deduct 30 yuan for orders over 50 yuan.

[0066] Preferably, the existing rules are loaded in a pre-loaded manner, and the loaded objects are cached. The entire rule is reloaded each time the rule is changed, which greatly improves the execution efficiency of the engine.

[0067] Through this embodiment, the flink (a distributed computing engine) real-time computing engine is used in the service layer. After the business triggers the behavior rules, it enters the flink engine for real-time computing and processing, and then sends the kafka message. However, flink cannot be dynamically compiled, so the real-time parsing engine here is needed to cooperate and complement. The real-time computing of the flink engine saves the user's status. For example, if there is an event of clicking the XX button more than 5 times within 3 days, an XX message is sent. By using the flink engine, after the non-perceptual access of kafka, kafka is used as the source of data to identify the business data that meets the rules, and then specific business is executed.

[0068] In summary, the user contact time has changed from t+1 to real-time triggering; user behavior analysis is effective in real time and dynamically; interfaces are unified and standardized, which greatly saves manpower and improves efficiency costs; subsequent scalability is also enhanced; variables, actions, tags and other data can be easily managed and integrated; the technology has also been upgraded from the original interface access to stream processing operations.

[0069] The present invention is further described below in conjunction with a specific embodiment:

[0070] First, if the data source of each business can be identified, the data source can be defined at will. For example, the data source defined by the Compass tracking data is Compass; see Figure 5 As shown, Figure 5 It is a schematic diagram of a business rule provided according to an embodiment of the present invention, by abstracting actions and conditions, such as "recommendation engine / car insurance type", "equal to", "motor vehicle theft and robbery insurance"; and executing the business rules through logical AND and logical OR.

[0071] According to the above embodiment, the code for feeding back the result of the executed business rule is as follows:

[0072]

[0073]

[0074] That is, the premium (insuredAmount) of the insurance type CV09003 is modified to 180, and then feedback is given to the caller (i.e. the target business party); the operation of the feedback result represents a series of action types. The above example is the edit operation, which is the default one provided to the user. The same type includes at least add and send, etc. The caller only needs to select the action he needs to express his meaning according to his business needs; if none of them meet the requirements, you can customize the action type you need, for example, Main (object), property (object property), result (execution data).

[0075] Through the above-mentioned embodiments, other departments can abstract actions, conditions, judgment statements, input parameters, and output parameters according to this unified interface document specification, and achieve the effect of real-time configuration. For example, when a department is connected, it imports the conditions and actions it needs according to the interface document, and informs what tags are needed. Tags are additional conditions, so that the desired results can be effective in real time, maximizing the number of active operators. When different system sources are accessed, they also see their own data sources, so that the permissions are separated and maintenance is easy later. When the script is running, the business can change the behavior of reaching users at any time according to the data analyzed from user behavior, increase user experience, and help subsequent modules improve activity. Moreover, this does not require version release, and it can be said that it is completely imperceptible to users; the fault tolerance rate is greatly improved.

[0076] Example 2

[0077] In this embodiment, a user behavior data processing device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0078] Figure 6 is a structural block diagram of a device for processing user behavior data according to an embodiment of the present invention. Figure 6As shown, the device is applied to a software as a service SaaS platform, and the SaaS platform is installed in a server. The SaaS platform includes at least a configuration module and a Flink engine, including: an acquisition module 60, used to obtain user behavior data operated on the client's operation interface; a definition module 62, connected to the above-mentioned acquisition module 60, used to predefine the user behavior data through the configuration module to obtain predefined data; a processing module 64, connected to the above-mentioned definition module 62, used to process the predefined data through Flink to determine the business rules that match the user behavior data; a push module 66, connected to the above-mentioned processing module 64, used to push the target business to the client based on the business rules, and display the target business on the client's operation interface.

[0079] Optionally, the acquisition module 60 includes: an acquisition unit, used to obtain the encrypted buried data reported by the client in real time, wherein a preset program is embedded in the client's operation interface, and the preset program is used to collect the user's behavior data in real time; a decryption module, used to decrypt the buried data to obtain the user's behavior data.

[0080] Optionally, the user behavior data is written into the blockchain node, and the definition module 62 includes: a first definition unit, used to predefine the data source of the user behavior data through the configuration module; a second definition unit, used to predefine the scenario of the user behavior data according to the data source of the user behavior data through the configuration module, and predefine the data source selected for use according to the scenario; a third definition unit, used to predefine the data source associated with the original variable corresponding to the scenario based on the data source selected for use by the scenario through the configuration module; and a fourth definition unit, used to obtain predefined data by predefining business rules in the data source associated with the original variable corresponding to the predefined scenario through the configuration module.

[0081] Optionally, the above-mentioned device also includes one of the following: a storage module, which is used to store the predefined data in the cache area of ​​the SaaS platform through the application programming interface API of the SaaS platform before processing the predefined data through the Flink engine to determine the business rules that match the user behavior data; a writing module, which is used to write the predefined data into the cache area of ​​the SaaS platform through the message cache queue of the SaaS platform.

[0082] Optionally, the processing module 64 includes: a reading unit, used to read predefined data in real time through the application program interface API or message cache queue of the SaaS platform; a parsing unit, used to parse the predefined business rules contained in the predefined data by using regular expressions through the parsing engine in the Flink engine based on the target business party connected, so as to adapt the predefined business rules to the execution engine of the business party; a matching unit, used to match the predefined business rules with the target business rules of the target business party to determine the business rules that match the user behavior data.

[0083] Optionally, the matching unit includes: a judgment subunit, used to judge whether a first threshold corresponding to the predefined business rule is the same as a second threshold corresponding to the target business rule; a determination unit, used to rewrite the first threshold to the second threshold through the rewrite engine in the Flink engine if the first threshold is different, and use the rewritten predefined business rule as the business rule that matches the user behavior data; if the first threshold is the same as the second threshold, use the predefined business rule as the business rule that matches the user behavior data; an output unit, used to output the business rule that matches the user behavior data to the execution engine in the Flink engine through an API interface or a message queue.

[0084] Optionally, the push module 66 includes: an execution unit, which is used to execute business rules that match predefined data through the Flink engine using logical AND and logical OR operations; and push target businesses corresponding to the business rules to the client; or, a screening unit, which is used to screen one or more rules with higher priorities from the multiple rules through a merging engine in the Flink engine if there are multiple business rules; and push the target businesses corresponding to the one or more rules to the client.

[0085] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0086] Example 3

[0087] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0088] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0089] S1, obtaining user behavior data operated on the client operation interface;

[0090] S2, predefining the user behavior data through the configuration module to obtain predefined data;

[0091] S3, processing the predefined data through the Flink engine to determine a business rule matching the user behavior data;

[0092] S4, pushing the target service to the client based on the service rule, and displaying the target service on the operation interface of the client.

[0093] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0094] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0095] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0096] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0097] S1, obtaining user behavior data operated on the client operation interface;

[0098] S2, predefining the user behavior data through the configuration module to obtain predefined data;

[0099] S3, processing the predefined data through the Flink engine to determine a business rule matching the user behavior data;

[0100] S4, pushing the target service to the client based on the service rule, and displaying the target service on the operation interface of the client.

[0101] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0102] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for processing user behavior data, applied to a software as a service (SaaS) platform, wherein the SaaS platform is installed in a server, and characterized in that: The SaaS platform includes at least a configuration module and a Flink engine, and the method includes: Obtain user behavior data on the client's operation interface; Predefine the user behavior data through the configuration module to obtain predefined data; Processing the predefined data through the Flink engine to determine business rules that match the user behavior data; Pushing the target service to the client based on the service rule, and displaying the target service on the operation interface of the client; The processing of the predefined data by the Flink engine to determine a business rule matching the user behavior data includes: Read the predefined data in real time through the application program interface API or message cache queue of the SaaS platform; Based on the accessed target business party, the parsing engine in the Flink engine parses the predefined business rules contained in the predefined data using regular expressions, so that the predefined business rules are adapted to the execution engine of the business party; Matching the predefined business rules with the target business rules of the target business party to determine the business rules that match the user behavior data; The matching of the predefined business rules with the target business rules of the target business party to determine the business rules matching the user behavior data includes: Determining whether a first threshold corresponding to the predefined business rule is the same as a second threshold corresponding to the target business rule; If the first threshold value is different from the second threshold value, the first threshold value is rewritten into the second threshold value by the rewriting engine in the Flink engine, and the rewritten predefined business rule is used as the business rule matching the user behavior data; if the first threshold value is the same as the second threshold value, the predefined business rule is used as the business rule matching the user behavior data; The business rules matching the user behavior data are output to the execution engine in the Flink engine through the API interface or the message queue.

2. The method according to claim 1, characterized in that The user behavior data obtained on the client's operation interface includes: Obtaining encrypted tracking data reported by the client in real time, wherein a preset program is embedded in the operation interface of the client, and the preset program is used to collect user behavior data in real time; The buried data is decrypted to obtain the user behavior data.

3. The method according to claim 1, characterized in that The user behavior data is written into the blockchain node, and the user behavior data is predefined by the configuration module to obtain the predefined data including: Predefining the data source of the user behavior data by the configuration module; Predefining the scenario of the user behavior data according to the data source of the user behavior data through the configuration module, and predefining the data source for use in the scenario according to the scenario; The configuration module selects a data source to be used based on the scenario and predefines a data source associated with the original variable corresponding to the scenario; The predefined data is obtained by predefining the predefined business rules in the data source associated with the original variables corresponding to the scenario through the configuration module.

4. The method according to claim 1, characterized in that Before processing the predefined data by the Flink engine to determine a business rule matching the user behavior data, the method further includes one of the following: Storing the predefined data in a cache area of ​​the SaaS platform through an application programming interface (API) of the SaaS platform; The predefined data is written into the cache area of ​​the SaaS platform through the message cache queue of the SaaS platform.

5. The method according to claim 1, characterized in that Pushing the target service to the customer based on the service rule includes: Execute the business rules matching the predefined data by using the Flink engine using logical AND and logical OR operations; and push the target business corresponding to the business rules to the client; or, If the business rule has multiple rules, one or more rules with higher priority are selected from the multiple rules by a merging engine in the Flink engine; and target businesses corresponding to the one or more rules are pushed to the client.

6. A user behavior data processing device, applied to a software as a service (SaaS) platform, wherein the SaaS platform is installed in a server, characterized in that: The SaaS platform includes at least a configuration module and a Flink engine, and the device includes: The acquisition module is used to acquire the user behavior data operated on the client operation interface; A definition module, used for predefining the user behavior data through the configuration module to obtain predefined data; A processing module, configured to process the predefined data through Flink to determine a business rule matching the user behavior data; A push module, used to push the target service to the client based on the service rule and display the target service on the operation interface of the client; The processing module includes: a reading unit, which is used to read the predefined data in real time through the application program interface API or the message cache queue of the SaaS platform; a parsing unit, which is used to parse the predefined business rules contained in the predefined data by using regular expressions through the parsing engine in the Flink engine based on the target business party connected, so as to adapt the predefined business rules to the execution engine of the business party; a matching unit, which is used to match the predefined business rules with the target business rules of the target business party to determine the business rules that match the user behavior data; The matching unit includes: a judgment subunit, used to judge whether a first threshold corresponding to a predefined business rule is the same as a second threshold corresponding to a target business rule; a determination unit, used to rewrite the first threshold into the second threshold through a rewriting engine in a Flink engine if the first threshold is different from the second threshold, and use the rewritten predefined business rule as a business rule matching the user behavior data; if the first threshold is the same as the second threshold, use the predefined business rule as a business rule matching the user behavior data; an output unit, used to output the business rule matching the user behavior data to an execution engine in the Flink engine through an API interface or a message queue.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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