Data processing method, data processing device, electronic device and storage medium

Abstract

The present application provides a data processing method, a data processing device, an electronic device and a storage medium, which belongs to the field of data processing technology. The method includes: obtaining target data of a target object; performing data combination processing on the basic information, operation information, target event and reference resource data in the target data to obtain a target data block; performing image generation based on the flow status of the target event and multiple target data blocks to obtain an event storm image; performing vocabulary extraction on the target event according to the event storm image to obtain a target vocabulary; performing clustering processing on the target vocabulary to obtain a vocabulary set, performing module combination processing on the business modules of the business platform according to the clustering keywords of the vocabulary set to obtain a target service unit, and the target service unit is used to perform data processing on the target resource data to be processed according to the acquired control command. The present application can efficiently manage resource data based on the target service unit and speed up the flow of resource data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing method, a data processing device, an electronic device, and a storage medium. Background Art

[0002] At present, there is a lack of systematic and digital business systems to manage business resource data, which leads to the inability to effectively integrate relevant resources, efficiently manage relevant resources and quickly circulate relevant resources. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a data processing method, a data processing device, an electronic device and a storage medium, aiming to efficiently manage resource data and accelerate the flow of resource data.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application provides a data processing method, the method comprising:

[0005] Acquire target data of a target object; wherein the target data includes basic information of the target object, operation information, a target event triggered by the target object based on the operation information on a preset business platform, and reference resource data used to trigger the target event;

[0006] Performing data combination processing on the basic information, the operation information, the target event, and the reference resource data to obtain a target data block;

[0007] Generate an image based on the flow state of the target event and the plurality of target data blocks to obtain an event storm image;

[0008] Performing vocabulary extraction on the target event according to the event storm image to obtain a target vocabulary for the target event;

[0009] Clustering the target vocabulary according to a preset clustering condition to obtain a plurality of vocabulary sets; wherein the vocabulary sets include clustering keywords;

[0010] According to each of the clustering keywords, multiple business modules in the business platform are combined to obtain a target service unit, which is used to process the target resource data to be processed according to the acquired control command to obtain the data processing result of the target resource data.

[0011] In some embodiments, obtaining target data of the target object includes:

[0012] Acquire initial resource data from a preset data acquisition source, and acquire the reference resource data from a preset storage path;

[0013] The initial resource data is annotated according to a preset marking method to obtain intermediate resource data; wherein the intermediate resource data includes a first coding field and a second coding field, the first coding field is used to represent the identity of the data acquisition source, and the second coding field is used to represent the identity of the intermediate resource data;

[0014] The intermediate resource data is processed based on the reference resource data and the operation information to obtain the target event.

[0015] In some embodiments, the target event includes a first event, the reference resource data includes an icebreaker tool, and processing the intermediate resource data based on the reference resource data and the operation information to obtain the target event includes:

[0016] Obtaining the current time of the service platform;

[0017] If the current time is greater than a preset time threshold, or the amount of the intermediate resource data is greater than a preset amount threshold, information creation processing is performed on all the intermediate resource data based on the icebreaking tool and the pre-acquired creation batch command to obtain the first event.

[0018] In some embodiments, the target event includes at least one of the following events: a first event, a second event, a third event, and a fourth event; the operation information includes at least one of a resource recovery command, a resource delivery command, and a resource cleanup command; and the processing of the intermediate resource data based on the reference resource data and the operation information to obtain the target event further includes at least one of the following steps:

[0019] Recycling the intermediate resource data based on the reference resource data and the resource recycling command to obtain the second event;

[0020] performing distribution processing on the intermediate resource data based on the reference resource data and the resource distribution command, and distributing the intermediate resource data to a preset target location to obtain the third event;

[0021] The intermediate resource data is cleansed based on the reference resource data and the resource cleansing command to obtain the fourth event.

[0022] In some embodiments, the step of performing distribution processing on the intermediate resource data based on the reference resource data and the resource distribution command, and distributing the intermediate resource data to a preset target location to obtain the third event includes:

[0023] Get the preset target time;

[0024] The target resource data is issued according to the target time, the reference resource data and the resource issuance command, and the target resource data is issued to the target location at the target time to obtain the third event.

[0025] In some embodiments, clustering the target vocabulary according to a preset clustering condition to obtain multiple vocabulary sets includes:

[0026] Obtaining the business type of the target event;

[0027] The target vocabulary is clustered according to the basic information or the business type to obtain the vocabulary set.

[0028] In some embodiments, clustering the target vocabulary according to the basic information or the business type to obtain the vocabulary set includes:

[0029] performing clustering processing on the target words according to the basic information, clustering the target words from the same basic information into the same vocabulary set, and generating the cluster keywords according to the target words;

[0030] or,

[0031] The target words are clustered according to the business type, the target words belonging to the same business type are clustered into the same word set, and the cluster keywords are generated according to the target words.

[0032] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a data processing device, comprising:

[0033] An acquisition module, configured to acquire target data of a target object; wherein the target data includes basic information of the target object, operation information, a target event triggered by the target object based on the operation information on a preset business platform, and reference resource data used to trigger the target event;

[0034] A first combining module is configured to perform data combining processing on the basic information, the operation information, the target event, and the reference resource data to obtain a target data block;

[0035] An image generation module, configured to generate an image based on the flow state of the target event and a plurality of the target data blocks to obtain an event storm image;

[0036] A vocabulary extraction module, configured to extract vocabulary from the target event according to the event storm image to obtain a target vocabulary for the target event;

[0037] A clustering module, configured to cluster the target vocabulary according to a preset clustering condition to obtain a plurality of vocabulary sets; wherein the vocabulary sets include clustering keywords;

[0038] The second combination module is used to perform module combination processing on multiple business modules in the business platform according to each of the clustering keywords to obtain a target service unit; wherein, the target service unit is used to perform data processing on the target resource data to be processed according to the acquired control command to obtain the data processing result of the target resource data.

[0039] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0040] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0041] The data processing method, data processing device, electronic device and computer-readable storage medium proposed in the present application obtain target data of a target object, wherein the target data includes basic information of the target object, operation information, target events triggered by the target object based on the operation information on a preset business platform, and reference resource data for triggering the target event, and performs data combination processing on the basic information, operation information, target event and reference resource data to obtain a target data block, generates an image based on the flow state of the target event and multiple target data blocks to obtain an event storm image, and visualizes the basic information, operation information, target event and reference resource data of the target object through the event storm image, which can intuitively display the same links and different links of different business modules on the business platform. Furthermore, vocabulary extraction is performed on the target event based on the event storm image to obtain the target vocabulary of the target event, and the target vocabulary is clustered according to preset clustering conditions to obtain multiple vocabulary sets, wherein the vocabulary sets include cluster keywords, and vocabulary extraction can be performed on the target event based on the event storm image, and the target vocabulary can be quickly identified and clustered to obtain reusable and abstract business modules in the business platform. Finally, based on each clustered keyword, multiple business modules in the business platform are combined to form a target service unit. The target service unit processes the target resource data to be processed based on the acquired control command, generating the data processing result for the target resource data. By combining business modules to form a target service unit, resource data can be efficiently managed based on the target service unit, accelerating the flow of resource data. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of the data processing method provided in an embodiment of the present application;

[0043] Figure 2 yes Figure 1 Flowchart of step S110 in FIG.

[0044] Figure 3 yes Figure 2 The first flow chart of step S230 in FIG.

[0045] Figure 4 yes Figure 2 A second flow chart of step S230 in FIG.

[0046] Figure 5 yes Figure 4 Flowchart of step S420 in FIG.

[0047] Figure 6 yes Figure 1 Flowchart of step S150 in FIG.

[0048] Figure 7 yes Figure 6 Flowchart of step S620 in FIG.

[0049] Figure 8 is a structural diagram of a data processing device provided in an embodiment of the present application;

[0050] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0052] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0054] First, let’s analyze some of the terms used in this application:

[0055] Clustering: The process of dividing a collection of physical or abstract objects into multiple classes consisting of similar objects is called clustering. The clusters generated by clustering are a collection of data objects that are similar to objects in the same cluster and different from objects in other clusters. In natural sciences and social sciences, there are a large number of classification problems. Cluster analysis, also known as group analysis, is a statistical analysis method for studying (sample or indicator) classification problems. Cluster analysis originated from taxonomy, but clustering is not equal to classification. The difference between clustering and classification is that the classes required for clustering are unknown. Cluster analysis has a very rich content, including systematic clustering, ordered sample clustering, dynamic clustering, fuzzy clustering, graph clustering, cluster prediction, etc.

[0056] Microservices: Microservices is an architectural style that splits a project into multiple services that can run independently. Each service can use different storage methods and development models. Monolithic architecture couples all modules together, resulting in large amounts of code and difficult maintenance. All modules share a database with a single storage method, and all modules must use the same development technology. Compared to monolithic architecture, microservice architecture has small code size, is easy to maintain, has diverse storage methods, and has flexible development models, enabling agile application development.

[0057] Bounded context: Bounded context can be divided into two words: bound and context. Bound refers to a certain limit or scope, and context refers to the domain model, that is, business knowledge. The bounded context specifies the business boundaries of the domain model and is used to encapsulate common language and domain objects.

[0058] Entity: An entity refers to a domain object in a domain model, that is, a business entity object, which is a carrier of multiple attributes, operations, or behaviors.

[0059] At present, the insurance industry has a large amount of business resource data in the two major business lines of issuing policies and recruiting new employees. However, there is a lack of systematic and digital business systems to manage business resource data, resulting in the inability to effectively integrate related resources, efficiently manage related resources, and quickly circulate related resources.

[0060] Based on this, the embodiments of the present application provide a data processing method, a data processing device, an electronic device and a storage medium, which aim to efficiently manage resource data and accelerate the flow of resource data.

[0061] The data processing method, data processing device, electronic device and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the data processing method in the embodiments of the present application is described.

[0062] The data processing method provided in the embodiment of the present application relates to the field of data processing technology. The data processing method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the data processing method, etc., but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0064] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0065] Figure 1 This is an optional flowchart of the data processing method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S160.

[0066] Step S110, acquiring target data of the target object; wherein the target data includes basic information of the target object, operation information, target events triggered by the target object based on the operation information on a preset business platform, and reference resource data used to trigger the target event;

[0067] Step S120, combining the basic information, operation information, target event, and reference resource data to obtain a target data block;

[0068] Step S130 , generating an image based on the flow state of the target event and multiple target data blocks to obtain an event storm image;

[0069] Step S140, extracting vocabulary for the target event based on the event storm image to obtain target vocabulary for the target event;

[0070] Step S150, clustering the target vocabulary according to a preset clustering condition to obtain a plurality of vocabulary sets; wherein the vocabulary sets include clustering keywords;

[0071] Step S160, performing module combination processing on multiple business modules in the business platform according to each cluster keyword to obtain a target service unit; wherein the target service unit is used to perform data processing on the target resource data to be processed according to the acquired control command to obtain a data processing result of the target resource data.

[0072] In the steps S110 to S160 shown in the embodiment of the present application, the target data of the target object is obtained, and the target data includes the basic information of the target object, the operation information, the target event triggered by the target object based on the operation information on the preset business platform, and the reference resource data used to trigger the target event. The basic information, operation information, target event and reference resource data are combined and processed to obtain a target data block. An image is generated based on the flow status of the target event and multiple target data blocks to obtain an event storm image. The basic information, operation information, target event and reference resource data of the target object are visualized through the event storm image, which can intuitively display the same links and different links of different business modules on the business platform. Further, the target event is subjected to vocabulary extraction based on the event storm image to obtain the target vocabulary of the target event, and the target vocabulary is clustered according to the preset clustering conditions to obtain multiple vocabulary sets, wherein the vocabulary sets include cluster keywords, and the target event can be subjected to vocabulary extraction based on the event storm image, and the target vocabulary can be quickly identified and clustered to obtain a reusable and abstract business module in the business platform. Finally, based on each clustered keyword, multiple business modules in the business platform are combined to form a target service unit. The target service unit processes the target resource data to be processed based on the acquired control command, generating the data processing result for the target resource data. By combining business modules to form a target service unit, resource data can be efficiently managed based on the target service unit, accelerating the flow of resource data.

[0073] In step S110 of some embodiments, target data of the target object is obtained. The target object may be a staff member related to the insurance business. The target data includes basic information of the staff member, operation information generated based on the staff member's operation command, target event triggered by the insurance business platform executing the operation command, and reference resource data used to trigger the target event. The basic information is used to characterize the identity of the staff member, such as the user account registered by the staff member on the insurance business platform, etc. The operation information is used to characterize the operations performed by the staff member on the business platform, and the reference resource data is the external resources required to trigger the target event, such as software tools, text materials, etc.

[0074] In step S120 of some embodiments, since there may be multiple target objects being operated on the insurance business platform, multiple types of operation information for the same target object, and multiple target events triggered by the operation information, the target data may become complex and large in volume. To facilitate data sorting, the basic information, operation information, target event, and reference resource data are combined and processed in a specific order to obtain a target data block. For example, the basic information, operation information, target event, and reference resource data are combined in sequence according to the chronological order of data acquisition to obtain the target data block.

[0075] In step S130 of some embodiments, multiple target data blocks are arranged in chronological order according to the time sequence of target event triggering within a preset time range, and images are generated or drawn for these multiple target data blocks to obtain an event storm image. The content of the event storm image may be a flowchart, a tree diagram, a module diagram, etc. Based on the event storm image, basic information of different target objects, operation information, target events triggered based on the operation information, and reference resource data used to trigger the target events can be visualized, thereby facilitating the rapid identification of reusable and abstractable business function modules in business lines such as order placement and staffing in the business platform based on the target data, thereby avoiding duplication of business function modules in different business lines.

[0076] In step S140 of some embodiments, the target event is response information generated based on the operation information, and the response information is composed of nouns and verbs. For example, if the operation information is the addition of information A, then the response information generated based on the operation information is that information A has been added, wherein information A is a noun and has been added as a verb. According to the event storm image, the target event is subjected to vocabulary extraction to obtain the target vocabulary of the target event. The target vocabulary is the noun in the target event. The target vocabulary is equivalent to the key entity in the business process. The key entity is the business module in the business line process. The clarification of the key entity can effectively identify the relevance and isolation between the key entities, thereby achieving high cohesion within the same bounded context and low coupling between different contexts. Building microservices according to the bounded context can ensure high cohesion within the microservice and low coupling between microservices.

[0077] In step S150 of some embodiments, there may be different associations between target words, for example, the first target word and the second target word may be derived from the same target object, or from different target events, and the associations may include strong associations and weak associations. A strong association indicates that the similarity between the first target word and the second target word is greater than a preset threshold, and a weak association indicates that the similarity between the first target word and the second target word is less than or equal to the threshold. The target words are clustered according to preset clustering conditions to distinguish multiple target words, grouping target words with strong associations into the same vocabulary set and grouping target words with weak associations into different vocabulary sets, thereby obtaining multiple vocabulary sets.

[0078] Specifically, the number of cluster keywords is determined, and the number of cluster keywords is recorded as N, where N is an integer greater than 0. The N target words are selected from multiple target words as cluster keywords, and the similarity between the target words and the cluster keywords is calculated. If the similarity is greater than a preset threshold, the target word is divided into the vocabulary set to which the cluster keyword belongs.

[0079] In step S160 of some embodiments, a target service unit is constructed based on a domain-driven design pattern. A vocabulary set is equivalent to a bounded context in the domain-driven design pattern. Business function modules associated with clustering keywords are obtained from the business platform based on clustering keywords. Multiple business function modules are combined and processed to obtain a target service unit. The target service unit is deployed as a microservice, and corresponding business data processing is performed on the target resource data to be processed based on the microservice. The target resource data can be business development resource data and recruitment resource data in the insurance industry, including basic information such as the name, age, contact information, and location of the business development target or the recruitment target. The business development target is the object to which insurance services are provided, and the recruitment target is the developable object that can provide insurance services.

[0080] It should be noted that if the logic of a single bounded context is relatively simple and has an association with other simple contexts, in order to avoid the waste of computing resources and storage resources caused by deploying multiple microservices, the target service units corresponding to multiple bounded contexts can be merged into one microservice.

[0081] See also Figure 2 In some embodiments, step S110 may include but is not limited to steps S210 to S230:

[0082] Step S210: acquiring initial resource data from a preset data acquisition source, and acquiring reference resource data from a preset storage path;

[0083] Step S220: annotate the initial resource data according to a preset marking method to obtain intermediate resource data; wherein the intermediate resource data includes a first coding field and a second coding field, the first coding field is used to represent the identity of the data acquisition source, and the second coding field is used to represent the identity of the intermediate resource data;

[0084] Step S230: Process the intermediate resource data based on the reference resource data and the operation information to obtain the target event.

[0085] In some embodiments, in step S210, initial resource data is obtained from different data acquisition sources based on the type of resource data, and the initial resource data is stored in a data center. The initial resource data, i.e., initial clues, only includes basic information about the business development target or the recruitment target. Specifically, when the type of the resource data to be obtained is business development resource data, the business development resource data is obtained from the business development data acquisition source; when the type of the resource data to be obtained is recruitment resource data, the recruitment resource data is obtained from the recruitment data acquisition source.

[0086] In step S220 of some embodiments, because the initial resource data only contains some basic information and cannot be effectively managed, additional information needs to be created for the initial resource data to facilitate its management and shorten the data flow from the data center to the insurance agent. The initial resource data is annotated according to a preset tagging method, and a first coding field and a second coding field are added to the initial resource data to obtain intermediate resource data. The first coding field is used to represent the data acquisition source ID, which can be the name or account number of the data provider, etc. The second coding field is used to represent the intermediate resource data ID, which can be the resource data number in the data center or the account number of the object corresponding to the resource data in the data acquisition source, etc. It is understood that based on the first coding field, intermediate resource data belonging to the same data acquisition source can be filtered out, and based on the second coding field, the status and attributes of the intermediate resource data can be tracked, where the status includes data pending, issued, expired, etc., and the attributes include data details, data expiration time, etc.

[0087] In step S230 of some embodiments, the insurance business platform executes the operation command, processes the intermediate resource data according to the reference resource data, and obtains the target event.

[0088] The above steps S210 to S230, by adding the first coding field and the second coding field, can filter out the resource data of a certain data acquisition source based on the first coding field, can track the status, attributes and other information of the resource data based on the second coding field, process the intermediate resource data according to the operation information and the reference resource data, obtain the target event, and extract the target vocabulary in the target event, which can improve the processing efficiency of the resource data.

[0089] See also Figure 3 In some embodiments, the target event includes a first event, the reference resource data includes an icebreaker tool, and step S230 may include but is not limited to steps S310 to S320:

[0090] Step S310, obtaining the current time of the service platform;

[0091] Step S320: If the current time is greater than the preset time threshold, or the amount of intermediate resource data is greater than the preset amount threshold, all intermediate resource data are processed for information creation based on the icebreaker tool and the pre-acquired create batch command to obtain the first event.

[0092] In some embodiments, in steps S310 to S320, since the intermediate resource data within a cycle forms a batch, the intermediate resource data also includes batch information. The current time recorded by the insurance business platform is obtained. If the current time is greater than a time threshold (the time threshold is the sum of the time of the previous batch and the time period), it indicates that a batch of intermediate resource data has been acquired from the data acquisition source. An icebreaker tool is then used to establish a connection between the data acquisition source and the insurance business platform. The reference batch information is verified. If the verification result is positive, a create batch command is executed to create information on the intermediate resource data based on the reference batch information, resulting in a first event. The first event includes information that the intermediate resource data has been created. If the verification result is negative, the reference resource data is reacquired. Using the create batch command to create information on the intermediate resource data, batch information such as the batch number and batch status is generated for the intermediate resource data. This allows for the integration of resource data in batches, improving data processing efficiency compared to processing individual resource data sequentially. It should be noted that the icebreaker tool can be downloaded from a pre-set icebreaker tool address.

[0093] See also Figure 4 In some embodiments, the target event includes at least one of the following events: a first event, a second event, a third event, and a fourth event; the operation information includes at least one of a resource recovery command, a resource delivery command, and a resource cleanup command; and step S230 may include, but is not limited to, at least one of steps S410 to S430:

[0094] Step S410: Reclaiming the intermediate resource data based on the reference resource data and the resource reclaim command to obtain a second event;

[0095] Step S420: performing distribution processing on the intermediate resource data based on the reference resource data and the resource distribution command, and distributing the intermediate resource data to a preset target location, thereby obtaining a third event;

[0096] Step S430 : performing data cleaning on the intermediate resource data based on the reference resource data and the resource cleaning command to obtain a fourth event.

[0097] In some embodiments, in step S410, the reference resource data may be the ID of the intermediate resource data to be recycled. Specifically, the ID of the intermediate resource data to be recycled is recorded in a statistical table, such as an Excel template table. Based on the resource recycling command and the data in the statistical table, the intermediate resource data is recycled to obtain a second event, which includes information indicating that the intermediate resource data has been recycled. The reference resource data may also be the batch number of the intermediate resource data to be recycled. Based on the resource recycling command and multiple batch numbers, the intermediate resource data is processed to recycle the intermediate resource data corresponding to the batch number, thereby obtaining the second event.

[0098] In some embodiments, in step S420, the reference resource data may be the ID or batch number of the intermediate resource data to be distributed. The intermediate resource data is distributed based on the ID or batch number and distributed to a target location, resulting in a third event. The target location may be an insurance agent's account, a terminal device, or an application, completing data connection. The third event includes the fact that the intermediate resource data has been distributed to the target location. Specifically, the insurance agent's preference data is obtained, which may be the type of resource data the insurance agent is skilled at processing. The ID of the intermediate resource data to be distributed is obtained based on the preference data. A resource distribution command is executed, and the intermediate resource data corresponding to the ID is distributed to the insurance agent's account. This optimizes the matching of the intermediate resource data with the insurance agent and facilitates the flow of intermediate resource data. When the intermediate resource data is distributed to the insurance agent, the insurance agent can accept or reject the resource. Data on the insurance agent's acceptance and rejection of resources is obtained and analyzed to determine the insurance agent's preferred resource data type. It should be noted that when the target location is an application or terminal device, the intermediate resource data can be distributed to the application in the form of an interface table. It should be further explained that resource data that has been recycled once or multiple times can be re-issued.

[0099] In step S430 of some embodiments, the reference resource data may be the ID or batch number of the intermediate resource data to be cleaned, and the intermediate resource data is cleaned according to the ID number or batch number, sensitive words in the intermediate resource data are filtered, repeated words in the intermediate resource data are removed, and unreasonable intermediate resource data are removed to obtain a fourth event, which includes information that the intermediate resource data has been cleaned.

[0100] Specifically, a data acquisition source identifier is obtained based on the ID number or batch number of the intermediate resource data, and a pre-set sensitive word judgment rule, repeated word judgment rule, and rationality judgment rule are obtained based on the data acquisition source identifier. The sensitive data in the intermediate resource data is obtained based on the sensitive word judgment rule, the repeated data in the intermediate resource data is obtained based on the repeated word judgment rule, and whether the intermediate resource data is abnormal data is determined based on the rationality judgment rule. Sensitive data, repeated data, and abnormal intermediate resource data are removed from the intermediate resource data to perform data cleaning on the intermediate resource data and distinguish resource data that can be sent. Among them, resource data that can be sent includes resource data that can be sent but some fields are incomplete and resource data that can be sent and all fields are complete. Data verification is performed on resource data that can be sent but some fields are incomplete. If the verification result is failed, it means that the resource data is abnormal and will not be sent. The resource data is recycled. If the verification result is passed, it means that the resource data can be sent.

[0101] It should be noted that the rationality judgment is based on whether the intermediate resource data is within the preset validity period. If it is not within the validity period, the intermediate resource data is considered abnormal data. It should be further explained that the sensitive word judgment rules, repeated word judgment rules, and rationality judgment rules can be dynamically configured.

[0102] The above steps S410 to S430 perform different processing such as recycling, distribution and data cleaning on the intermediate resource data based on the reference resource data and operation information, which can efficiently manage the intermediate resource data, enable the intermediate resource data to be accurately distributed to the insurance agents that match it, and promote the circulation of the intermediate resource data.

[0103] See also Figure 5 In some embodiments, step S420 may also include but is not limited to steps S510 to S520:

[0104] Step S510, obtaining a preset target time;

[0105] Step S520 , performing distribution processing on the target resource data according to the target time, the reference resource data and the resource distribution command, and distributing the target resource data to the target location at the target time, thereby obtaining a third event.

[0106] In steps S510 to S520 of some embodiments, a pre-configured target time is obtained, which can be a time point or a time period. The intermediate resource data is processed according to the ID or batch number, and the intermediate resource data is sent to the target location at the target time, and a third event is obtained. The specific time when the intermediate resource data is sent can be flexibly controlled. The third event includes information that the intermediate resource data has been sent to the target location at the target time.

[0107] See also Figure 6 In some embodiments, step S150 includes but is not limited to steps S610 to S620:

[0108] Step S610, obtaining the business type of the target event;

[0109] Step S620: clustering the target vocabulary according to the basic information or the business type to obtain a vocabulary set.

[0110] In step S610 of some embodiments, the clustering condition adopted is the basic information of the target object or the business type of the target event, and the basic information of the target object that triggers the target event and the business type of the target event are obtained from the database of the business platform, where the basic information includes the user ID, which can be the account and identity code registered by the user on the business platform, and the business type includes business development business and staffing business.

[0111] In step S620 of some embodiments, the target vocabulary is clustered according to the basic information or the business type to distinguish multiple target vocabulary and obtain a vocabulary set. Specifically, the target vocabulary is clustered according to the basic information, the target vocabulary derived from the same basic information is clustered into the same vocabulary set, and cluster keywords are generated based on the target vocabulary. Alternatively, the target vocabulary is clustered according to the business type, the target vocabulary belonging to the same business type is clustered into the same vocabulary set, and cluster keywords are generated based on the target vocabulary.

[0112] In the above steps S610 to S620, since there is an association relationship between target words, in order to determine the relationship between multiple target words, the target words are clustered according to basic information or business type, so that target words with strong association relationships are divided into the same vocabulary set, and target words with no association relationships or weak association relationships are divided into different vocabulary sets, thereby obtaining multiple vocabulary sets, which can be used to develop and deploy microservices based on the vocabulary sets, and realize the processing of resource data based on microservices.

[0113] See also Figure 7 In some embodiments, step S620 may include, but is not limited to, at least one of steps S710 to S720:

[0114] Step S710: clustering the target words according to the basic information, clustering the target words from the same basic information into the same vocabulary set, and generating cluster keywords based on the target words;

[0115] Step S720 , clustering the target words according to the business type, clustering the target words belonging to the same business type into the same word set, and generating cluster keywords based on the target words.

[0116] In step S710 of some embodiments, in order to distinguish target vocabulary, target vocabulary derived from the same basic information is clustered into the same vocabulary set. Specifically, a target event is determined based on the target vocabulary, and basic information corresponding to the triggering target event is determined based on the target event. This basic information is used as reference basic information, i.e., clustering keywords. The similarity between the basic information and the reference basic information is calculated. If the similarity is greater than or equal to a preset threshold, the target vocabulary is clustered into the same vocabulary set. If the similarity is less than the threshold, the target vocabulary is clustered into another vocabulary set.

[0117] In step S720 of some embodiments, in order to distinguish target vocabulary, target vocabulary from the same business type is clustered into the same vocabulary set. Specifically, a target event is determined based on the target vocabulary, a first coding field of the intermediate resource data is determined based on the target event, and a reference business type is determined based on the first coding field. The similarity between the business type and the reference business type is calculated. If the similarity is greater than a preset threshold, the target vocabulary is clustered into the same vocabulary set. If the similarity is less than the threshold, the target vocabulary is clustered into another vocabulary set.

[0118] In the above steps S710 to S720, in order to distinguish the target vocabulary, the target vocabulary derived from the same basic information is clustered into the same vocabulary set, or the target vocabulary derived from the same business type is clustered into the same vocabulary set. Different clustering conditions are used for clustering to obtain different vocabulary sets. Different target service units are obtained according to the vocabulary sets, so that the target service unit can be applied to a variety of service scenarios, thereby expanding the scope of application of the target service unit.

[0119] See also Figure 8 , an embodiment of the present application further provides a data processing device that can implement the above-mentioned data processing method, the device comprising:

[0120] Acquisition module 810 is used to acquire target data of a target object; wherein the target data includes basic information of the target object, operation information, target events triggered by the target object based on the operation information on a preset business platform, and reference resource data used to trigger the target event;

[0121] The first combining module 820 is used to combine the basic information, operation information, target event and reference resource data to obtain a target data block;

[0122] An image generation module 830 is configured to generate an image based on the flow state of the target event and multiple target data blocks to obtain an event storm image;

[0123] A vocabulary extraction module 840 is used to extract vocabulary from the target event according to the event storm image to obtain a target vocabulary for the target event;

[0124] Clustering module 850, configured to cluster target words according to preset clustering conditions to obtain multiple word sets; wherein the word sets include cluster keywords;

[0125] The second combination module 860 is used to perform module combination processing on multiple business modules in the business platform according to each clustering keyword to obtain a target service unit; wherein the target service unit is used to perform data processing on the target resource data to be processed according to the acquired control command to obtain a data processing result of the target resource data.

[0126] The specific implementation of the data processing device is basically the same as the specific embodiment of the above-mentioned data processing method, and will not be repeated here.

[0127] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned data processing method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0128] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0129] The processor 910 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0130] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 920 and is called by the processor 910 to execute the data processing method of the embodiments of this application;

[0131] Input / output interface 930, used to implement information input and output;

[0132] Communication interface 940, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0133] bus 950 , which transmits information between various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0134] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0135] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned data processing method is implemented.

[0136] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor 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.

[0137] The data processing method, data processing device, electronic device and computer-readable storage medium provided in the embodiment of the present application obtain target data of a target object, wherein the target data includes basic information of the target object, operation information, target events triggered by the target object based on the operation information on a preset business platform, and reference resource data for triggering the target event, and performs data combination processing on the basic information, operation information, target event and reference resource data to obtain a target data block, generates an image based on the flow state of the target event and multiple target data blocks to obtain an event storm image, and visualizes the basic information, operation information, target event and reference resource data of the target object through the event storm image, which can intuitively display the same links and different links of different business modules on the business platform. Further, according to the event storm image, vocabulary extraction is performed on the target event to obtain the target vocabulary of the target event, and the target vocabulary is clustered according to the preset clustering conditions to obtain multiple vocabulary sets, wherein the vocabulary sets include cluster keywords, and the target event can be extracted based on the event storm image, and the target vocabulary can be quickly identified and clustered to obtain a reusable and abstract business module in the business platform. Finally, based on each clustered keyword, multiple business modules in the business platform are combined to form a target service unit. The target service unit processes the target resource data to be processed based on the acquired control command, generating the data processing result for the target resource data. By combining business modules to form a target service unit, resource data can be efficiently managed based on the target service unit, accelerating the flow of resource data.

[0138] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0139] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0140] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0141] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0142] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0143] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0144] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0145] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0147] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0148] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire target data of a target object; wherein the target data includes basic information of the target object, operation information, a target event triggered by the target object based on the operation information on a preset business platform, and reference resource data used to trigger the target event; Performing data combination processing on the basic information, the operation information, the target event, and the reference resource data to obtain a target data block; Generate an image based on the flow state of the target event and the plurality of target data blocks to obtain an event storm image; Performing vocabulary extraction on the target event according to the event storm image to obtain a target vocabulary for the target event; Clustering the target vocabulary according to a preset clustering condition to obtain a plurality of vocabulary sets; wherein the vocabulary sets include clustering keywords; According to each of the clustering keywords, module combination processing is performed on multiple business modules in the business platform to obtain a target service unit; wherein, the target service unit is used to perform data processing on the target resource data to be processed according to the acquired control command to obtain the data processing result of the target resource data.

2. The data processing method according to claim 1, wherein: The acquiring target data of the target object includes: Acquire initial resource data from a preset data acquisition source, and acquire the reference resource data from a preset storage path; The initial resource data is annotated according to a preset marking method to obtain intermediate resource data; wherein the intermediate resource data includes a first coding field and a second coding field, the first coding field is used to represent the identity of the data acquisition source, and the second coding field is used to represent the identity of the intermediate resource data; The intermediate resource data is processed based on the reference resource data and the operation information to obtain the target event.

3. The data processing method according to claim 2, characterized in that: The target event includes a first event, the reference resource data includes an icebreaker tool, and the processing of the intermediate resource data based on the reference resource data and the operation information to obtain the target event includes: Obtaining the current time of the service platform; If the current time is greater than a preset time threshold, or the amount of the intermediate resource data is greater than a preset amount threshold, information creation processing is performed on all the intermediate resource data based on the icebreaking tool and the pre-acquired creation batch command to obtain the first event.

4. The data processing method according to claim 2, wherein: The target event includes at least one of the following events: a first event, a second event, a third event, and a fourth event; the operation information includes at least one of a resource recovery command, a resource delivery command, and a resource cleaning command; and the processing of the intermediate resource data based on the reference resource data and the operation information to obtain the target event further includes at least one of the following steps: Recycling the intermediate resource data based on the reference resource data and the resource recycling command to obtain the second event; performing distribution processing on the intermediate resource data based on the reference resource data and the resource distribution command, and distributing the intermediate resource data to a preset target location to obtain the third event; The intermediate resource data is cleansed based on the reference resource data and the resource cleansing command to obtain the fourth event.

5. The data processing method according to claim 4, characterized in that: The performing the distribution processing on the intermediate resource data based on the reference resource data and the resource distribution command, and distributing the intermediate resource data to a preset target location to obtain the third event, includes: Get the preset target time; The target resource data is issued according to the target time, the reference resource data and the resource issuance command, and the target resource data is issued to the target location at the target time to obtain the third event.

6. The data processing method according to any one of claims 1 to 5, characterized in that: The target vocabulary is clustered according to the preset clustering conditions to obtain multiple vocabulary sets, including: Obtaining the business type of the target event; The target vocabulary is clustered according to the basic information or the business type to obtain the vocabulary set.

7. The data processing method according to claim 6, characterized in that: The clustering of the target vocabulary according to the basic information or the business type to obtain the vocabulary set includes: performing clustering processing on the target words according to the basic information, clustering the target words from the same basic information into the same vocabulary set, and generating the cluster keywords according to the target words; or, The target words are clustered according to the business type, the target words belonging to the same business type are clustered into the same word set, and the cluster keywords are generated according to the target words.

8. A data processing device, characterized in that The device comprises: An acquisition module, configured to acquire target data of a target object; wherein the target data includes basic information of the target object, operation information, a target event triggered by the target object based on the operation information on a preset business platform, and reference resource data used to trigger the target event; A first combining module is configured to perform data combining processing on the basic information, the operation information, the target event, and the reference resource data to obtain a target data block; An image generation module, configured to generate an image based on the flow state of the target event and a plurality of the target data blocks to obtain an event storm image; A vocabulary extraction module, configured to extract vocabulary from the target event according to the event storm image to obtain a target vocabulary for the target event; A clustering module, configured to cluster the target vocabulary according to a preset clustering condition to obtain a plurality of vocabulary sets; wherein the vocabulary sets include clustering keywords; The second combination module is used to perform module combination processing on multiple business modules in the business platform according to each of the clustering keywords to obtain a target service unit; wherein, the target service unit is used to perform data processing on the target resource data to be processed according to the acquired control command to obtain the data processing result of the target resource data.

9. An electronic device, characterized in that The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the data processing method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data processing method according to any one of claims 1 to 7 is implemented.

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