CN116467262A - Metadata capability-based client liveness analysis method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the invention provides a method, a device, equipment and a medium for analyzing client liveness based on metadata capability, wherein the method acquires behavior data according to a timing task and generates an index file; the timing task is a task for acquiring behavior data of a corresponding client in a preset period at a set time point; extracting and processing the index file, and determining service data of each client in a preset time period of the requirement; counting the business data increment and the memory quantity of each customer in a database in a preset time period, and determining the key business of each customer and the key business increment of each customer in the preset time period; grouping according to the service modules, and marking and binding the service modules and the key service through ApiName fields of the service objects of all clients; and determining the liveness of each client through a client liveness scoring model set on the basis of the PaaS platform. The invention timely positions the inactive clients to check, pay attention to and activate by determining and analyzing whether the activities of the clients are active or not.
Description
Technical Field
The present invention relates to the field of data analysis technologies, and in particular, to a metadata capability-based client liveness analysis method, apparatus, device, and medium.
Background
Under the enterprise management of multiple tenants, a service provider needs to know the activity degree of each tenant, which functional module is active, and which user role is active, so that the product is better adjusted and iterated, and high-quality service is provided for the client, and the client success rate, the client charge rate, the client renewal rate and the like are improved. The service provider can master the first-hand data, and meanwhile, provide the tenant with the data analysis capability for inquiring the activity of the enterprise user, help the customer to know the progress of each work, master the working condition of each person, and provide a certain data support for the efficient management enterprise. Thus, techniques for liveness monitoring of data burial points have evolved.
In the prior art, a lot of enterprise data cannot control and lack systematic management of customer activity data, so that a provider has the problem of controlling islanding on own customers, or the problem of high investment and development cost of machine language and Phython is needed to be solved by monitoring and analyzing the customer activity data through the technical scheme of the machine language and Phython.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a method, a device, equipment and a medium for analyzing the activity of a client based on metadata capability, so as to solve the technical problem of high monitoring and analysis investment and development cost for the client activity data in the related art.
One or more embodiments of the present specification provide a metadata-capability-based client liveness analysis method, including:
storing behavior data of each client in a preset log embedded point mode;
acquiring behavior data according to the timing task and generating an index file; the timing task is a task for acquiring behavior data of a corresponding client in a first preset period at a set time point;
extracting and processing the index file, and determining service data of each client in a first preset time period of demand, wherein the service data comprises active clients, active client details, active contacts and active contact detail data;
counting the increment of the business data of each customer and the memory capacity in a database in a second preset period, determining the key business of each customer according to the preset key business, and counting the increment of the key business of each customer in the second preset period;
The method comprises the steps of performing service closed loop marking processing on service objects under clients, grouping the service objects under the clients through ApiName and label fields of the service objects to form a plurality of service modules, and performing marking binding on the service modules and the key services through ApiName and label fields of each client service object;
and determining the activity degree of each client through a client activity degree scoring model set based on the PaaS platform based on the service data of each client in the first preset period and the key service increment in the second preset period.
One or more embodiments of the present specification provide a metadata-based capability customer liveness analysis apparatus, including:
and a data acquisition module: the method comprises the steps of obtaining behavior data according to a timing task and generating an index file, wherein the behavior data are stored behavior data of each client in a preset log embedded point mode; the timing task is a task for acquiring behavior data of a corresponding client in a first preset period at a set time point;
the data collection and processing module is used for: the method comprises the steps of extracting and processing an index file, and determining service data of each client in a first preset time period of demand, wherein the service data comprise active clients, active client details, active contacts and active contact detail data;
The key business determining module to which the customer belongs: the method comprises the steps of counting the increment of business data of each customer and the storage amount in a database in a second preset period, determining the key business of each customer according to the preset key business, and counting the increment of the key business of each customer in the second preset period;
binding a marking module: the method comprises the steps of performing service closed loop marking processing on service objects under clients, grouping the service objects under the clients through ApiName and label fields of the service objects to form a plurality of service modules, and performing marking binding on the service modules and the key services through ApiName and label fields of each client service object;
and a model setting module: a customer liveness scoring model configured for use with PaaS-based platforms;
the calculation module: and the activity degree of each client is calculated and determined through the client activity degree scoring model according to the business data of each client in the first preset period and the key business increment which belongs to in the second preset period.
One or more embodiments of the present specification provide a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing a metadata capability-based client activity analysis method as described above when executing the computer program.
One or more embodiments of the present specification provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of a metadata-based capability customer activity analysis method as described above.
The invention utilizes the index file to extract and process and determines the business data of each customer required by the PaaS by means of the business object capability, carries out statistics and classification by the business object to obtain the data of object statistics and key business increment statistics, finally combines the access result of the business object in a certain period by the internal scoring model application to determine and analyze whether the customer activity is active or not, the key function data use trend of the system, and timely position the inactive customer to count, pay attention to and activate, thereby effectively solving the problem that the service provider has management and control island for the customer.
Drawings
For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description that follow are only some of the embodiments described in the description, from which, for a person skilled in the art, other drawings can be obtained without inventive faculty.
FIG. 1 is a flow diagram of a method for metadata-based capability customer liveness analysis in accordance with one or more embodiments of the present disclosure;
FIG. 2 is a schematic diagram of a relationship between a business object and a timing task in a metadata-based capability customer activity analysis method according to one or more embodiments of the present disclosure;
FIG. 3 is a schematic diagram of a hierarchical setup page between a client application depth level and a business module based on a metadata capability client liveness analysis method according to one or more embodiments of the present disclosure;
FIG. 4 is a schematic diagram of a scoring model setup interface based on a metadata capability customer liveness analysis method according to one or more embodiments of the present disclosure;
FIG. 5 is a schematic diagram of a decision-making layer scoring configuration interface based on a metadata capability customer liveness analysis method according to one or more embodiments of the present disclosure;
FIG. 6 is a schematic block diagram illustrating a method for analyzing and calculating customer activity based on metadata capability customer activity according to one or more embodiments of the present disclosure;
FIG. 7 is a schematic diagram of a metadata-based capability customer activity analysis apparatus according to one or more embodiments of the present disclosure;
Fig. 8 is a schematic structural diagram of a computer device according to one or more embodiments of the present disclosure.
Detailed Description
In order to enable a person skilled in the art to better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the present disclosure.
Noun interpretation:
ELK: consists of ElasticSearch, logstash and Kiabana three open source tools;
the elastiscearch is an open source distributed search engine, which is characterized by: distributed, zero configuration, auto discovery, index auto-sharding, index copy mechanism, restful style interface, multiple data sources, auto-search load, etc.
Logstack is a completely open source tool that can collect, filter, and store logs for later use (e.g., searching).
Kibana is also an open source and free tool that can analyze friendly Web interfaces for logs provided by Logstash and elastic search, and can aggregate, analyze and search important data logs.
Metadata (Metadata), also called intermediate data and relay data, is data (data about data) describing data, mainly describing data attribute (property) information, and is used to support functions such as indicating storage location, history data, resource searching, file recording, and the like. Metadata is an electronic catalog, and in order to achieve the purpose of cataloging, the contents or characteristics of data must be described and collected, so as to achieve the purpose of assisting in data retrieval.
The invention is described in detail below with reference to the detailed description and the accompanying drawings.
Method embodiment
According to an embodiment of the present invention, as shown in fig. 1, a metadata capability-based client activity analysis method is provided, and according to an embodiment of the present invention, a metadata capability-based client activity analysis method includes:
step S101, storing behavior data of each client through a preset log embedded point mode.
In some embodiments, the implementation process of step S101 includes:
defining a log section in a service, outputting a log printed by a designated log into a designated file controller log by using a log back in a log embedding mode, for example, a page request log file and a user behavior log file, acquiring data from the controller log by using a log stack service, and generating a ES (ElasticSearch) index file according to a data format in a configuration file of the log stack.
Step S102, behavior data are obtained according to a timing task, and an index file is generated; the timing task is a task for acquiring behavior data of a corresponding client in a first preset period at a set time point; the corresponding clients are clients under certain conditions in the database, such as formal clients, clients using a product type as a certain product, and the like.
In some embodiments, according to the setting of the timing task, the ES index file generated in the first preset period is acquired at a preset time point, for example, the service requirement is behavior data of recording one day of the client, so that the first preset period is in units of days, for example, the requirement of real-time statistics on the client dynamics is involved, and the timing task (preset statistics time) can be designed as a task with an even shorter time interval per hour.
In a specific embodiment, assuming that the first preset period is in units of days, acquiring an ES index file newly created every day from the demand scene file, and generating an index file according to the acquisition of the ES index file, recording all information related to the request through the index file, for example, including but not limited to, request time, user information of a requester, request URL information, request header information, request parameter information, requested service name, and the like, where the index file may be stored by naming a log-all-2023.03.01.
Step S103, extracting and processing an index file, and determining Service data of each client in a first preset period of time required by the Service object capability of PaaS (Platform-as-a-Service), wherein the Service data comprises active clients, active client details, active contacts and active contact detail data; in this embodiment, metadata definition and storage may be performed on service data in an interfacing manner; the PaaS metadata is used as a data carrier for displaying, so that the problem that a service provider can manage and control islanding for own clients is solved; and simultaneously, various objects representing enterprise activities are created by combining flexible metadata capability of PaaS.
In this embodiment, taking a daily active client detail as an example, how the client accesses information daily is extracted, processed and analyzed, for example, 1 point 20 minutes daily is executed by a timing task, and the index file log stack-all-2023.03.01 data is queried according to different query conditions.
The processing of data based on the index file may include, for example, the following scenarios:
1. core/auxiliary object: inquiring a service object list of a client, inquiring all request data in a logstack-all-2023.03.01 according to a request URL set of the service object, and counting the function access amount of the module;
2. report, business flow, management summary, approval flow and other processes: inquiring a data list of a client, such as a report, a business flow, a management summary list, an approval flow and the like, combining request URL (uniform resource locator) splice inquiry conditions of different applications, acquiring access data of a corresponding module, and according to fields such as dataID, a module ApiName (input table function) and the like, and the related field information of the report, such as a core report and the like;
3. fixed application modules (secondary business analysis, cash flow analysis, etc.): and acquiring corresponding access data in a similar way, and storing and displaying the data.
In an embodiment, the active client details, the active contacts, and the active contact detail data are specifically:
Active clients: extracting a request record of a client from the index file and performing data processing (namely, performing normalization, enrichment, circulation, desensitization and filtering on the obtained log data) to obtain specific detailed data contents, such as fields of total access amount, core function access amount, auxiliary function access amount, management behavior access amount, activation user number, total user number, active user number, average access amount and the like in a first preset period (for example, daily) of each client; because a plurality of user data are arranged below the clients, each client can be understood to be an enterprise, and a plurality of staff are arranged below each enterprise, the active client data are composed of a plurality of pieces of user data;
active customer details: the method comprises the steps of obtaining information of an access service object in a request parameter from an index file, and then processing data, wherein the information comprises fields such as a module name, an actual report name, a core report, a module API, a function access amount, a data type, an object type, a week active user proportion and the like;
active contacts: the active data of each affiliated contact person under each client in a first preset period (for example, daily) comprises fields of total access quantity, core function access quantity, auxiliary function access quantity, management behavior access quantity, telephone, mobile phone and the like;
Active contact details: the data description specifically accessed by each contact is expressed as follows: module name, actual report name, core report, module API, function access, data type, object type, service role, etc.; referring to fig. 2, a definition of a business object, a basic description and a responsible functional schematic are provided in this embodiment.
Step S104, counting the business data increment and the memory space of each customer in the database in a second preset period, determining the key business of each customer according to the preset key business, and determining and counting the key business increment of each customer in the second preset period.
In one embodiment, the step S104 is performed with the second preset time period as one week, wherein,
subject statistics (weeks): carrying out business data increment statistics on clients every week (such as friday), pulling increment and storage data from business object data of each client, and determining the key business of each client for the key business under a preset object statistics table; in a specific embodiment, the object statistics is a statistics table, the key service and the service object APIName, label (tag) are fields in the object statistics (week) table, and the value of the field is derived from the service closed loop label (in this embodiment, the service closed loop label belongs to a configuration table), that is, the key service can be obtained by inputting ApiName, label.
And (3) counting key business increment: and (5) acquiring service increment in the key service of each item from the object statistics (week) every week to obtain the increment value of the key service of each item.
Step S105, service closed loop marking processing is carried out on the service objects under the clients, so that the service objects under the clients are grouped into a plurality of service modules through fields such as ApiName and label of the service objects, and the service modules are marked and bound with the key service through the ApiName and label fields of the service objects of the clients. It can be understood that the key service and the object ApiName, label are fields on the object statistics-circumference, and the association relationship between the key service and the object ApiName, label is bound by the division rule in the service closed-loop mark.
In some embodiments, the business closed loop label implementation is as follows:
according to fields such as ApiName, label of the service objects, the service objects under the clients are grouped into a plurality of service modules, for example, as shown in fig. 3, a schematic diagram of a hierarchical setting module formed by the client grouping result and the service modules is provided in this embodiment; the grouping of business objects under clients includes management application, cost management, project payment, project billing, project settlement, expenditure contract, project collection, income contract, material management, mechanical management, labor management, sales management and other business modules, and the management application comprises business objects such as approval flow, business flow, workflow and the like.
Binding the key business belonging to the object statistics (week) with the corresponding business module according to the fields of the business object ApiName, label and the like and the business closed loop marking method, realizing the key business increment statistics, the business closed loop marking and the key business related to the object statistics, and judging whether the increment data exists in the key business belonging to the corresponding connection of the business module.
In this embodiment, the client classification is preferably further determined by a combination of setting depth values in the client classification configuration, so as to determine the application depth of the client to each functional module of the system, thereby determining the maximum requirement of the client, and using the determined maximum requirement as a basis for adjusting the service of each client.
The application depth scale value comprises a plurality of levels, such as A, B, B-1, B-2, B2+, B2, B21, B22, B1, B11 and the like, which are arranged from top to bottom, wherein each level correspondingly comprises at least one service module, and the corresponding service module of the mark is determined according to the incremental data of all the service objects contained under each service module; wherein, in one embodiment,
the relation among all levels is that the types of business objects contained in the level A are the most comprehensive, other levels such as the level B are all subsets of the level A, and the levels B-1, B-2 and the like are subsets of the level B; illustrating:
The A level includes flow management actions (including approval flow, business flow, workflow request), basic project information data, cost management related business data, complete business closed-loop data with up income (collection, income contract, project acceptance, billing), complete business closed-loop data with down expenditure (payment, expenditure contract, supplier settlement, receipt), etc.
The B level includes flow management actions (including approval flow, business flow, workflow request), basic project information data, complete business closed-loop data for up income (collection, income contract, project acceptance, billing), complete business closed-loop data for down expenditure (payment, expenditure contract, supplier settlement, receipt), etc.
The B2 level comprises flow management actions (including request of approval flow, business flow and workflow), basic project information data, complete business closed-loop data (project payment, payment contract, payment settlement and project ticket collection) for lower payment and the like.
And step S106, determining the activity degree of each client through a client activity degree scoring model set on the basis of the service data of each client in a first preset period and the key service increment in a second preset period.
In some embodiments, the first preset period and the second preset period are set according to statistical requirements, and in this embodiment, the first preset period may be a period set as several hours or a day according to requirements; the second preset period is set as a period of time in units of one week or one month.
In some embodiments, referring to fig. 4 to 5, an interface schematic diagram of a scoring model setup interface and an interface schematic diagram of a decision layer scoring configuration provided in this embodiment are provided, and calculating coefficients or weights in each sub-scoring model in a customer liveness scoring model are set by using the scoring model set by the PaaS platform, where the sub-scoring model includes an access score, a model score, a depth score, a decision layer score, an liveness score and a comprehensive vector score; calculating coefficients or weights, including access coefficients, depth coefficients, management weights, core weights, model coefficients, decision layer coefficients, comprehensive depth weights, and decision layer rating condition configuration;
referring to fig. 6, a schematic block diagram of a process for analyzing and calculating the activity of a client according to this embodiment is shown, where specific calculation of each sub-scoring model includes:
visit score = math.log10 (total visit/number of days to activate/number of users to activate) xvisit score coefficient;
The Math is Math function operation, and the Math function can be applied to solve part of computer program problems;
the sum of the access amount in the daily active client record with the total access amount of 30 days;
the activation days are the total number of daily active client records;
the number of the activated users is the number of the activated users of the client which is inquired from a database;
depth score = incremental value of each business module × depth score coefficient;
model division= (management weight management duty+ (1-management weight) + (core weight core duty+ (1-core weight) ×auxiliary duty));
decision layer score = (lg (total access score of boss role)) decision layer coefficient;
active score = model score + visit score + decision layer score;
complex vector component = sqrt (active component, component depth weight) + (depth component, component depth weight); sqrt is square root calculation;
wherein the management duty ratio, the core duty ratio and the auxiliary duty ratio are the respective proportions of the management access amount, the core access amount and the auxiliary access amount in the total access amount.
According to the method, index files are utilized to extract and process service data of all clients required by the users, the service object is used for counting and classifying the service data to obtain object counting and key service increment counting data, finally, the service object is used for determining and analyzing whether the client is active or not according to access results in a certain time period by combining with access results of the service object, key function data using trend of the system is determined, inactive clients are located in time to count, pay attention to and activate the service object, and the problem that service providers have management and control islanding on own clients is effectively solved.
The method is different from other high-input machine languages and Phython with higher development cost, and is applicable to the technology that a service manufacturer of certain Java languages for developing languages wants to discover modes and trends by analyzing a large amount of data through ELK capability, java language development platforms and one technical capability in PaaS environment.
Device embodiment
According to an embodiment of the present invention, as shown in fig. 7, a metadata capability-based client activity analysis device is provided, which is a schematic block diagram of a structure of the metadata capability-based client activity analysis device according to the present embodiment, where the metadata capability-based client activity analysis device according to the embodiment of the present invention includes:
and a data acquisition module: the method comprises the steps of obtaining behavior data according to a timing task and generating an index file, wherein the behavior data are stored behavior data of each client in a preset log embedded point mode; the timing task is a task for acquiring behavior data of a corresponding client in a first preset period at a set time point;
The data collection and processing module is used for: the method comprises the steps of extracting and processing an index file, and determining service data of each client in a first preset time period of demand, wherein the service data comprise active clients, active client details, active contacts and active contact detail data;
the key business determining module to which the customer belongs: the method comprises the steps of counting the business data increment and the memory quantity of each customer in a database in a second preset period, determining the key business of each customer according to the preset key business, and determining and counting the key business increment of each customer in the second preset period;
binding a marking module: the method comprises the steps of performing service closed loop marking processing on service objects under clients, grouping the service objects under the clients through ApiName and label fields of the service objects to form a plurality of service modules, and marking and binding the service modules with the key service through ApiName and label fields of each client service object;
and a model setting module: a customer liveness scoring model configured for use with PaaS-based platforms;
the calculation module: and the activity degree of each client is calculated and determined through the client activity degree scoring model according to the business data of each client in the first preset period and the key business increment which belongs to in the second preset period.
In some embodiments, the active client, active client details, active contacts, and active contact detail data are respectively:
active clients: the data collecting and processing module extracts a request record of a client from the index file and processes data obtained by data processing, wherein the data comprises data of total access quantity, core function access quantity, auxiliary function access quantity, management behavior access quantity, activation user quantity, total user quantity, active user quantity and average access quantity of each client in a first preset period;
active customer details: the data receiving and processing module acquires the information of the access service object in the request parameter from the index file and then processes the data to acquire module name, actual report name, core report, module API, function access amount, data type, object type and peri-active user proportion data of the active client;
active contacts: the data collection processing module acquires active data in a first preset period of each affiliated contact under each client, wherein the active data comprises total access quantity, core function access quantity, auxiliary function access quantity, management behavior access quantity and contact information data;
the data collection processing module obtains the data description specifically accessed by each contact, wherein the data description comprises the module name, the actual report name, the core report, the module API, the function access amount, the data type, the object type and the service role data of the active contact.
In some embodiments, the system further comprises a business module indexing module and a model coefficient or weight setting module, wherein,
the business module marking module: the client classification method comprises the steps of marking each client service module by applying a depth mark value to realize client classification;
model coefficient or weight setting module: the method comprises the steps that a calculation coefficient or weight in each sub-scoring model in a customer liveness scoring model is set through a scoring model set by the PaaS platform, wherein the sub-scoring model comprises an access score, a model score, a depth score, a decision layer score, an liveness score and a comprehensive vector score; calculating coefficients or weights comprises accessing coefficients, depth coefficients, management weights, core weights, model coefficients, decision layer coefficients, comprehensive depth weights, and decision layer rating condition configuration.
In some embodiments, the calculating of each sub-score model specifically includes:
visit score = math.log10 (total visit/number of days to activate/number of users to activate) xvisit score coefficient; the total access amount is the total access amount in the daily active client records in the preset days;
the activation days are the total number of daily active client records;
the number of the activated users is the number of the activated users of the client which is inquired from a database;
Depth score = incremental value of each business module × depth score coefficient;
model division= (management weight management duty+ (1-management weight) + (core weight core duty+ (1-core weight) ×auxiliary duty));
decision layer score = (lg (total access score of boss role)) decision layer coefficient;
active score = model score + visit score + decision layer score;
complex vector component = sqrt (active component, component depth weight) + (depth component, component depth weight);
wherein the management duty ratio, the core duty ratio and the auxiliary duty ratio are the respective proportions of the management access amount, the core access amount and the auxiliary access amount in the total access amount.
According to the device, the index file is utilized to extract and process the business data of each client required by the business object capability of PaaS, the business object is used for statistics and classification to obtain the data of object statistics and key business increment statistics, finally, the internal scoring model is used, the access result of the business object in a certain period of time is combined, whether the client activity is active or not is determined and the key function data using trend of the system is determined, the inactive client is positioned in time for checking, focusing and activating, and the problem that a service provider has management and control island for the client is effectively solved.
The embodiment of the present invention is a system embodiment corresponding to the above embodiment of the method, and specific operations of processing steps of each module may be understood by referring to descriptions of the method embodiment, which are not repeated herein.
As shown in fig. 8, the present invention further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements the metadata-based capability client activity analysis method in the above embodiment, or where the computer program when executed by the processor implements the metadata-based capability client activity analysis method in the above embodiment, and where the computer program when executed by the processor implements the following method steps:
step S101, storing behavior data of each client in a preset log embedded point mode;
step S102, behavior data are obtained according to a timing task, and an index file is generated; the timing task is a task for acquiring behavior data of a corresponding client in a first preset period at a set time point;
step S103, extracting and processing the index file, and determining service data of each client in a first preset time period of demand, wherein the service data comprise active clients, active client details, active contacts and active contact detail data;
Step S104, counting the business data increment and the memory quantity of each customer in the database in a second preset period, determining the key business of each customer according to the preset key business, and determining and counting the key business increment of each customer in the second preset period;
step S105, carrying out service closed loop marking processing on service objects under clients, grouping the service objects under the clients into a plurality of service modules through ApiName and label fields of the service objects, and marking and binding the service modules with the key service through ApiName and label fields of each client service object;
and step S106, determining the activity degree of each client through a client activity degree scoring model set on the basis of the service data of each client in a first preset period and the key service increment in a second preset period.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention and are not specifically described in the present specification and will be apparent to those skilled in the art from the scope of the present invention.
Claims (10)
1. The metadata capability-based client liveness analysis method is characterized by comprising the following steps of:
storing behavior data of each client in a preset log embedded point mode;
acquiring behavior data according to the timing task and generating an index file; the timing task is a task for acquiring behavior data of a corresponding client in a first preset period at a set time point;
extracting and processing the index file, and determining service data of each client in a first preset time period of demand, wherein the service data comprises active clients, active client details, active contacts and active contact detail data;
counting the increment of the business data of each customer and the memory capacity in a database in a second preset period, determining the key business of each customer according to the preset key business, and counting the increment of the key business of each customer in the second preset period;
the method comprises the steps of performing service closed loop marking processing on service objects under clients, grouping the service objects under the clients through ApiName and label fields of the service objects to form a plurality of service modules, and performing marking binding on the service modules and the key services through ApiName and label fields of each client service object;
And determining the activity degree of each client through a client activity degree scoring model set based on the PaaS platform based on the service data of each client in the first preset period and the key service increment in the second preset period.
2. The metadata capability based client liveness analysis method of claim 1 wherein the active client, active client details, active contacts, and active contact detail data are respectively:
active clients: extracting a request record of a client from an index file and processing data to obtain data, wherein the data comprises total access quantity, core function access quantity, auxiliary function access quantity, management behavior access quantity, activation user quantity, total user quantity, active user quantity and average access quantity data of each client in a first preset period;
active customer details: the method comprises the steps of obtaining information of an access service object in a request parameter from an index file, and then processing data to obtain module name, actual report name, core report, module API, function access amount, data type, object type and periactive user proportion data of an active client;
active contacts: active data in a first preset period of each affiliated contact under each client comprises total access quantity, core function access quantity, auxiliary function access quantity, management behavior access quantity and contact information data;
The active contact details comprise the module name, the actual report name, the core report, the module API, the function access amount, the data type, the object type and the business role data of each contact specifically accessed.
3. The metadata capability based customer liveness analysis method as in claim 2 further comprising the steps of:
marking the value of each client service module by applying the depth mark value to realize client grading;
setting calculation coefficients or weights in each sub-scoring model in a customer liveness scoring model through a scoring model set by the PaaS platform, wherein the sub-scoring model comprises an access score, a model score, a depth score, a decision layer score, an liveness score and a comprehensive vector score; calculating coefficients or weights includes accessing coefficients, depth coefficients, management weights, core weights, model coefficients, decision layer coefficients, comprehensive depth weights, and decision layer rating condition configurations.
4. The metadata capability based customer liveness analysis method of claim 3 wherein each of the sub-scoring model specification calculations comprises:
visit score = math.log10 (total visit/number of days to activate/number of users to activate) xvisit score coefficient; the total access amount is the total access amount in the daily active client records in the preset days;
The activation days are the total number of daily active client records;
the number of the activated users is the number of the activated users of the client which is inquired from a database;
depth score = incremental value of each business module × depth score coefficient;
model division= (management weight management duty+ (1-management weight) + (core weight core duty+ (1-core weight) ×auxiliary duty));
decision layer score = (lg (total access score of boss role)) decision layer coefficient;
active score = model score + visit score + decision layer score;
complex vector component = sqrt (active component, component depth weight) + (depth component, component depth weight);
wherein the management duty ratio, the core duty ratio and the auxiliary duty ratio are the respective proportions of the management access amount, the core access amount and the auxiliary access amount in the total access amount.
5. Client activity analysis device based on metadata capability, which is characterized by comprising
And a data acquisition module: the method comprises the steps of obtaining behavior data according to a timing task and generating an index file, wherein the behavior data are stored behavior data of each client in a preset log embedded point mode; the timing task is a task for acquiring behavior data of a corresponding client in a first preset period at a set time point;
The data collection and processing module is used for: the method comprises the steps of extracting and processing an index file, and determining service data of each client in a first preset time period of demand, wherein the service data comprise active clients, active client details, active contacts and active contact detail data;
the key business determining module to which the customer belongs: the method comprises the steps of counting the increment of business data of each customer and the storage amount in a database in a second preset period, determining the key business of each customer according to the preset key business, and counting the increment of the key business of each customer in the second preset period;
binding a marking module: the method comprises the steps of performing service closed loop marking processing on service objects under clients, grouping the service objects under the clients through ApiName and label fields of the service objects to form a plurality of service modules, and marking and binding the service modules with the key service through ApiName and label fields of each client service object;
and a model setting module: a customer liveness scoring model configured for use with PaaS-based platforms;
the calculation module: and the activity degree of each client is calculated and determined through the client activity degree scoring model according to the business data of each client in the first preset period and the key business increment which belongs to in the second preset period.
6. The metadata capability based client liveness analysis device of claim 5 wherein the active client, active client details, active contacts, and active contact detail data are respectively:
active clients: the data collecting and processing module extracts a request record of a client from the index file and processes data obtained by data processing, wherein the data comprises data of total access quantity, core function access quantity, auxiliary function access quantity, management behavior access quantity, activation user quantity, total user quantity, active user quantity and average access quantity of each client in a first preset period;
active customer details: the data receiving and processing module acquires the information of the access service object in the request parameter from the index file and then processes the data to acquire module name, actual report name, core report, module API, function access amount, data type, object type and peri-active user proportion data of the active client;
active contacts: the data collection processing module acquires active data in a first preset period of each affiliated contact under each client, wherein the active data comprises total access quantity, core function access quantity, auxiliary function access quantity, management behavior access quantity and contact information data;
Active contact details: the data collection processing module obtains the data description specifically accessed by each contact person, wherein the data description comprises the module name, the actual report name, the core report, the module API, the function access amount, the data type, the object type and the service role data of the active contact person.
7. The metadata capability based customer liveness analysis device of claim 6 further comprising a business module indexing module and a model coefficient or weight setting module wherein,
the business module marking module: the client classification method comprises the steps of marking each client service module by applying a depth mark value to realize client classification;
model coefficient or weight setting module: the method comprises the steps that a calculation coefficient or weight in each sub-scoring model in a customer liveness scoring model is set through a scoring model set by the PaaS platform, wherein the sub-scoring model comprises an access score, a model score, a depth score, a decision layer score, an liveness score and a comprehensive vector score; calculating coefficients or weights includes accessing coefficients, depth coefficients, management weights, core weights, model coefficients, decision layer coefficients, comprehensive depth weights, and decision layer rating condition configurations.
8. The metadata capability based customer liveness analysis device of claim 7 wherein each of said sub-scoring model specification calculations comprises:
visit score = math.log10 (total visit/number of days to activate/number of users to activate) xvisit score coefficient; the total access amount is the total access amount in the daily active client records in the preset days;
the activation days are the total number of daily active client records;
the number of the activated users is the number of the activated users of the client which is inquired from a database;
depth score = incremental value of each business module × depth score coefficient;
model division= (management weight management duty+ (1-management weight) + (core weight core duty+ (1-core weight) ×auxiliary duty));
decision layer score = (lg (total access score of boss role)) decision layer coefficient;
active score = model score + visit score + decision layer score;
complex vector component = sqrt (active component, component depth weight) + (depth component, component depth weight);
wherein the management duty ratio, the core duty ratio and the auxiliary duty ratio are the respective proportions of the management access amount, the core access amount and the auxiliary access amount in the total access amount.
9. Computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the metadata-based capability customer activity analysis method according to any of claims 1 to 4 when executing the computer program.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the metadata capability based client activity analysis method of any of claims 1 to 4.
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