CN117196801A - Analysis method and device for banking activity data, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a method, a device, computer equipment, a storage medium and a computer program product for analyzing banking activity data, and relates to the technical field of big data. The method comprises the following steps: receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel; collecting target activity data of the target banking activity in each data collecting channel by using a buried point collecting model; carrying out relevance processing on the target activity data to obtain corresponding relevance factors; and carrying out multi-data source analysis on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity. By adopting the method, the accuracy rate of analysis of the bank activity data can be improved.
Description
Technical Field
The present application relates to the field of big data technology, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for analyzing banking activity data.
Background
In the context of rapid development of big data technology, data driving has become the main melody of the new era of digital economy. The evaluation of the implementation effect of various banking activities by data analysis technology has become an important work for each bank.
At present, analysis of banking activity data is usually completed by means of data retrieval or keyword query on banking activity webpages. However, the analysis mode faces the problem of single data source, incomplete activity data easily causes errors in the finally obtained data analysis result, and therefore the accuracy rate of banking activity data analysis is low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a banking data analysis method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve accuracy of banking data analysis.
In a first aspect, the present application provides a method of analysis of banking activity data. The method comprises the following steps:
receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel; collecting target activity data of the target banking activity in each data collecting channel by using a buried point collecting model; carrying out relevance processing on the target activity data to obtain corresponding relevance factors; and carrying out multi-data source analysis on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity.
In an embodiment, the performing relevance processing on the target activity data to obtain a corresponding relevance factor includes:
normalizing the target activity data to obtain normalized data; calculating a relevance value of the normalized data by using a relevance model, wherein the relevance model is a relevance data analysis model established based on gray relevance and gray entropy theory; comparing the association value with a preset association threshold value to obtain an association comparison result; and determining the relevance factor based on the relevance comparison result.
In an embodiment, the relevance factors include an activity cost factor, an activity participation factor, and an activity rewards factor; the multi-data source analysis is carried out on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity, which comprises the following steps:
acquiring the factor weights corresponding to the activity cost factor, the activity participation factor and the activity rewarding factor respectively; and inputting the activity cost factor, the activity participation factor, the activity rewarding factor and the respective corresponding factor weights into the multi-data source analysis model to obtain the activity data analysis result.
In an embodiment, the collecting, by using a buried point collection model, target activity data of the target banking activity under each data collection channel includes:
inquiring the acquisition priority of the target banking activity according to a preset priority dividing standard; based on the acquisition priority, uploading the unique identification of the target banking activity to a task queue to obtain a data acquisition task list; and acquiring the target activity data by using a buried point acquisition model according to the sequence of the data acquisition task list.
In an embodiment, the target activity data includes basic data and business data, and the buried point acquisition model includes a basic data buried point acquisition model and a business data buried point acquisition model; the acquiring the target activity data by using the buried point acquisition model comprises the following steps:
acquiring the basic data of the target banking activity in each data acquisition channel by using the basic data buried point acquisition model; and acquiring the business data of the target banking activity under each data acquisition channel by using the business data embedded point acquisition model.
In an embodiment, after the step of obtaining the activity data analysis result corresponding to the target banking activity, the method further includes:
Receiving an activity data display instruction triggered by a target user, wherein the activity data display instruction comprises at least one display index, a display time period and a display mode; screening data to be displayed corresponding to each display index from the activity data analysis result; and displaying the data to be displayed based on the display time period and the display mode.
In a second aspect, the application also provides a device for analyzing banking activity data. The device comprises:
the channel acquisition module is used for receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel;
the data acquisition module is used for acquiring target activity data of the target banking activity in each data acquisition channel by using a buried point acquisition model;
the relevance analysis module is used for carrying out relevance processing on the target activity data to obtain corresponding relevance factors;
and the data analysis module is used for acquiring the relevance factor corresponding to the target activity data, and carrying out multi-data source analysis on the relevance factor to obtain an activity data analysis result corresponding to the target bank activity.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel; collecting target activity data of the target banking activity in each data collecting channel by using a buried point collecting model; carrying out relevance processing on the target activity data to obtain corresponding relevance factors; and carrying out multi-data source analysis on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel; collecting target activity data of the target banking activity in each data collecting channel by using a buried point collecting model; carrying out relevance processing on the target activity data to obtain corresponding relevance factors; and carrying out multi-data source analysis on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel; collecting target activity data of the target banking activity in each data collecting channel by using a buried point collecting model; carrying out relevance processing on the target activity data to obtain corresponding relevance factors; and carrying out multi-data source analysis on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity.
Compared with the prior art that the analysis of the banking activity data is finished by means of data retrieval or keyword query on the banking activity webpage, the method and the device for analyzing the banking activity data firstly receive the data acquisition signals aiming at the target banking activity, wherein the data acquisition signals carry at least one data acquisition channel, so that the follow-up activity data acquisition is carried out according to each data acquisition channel, and the integrity of the data acquisition is ensured; then, acquiring target activity data of the target banking activity in each data acquisition channel by using a buried point acquisition model, wherein the buried point acquisition model can acquire the activity data more comprehensively and more specifically, so that the method is more suitable for subsequent data analysis; and performing relevance processing on the target activity data to obtain corresponding relevance factors, wherein the collected target activity data are derived from different data collection channels, so that the relevance factors are obtained by performing relevance analysis on the target activity data, and then performing multi-data source analysis on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity, thereby effectively improving the accuracy of banking activity data analysis.
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FIG. 1 is an application scenario diagram of a method of analysis of banking activity data in one embodiment;
FIG. 2 is a flow chart of a method of analyzing banking activity data in one embodiment;
FIG. 3 is a flow diagram of relevance analysis in one embodiment;
FIG. 4 is a flow chart of obtaining a correlation factor in one embodiment;
FIG. 5 is a flow diagram of query acquisition priority in one embodiment;
FIG. 6 is a flow diagram of data acquisition in one embodiment;
FIG. 7 is a flow diagram of data presentation in one embodiment;
FIG. 8 is a block diagram of an analysis device of banking activity data in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The analysis method of banking activity data provided by the embodiment of the disclosure can be applied to an application environment as shown in fig. 1. The execution body of the banking activity data analysis may be a data capture server 102, where the data capture server 102 is in communication connection with the user terminal 104, and when the data capture server 102 receives a data collection signal for a target banking activity sent from the user terminal 104, the data capture server analyzes the signal to obtain at least one data collection channel 106, and collects target activity data of the target banking activity under each data collection channel 106 by using a pre-stored buried point collection model, then performs relevance processing on the target activity data to obtain a corresponding relevance factor, and finally performs multi-data source analysis on the relevance factor, thereby obtaining an activity data analysis result corresponding to the target banking activity.
The data crawling server 102 may be implemented by an independent server or a server cluster formed by a plurality of servers; the user terminal 104 may be, but is not limited to, various smartphones, tablets, computers, portable wearable devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like.
In one embodiment, as shown in FIG. 2, a method of analyzing banking activity data is provided. In this embodiment, the method includes the steps of:
step S202, a data acquisition signal for a target banking activity is received.
The data acquisition signal carries at least one data acquisition channel, and the publishing range of the banking activities can be multi-platform, for example, a banking party can publish the data acquisition signal on a mobile phone bank, a micro-bank and an official website for a certain banking activity, and the data acquisition signal can also be published in a short message form; the target banking activities refer to various activities issued by the banks to users, such as marketing activities, coupon activities, full deactivation, shopping activities, and the like; the data acquisition signal refers to a signal for acquiring activity data of a target banking activity.
Specifically, when the data capture server receives a data acquisition signal sent by the user terminal, the data acquisition signal is analyzed, so that at least one data acquisition channel is obtained, and the expression form of the data acquisition channel can be in a text form or an address form.
Step S204, collecting target activity data of the target banking activity in each data collection channel by using a buried point collection model.
The buried point acquisition model is a model for carrying out buried point acquisition on data of target banking activities. It can be understood that the data collected by the buried point collection model can be buried in advance, for example, two data of the activity expense and the activity information of the bank activity are buried in advance, so that the two data can be directly collected by the buried point collection model, and the collected data can be more comprehensive and more specific by the data collection by the buried point collection model, so that the method is more suitable for subsequent data analysis; the target activity data refers to the whole period data of the target banking activity, and can comprise activity cost data, activity participation data and activity rewards data.
As one example, the activity cost data may include data of total cost, usage cost, remaining cost, etc. of the activity, the activity participation data may include data of number of activity visitors, number of activity participants, number of activity forwarders, etc., and the activity rewards data may include activity winning data, activity winning data.
Specifically, after the data acquisition channels are acquired, the data acquisition server calls the buried point acquisition model, and the buried point acquisition model can automatically search data of the pre-buried points under each data acquisition channel and acquire data to obtain target activity data.
Step S206, carrying out relevance processing on the target activity data to obtain corresponding relevance factors.
The relevance processing refers to analysis processing of relevance among the target activity data; the relevance factor refers to a relevance influence factor between target activity data, and can comprise an activity cost factor, an activity participation factor and an activity rewards factor.
Specifically, a relevance value between each target activity data is calculated, the obtained relevance value is compared with a preset relevance threshold value to obtain a relevance comparison result, and therefore a relevance factor is determined according to the relevance comparison result.
Step S208, multi-data source analysis is carried out on the relevance factors, and an activity data analysis result corresponding to the target bank activity is obtained.
The multi-data source analysis refers to analyzing data of a plurality of data sources, that is, analyzing target activity data collected under a plurality of data collection channels in the embodiment; the activity data analysis results can be used for multi-dimensional assessment of target banking activities, such as activity cost assessment, activity participation assessment and activity rewards assessment.
Specifically, the relevance factors are input into a multi-data source analysis model, data analysis is carried out, and an activity data analysis result corresponding to the target banking activity is obtained. Through multi-data source analysis of target activity data from a plurality of data acquisition channels, the relevance among the activity data of different data acquisition channels is comprehensively considered, so that the finally obtained activity data analysis result is more accurate and reliable.
The method comprises the steps of firstly receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel, so that activity data acquisition can be conveniently carried out according to each data acquisition channel, and the integrity of the data acquisition is ensured; then, acquiring target activity data of the target banking activity in each data acquisition channel by using a buried point acquisition model, wherein the buried point acquisition model can acquire the activity data more comprehensively and more specifically, so that the method is more suitable for subsequent data analysis; and performing relevance processing on the target activity data to obtain corresponding relevance factors, wherein the collected target activity data are derived from different data collection channels, so that the relevance factors are obtained by performing relevance analysis on the target activity data, and then performing multi-data source analysis on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity, thereby effectively improving the accuracy of banking activity data analysis.
In one embodiment, as shown in fig. 3, the performing relevance processing on the target activity data to obtain a corresponding relevance factor includes:
step S302, normalizing the target activity data to obtain normalized data.
The normalized data is data obtained by normalizing target activity data. The target activity data is normalized, so that the problem of comparability between the data can be solved, and the data in the subsequent input model is more accurate.
Specifically, the target activity data is input into a preset normalization model, and the normalization data is output, wherein the preset normalization model can be any one of a linear normalization model, a zero-mean normalization model and a batch normalization model (Batch Normalization, BN).
And step S304, calculating the relevance value of the normalized data by using a relevance model.
The relevance model is a model for performing relevance analysis on target activity data, the relevance model is a relevance data analysis model established based on gray relevance and gray entropy theory, the gray relevance is similarity or dissimilarity degree of development trends among data, and the gray relevance can be used for measuring the relevance degree among factors; the gray entropy theory refers to the degree to which certain data states appear, i.e., the degree to which correlation values appear, and is more accurate than the gray correlation theory. The association degree value of the target activity data is calculated by using the association degree model combining the gray association degree and the gray entropy theory, the association degree between the target activity data and the probability of occurrence of the association degree can be better analyzed, and the accuracy of association degree analysis is improved.
Specifically, the normalized data is input into a relevance model, the relevance model can calculate gray relevance coefficients corresponding to each normalized data, then weight average is carried out on each gray relevance coefficient, and the obtained weight average is used as a relevance value corresponding to each normalized data.
And step S306, comparing the association value with a preset association threshold value to obtain an association comparison result.
Step S308, determining the relevance factor based on the relevance comparison result.
The preset relevance threshold is a threshold for representing reliability of a relevance value, namely minimum confidence level or minimum support level; the correlation comparison result can be used for representing a reliability result of the correlation value; the relevance factors refer to index factors describing the relevance relation of the target activity data, and can comprise an activity cost factor, an activity participation factor and an activity rewards factor.
Specifically, the obtained relevance values are compared with a preset relevance threshold value to check the reliability of the relevance values, if the relevance values are larger than or equal to the preset relevance threshold value, the relevance values are indicated to be reliable, the target activity data corresponding to the reliable relevance values are taken as relevance factors, if the relevance values are smaller than the preset relevance threshold value, the target activity data indicating that the relevance values are unreliable are not taken as the relevance factors, and after relevance analysis is carried out on the target activity data, the activity cost factors, the activity participation factors and the activity rewarding factors with the relevance values larger than or equal to the preset relevance threshold value are obtained.
In the embodiment, the association degree analysis is performed on the target activity data by using the association degree model established based on the gray association degree and the gray entropy theory, so that the association degree between the target activity data can be more accurately analyzed, and then the association degree value obtained by association analysis is compared with the preset association degree threshold value, so that the association factor with high reliability is screened, and the accuracy and the reliability of the subsequent multi-data source analysis based on the association factor are ensured.
In one embodiment, as shown in fig. 4, the performing multi-data source analysis on the relevance factor to obtain an activity data analysis result corresponding to the target banking activity includes:
step S402, acquiring the factor weights corresponding to the activity cost factor, the activity participation factor and the activity rewards factor.
Wherein, the activity cost factor refers to a correlation index between activity cost data; the activity participation factors refer to association indexes among the activity participation data; the activity rewarding factor refers to a correlation index between activity rewarding data; the factor weight may refer to the importance of the relevance factor.
Specifically, after the activity cost factor, the activity participation factor and the activity rewarding factor are obtained, a relation mapping table between each relevance factor and the weight is queried, the factor weight corresponding to each relevance factor can be recorded in the relation mapping table, and the factor weight can be manually set according to experience or an actual application scene.
And step S404, inputting the activity cost factor, the activity participation factor, the activity rewarding factor and the respective corresponding factor weights into the multi-data source analysis model to obtain the activity data analysis result.
The multi-data source analysis model is a model for carrying out data analysis on target activity data in a plurality of data acquisition channels; the activity data analysis results can be used for multi-dimensional assessment of target banking activities, such as activity cost assessment, activity participation assessment and activity rewards assessment.
Specifically, a multi-data source analysis model is built in advance, when an activity cost factor, an activity participation factor and an activity rewarding factor are obtained after relevance analysis, factor weights corresponding to the activity cost factor, the activity participation factor and the activity rewarding factor are inquired and obtained, then the activity cost factor and the corresponding factor weights are input into the multi-data source analysis model to obtain an activity cost analysis result, the activity cost analysis result is used for evaluating the activity cost of the target bank activity, the activity participation factor and the corresponding factor weights are input into the multi-data source analysis model to obtain an activity participation analysis result, the activity participation information of the target bank activity is evaluated, the activity rewarding factor and the corresponding factor weights are input into the multi-data source analysis model to obtain an activity rewarding analysis result, and the activity rewarding information of the target bank activity is evaluated.
In this embodiment, multiple data sources are analyzed through target activity data under different data acquisition channels, so that the problem that the accuracy rate of analysis of banking activity data is low due to single data source and incomplete acquired banking activity data caused by completing data analysis by only carrying out data retrieval or keyword query on banking activity webpages at present is avoided. Therefore, the embodiment can effectively improve the accuracy of the analysis of the banking activity data.
In one embodiment, as shown in fig. 5, the collecting, by using the buried point collection model, the target activity data of the target banking activity under each data collection channel includes:
step S502, inquiring the acquisition priority of the target banking activity according to a preset priority division standard.
The preset priority classification standard refers to a standard for classifying the priority of the target banking activity; the collection priority refers to the priority of data collection for the target banking activity.
As an example, the condition for prioritizing the target banking activity may be according to the time of development, the extent of popularity, the number of participants, etc. of the target banking activity, for example, the recently developed banking activity may be preferentially subjected to data acquisition, or the popular banking activity may be preferentially subjected to data acquisition, or the banking activity with a large number of participants may be preferentially subjected to data acquisition. The banking activities which are performed at high heat, high number of people and recently are more representative, and the data of the banking activities can be collected preferentially, so that the subsequent data analysis is more reliable.
The corresponding relation table between each target banking activity and the corresponding collection priority can be preset, after the data collection signals of the target banking activities are received, the corresponding relation table is queried, the collection priority corresponding to the target banking activities is obtained, wherein the collection priority can be classified, and the higher the grade of the target banking activities, the more preferentially the data collection is carried out.
Step S504, based on the collection priority, uploading the unique identification of the target banking activity to a task queue to obtain a data collection task list.
And step S506, acquiring the target activity data by using a buried point acquisition model according to the sequence of the data acquisition task list.
The unique identifier refers to a unique identifier of the target banking activity, and can be an activity proposal number of the target banking activity; the task queue is a task queue for collecting data of a target banking activity; the data acquisition task list refers to a list generated after uploading a unique identifier of a target banking activity to a task queue and is used for indicating a buried point acquisition model to acquire data according to a list sequence.
Specifically, after acquiring acquisition priority information of a target banking activity, uploading an activity proposal number of the target banking activity to a task queue according to the acquisition priority, namely, uploading the activity proposal number to the task queue with high acquisition priority, uploading the activity proposal number to the task queue with low acquisition priority, and after all activity proposal numbers of the target banking activity needing data acquisition are uploaded to the task queue, generating a data acquisition task list; and controlling the buried point acquisition model to acquire target activity data of target banking activities according to the sequence of the data acquisition task list.
In the embodiment, the data acquisition task list is generated according to the acquisition priority of the target banking activity, so that the buried point acquisition model is controlled to acquire data according to the sequence of the data acquisition character list, the data of the representative target banking activity can be acquired preferentially, and the reliability of subsequent data analysis is improved.
In one embodiment, as shown in fig. 6, the acquiring the target activity data using a buried point acquisition model includes:
step S602, collecting the basic data of the target banking activity under each data collection channel by using the basic data buried point collection model.
Step S604, collecting the business data of the target banking activity under each data collection channel by using the business data embedded point collection model.
The target activity data is divided into basic data and business data, namely the activity cost data can be divided into activity cost basic data and activity cost business data, the activity participation data can be divided into activity participation basic data and activity participation business data, the activity rewarding data can be divided into activity rewarding basic data and activity rewarding business data, the basic data refers to basic attribute data of target banking activities, and the business data refers to data generated in the developing process of the target banking activities; the buried point acquisition model comprises a basic data buried point acquisition model and a service data buried point acquisition model, wherein the basic data buried point acquisition model refers to a model for buried point acquisition of basic data, and the service data buried point acquisition model refers to a model for buried point acquisition of service data.
As one example, the base data may be an activity cost budget, an activity participation time, an activity participation place, an activity prize number, an activity prize type, etc., and the business data may be an activity use fee, an activity remaining fee, an activity participation number, an activity winner, etc.
Specifically, the basic data embedded point acquisition model is utilized to acquire the basic data of the activity cost, the basic data of the activity participation and the basic data of the activity rewards of the target banking activity under each data acquisition channel, and the business data embedded point acquisition model is utilized to acquire the business data of the activity cost, the business data of the activity participation and the business data of the activity rewards of the target banking activity under each data acquisition channel.
In this embodiment, different data embedded point acquisition models are used to respectively perform data acquisition on basic data and business data of a target banking activity, and compared with the data acquisition mode of only using one embedded point acquisition model, the data acquisition mode of this embodiment has pertinence, and missing acquisition of important data can be avoided, so that acquired target activity data is ensured to be more accurate.
In one embodiment, as shown in fig. 7, after the step of obtaining the activity data analysis result corresponding to the target banking activity, the method further includes:
Step S702, an activity data display instruction triggered by a target user is received.
The activity data display instruction is used for representing display information required by a target user for target banking activities and comprises at least one display index, a display time period and a display mode.
Specifically, an activity data display instruction triggered by a target user is received, wherein the mode of triggering the activity data display instruction by the target user can be triggered by sending a short message or by clicking a corresponding button, and after the activity data display instruction is received, instruction analysis is performed to obtain a display index, a display time period and a display mode.
Step S704, screening the data to be displayed corresponding to each display index from the activity data analysis result.
The data to be displayed is data displayed to a target user; the display index refers to an activity data analysis index required to be displayed by a target user.
Specifically, according to each display index, the corresponding data to be displayed is screened out from the analysis results of the activity data, for example, the display index is the activity cost, then the data analysis results corresponding to the activity cost data are screened out from the analysis results of the activity data, for example, the display index is the activity participation and the activity rewards, and then the data analysis results corresponding to the activity rewards and the data analysis results corresponding to the activity participation data are screened out from the analysis results of the activity data.
Step S706, displaying the data to be displayed based on the display time period and the display mode.
The display time period refers to a time range of displaying target activity data; the presentation mode refers to a form of presentation of target activity data.
Specifically, the data to be displayed is displayed on the terminal equipment of the target user according to the display time period and the display mode, wherein the display mode can be any one or any combination of a plurality of report displays, chart displays, text displays and video displays.
In this embodiment, diversified data display is performed according to the active data display instruction triggered by the target user, so that the displayed data not only meets the requirements of the target user, but also improves the richness of the data display, thereby improving the user experience.
In one embodiment, the target banking activity may be a full-consumption banking activity, and the data capture server may be a full-consumption banking activity data capture server, and in particular, the full-consumption banking activity data capture server receives a data acquisition signal for the full-consumption banking activity, where the data acquisition signal carries at least one data acquisition channel; acquiring consumption full deactivation data of a bank consumption full deactivation activity under each data acquisition channel by using a buried point acquisition model; carrying out relevance processing on consumption full deactivation data to obtain corresponding relevance factors; and carrying out multi-data source analysis on the relevance factors to obtain an activity data analysis result corresponding to the full consumption deactivation of the bank. And carrying out correlation analysis on the full consumption deactivation of the bank to obtain a correlation factor, and then carrying out multi-data source analysis on the correlation factor to obtain an activity data analysis result corresponding to the full consumption deactivation of the bank, thereby effectively improving the accuracy of the full consumption deactivation data analysis of the bank.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a banking activity data analysis device for realizing the banking activity data analysis method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the analysis device for banking activity data provided below may refer to the limitation of the analysis method for banking activity data hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 8, there is provided an analysis apparatus of banking activity data, including: channel acquisition module 802, data acquisition module 804, relevance analysis module 806, data analysis module 808, wherein:
a channel acquisition module 802, configured to receive a data acquisition signal for a target banking activity, where the data acquisition signal carries at least one data acquisition channel;
the data acquisition module 804 is configured to acquire target activity data of the target banking activity under each data acquisition channel by using a buried point acquisition model;
the relevance analysis module 806 is configured to perform relevance processing on the target activity data to obtain a corresponding relevance factor;
and the data analysis module 808 is configured to obtain a relevance factor corresponding to the target activity data, and perform multi-data source analysis on the relevance factor to obtain an activity data analysis result corresponding to the target banking activity.
In one embodiment, the relevance analysis module 806 is further configured to:
normalizing the target activity data to obtain normalized data; calculating a relevance value of the normalized data by using a relevance model, wherein the relevance model is a relevance data analysis model established based on gray relevance and gray entropy theory; comparing the association value with a preset association threshold value to obtain an association comparison result; and determining the relevance factor based on the relevance comparison result.
In one embodiment, the data analysis module 808 is further configured to:
acquiring the factor weights corresponding to the activity cost factor, the activity participation factor and the activity rewarding factor respectively; and inputting the activity cost factor, the activity participation factor, the activity rewarding factor and the respective corresponding factor weights into the multi-data source analysis model to obtain the activity data analysis result.
In one embodiment, the data acquisition module 804 further includes:
the priority inquiry unit is used for inquiring the acquisition priority of the target banking activity according to a preset priority division standard; a task queue uploading unit, configured to upload the unique identifier of the target banking activity to a task queue based on the acquisition priority, to obtain a data acquisition task list; and the buried point acquisition unit is used for acquiring the target activity data by using a buried point acquisition model according to the sequence of the data acquisition task list.
In one embodiment, the buried point acquisition unit is further configured to:
acquiring the basic data of the target banking activity in each data acquisition channel by using the basic data buried point acquisition model; and acquiring the business data of the target banking activity under each data acquisition channel by using the business data embedded point acquisition model.
In one embodiment, after the data analysis module 808, further includes:
receiving an activity data display instruction triggered by a target user, wherein the activity data display instruction comprises at least one display index, a display time period and a display mode; screening data to be displayed corresponding to each display index from the activity data analysis result; and displaying the data to be displayed based on the display time period and the display mode.
The above-described respective modules in the analysis device of banking data may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing item recommendation data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of analysis of banking activity data.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel; collecting target activity data of the target banking activity in each data collecting channel by using a buried point collecting model; carrying out relevance processing on the target activity data to obtain corresponding relevance factors; and acquiring a relevance factor corresponding to the target activity data, and performing multi-data source analysis on the relevance factor to obtain an activity data analysis result corresponding to the target banking activity.
In one embodiment, the processor when executing the computer program further performs the steps of:
normalizing the target activity data to obtain normalized data; calculating a relevance value of the normalized data by using a relevance model, wherein the relevance model is a relevance data analysis model established based on gray relevance and gray entropy theory; comparing the association value with a preset association threshold value to obtain an association comparison result; and determining the relevance factor based on the relevance comparison result.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring the factor weights corresponding to the activity cost factor, the activity participation factor and the activity rewarding factor respectively; and inputting the activity cost factor, the activity participation factor, the activity rewarding factor and the respective corresponding factor weights into the multi-data source analysis model to obtain the activity data analysis result.
In one embodiment, the processor when executing the computer program further performs the steps of:
inquiring the acquisition priority of the target banking activity according to a preset priority dividing standard; based on the acquisition priority, uploading the unique identification of the target banking activity to a task queue to obtain a data acquisition task list; and acquiring the target activity data by using a buried point acquisition model according to the sequence of the data acquisition task list.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring the basic data of the target banking activity in each data acquisition channel by using the basic data buried point acquisition model; and acquiring the business data of the target banking activity under each data acquisition channel by using the business data embedded point acquisition model.
In one embodiment, the processor when executing the computer program further performs the steps of:
receiving an activity data display instruction triggered by a target user, wherein the activity data display instruction comprises at least one display index, a display time period and a display mode; screening data to be displayed corresponding to each display index from the activity data analysis result; and displaying the data to be displayed based on the display time period and the display mode.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel; collecting target activity data of the target banking activity in each data collecting channel by using a buried point collecting model; carrying out relevance processing on the target activity data to obtain corresponding relevance factors; and acquiring a relevance factor corresponding to the target activity data, and performing multi-data source analysis on the relevance factor to obtain an activity data analysis result corresponding to the target banking activity.
In one embodiment, the computer program when executed by the processor further performs the steps of:
normalizing the target activity data to obtain normalized data; calculating a relevance value of the normalized data by using a relevance model, wherein the relevance model is a relevance data analysis model established based on gray relevance and gray entropy theory; comparing the association value with a preset association threshold value to obtain an association comparison result; and determining the relevance factor based on the relevance comparison result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the factor weights corresponding to the activity cost factor, the activity participation factor and the activity rewarding factor respectively; and inputting the activity cost factor, the activity participation factor, the activity rewarding factor and the respective corresponding factor weights into the multi-data source analysis model to obtain the activity data analysis result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inquiring the acquisition priority of the target banking activity according to a preset priority dividing standard; based on the acquisition priority, uploading the unique identification of the target banking activity to a task queue to obtain a data acquisition task list; and acquiring the target activity data by using a buried point acquisition model according to the sequence of the data acquisition task list.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the basic data of the target banking activity in each data acquisition channel by using the basic data buried point acquisition model; and acquiring the business data of the target banking activity under each data acquisition channel by using the business data embedded point acquisition model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
receiving an activity data display instruction triggered by a target user, wherein the activity data display instruction comprises at least one display index, a display time period and a display mode; screening data to be displayed corresponding to each display index from the activity data analysis result; and displaying the data to be displayed based on the display time period and the display mode.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel; collecting target activity data of the target banking activity in each data collecting channel by using a buried point collecting model; carrying out relevance processing on the target activity data to obtain corresponding relevance factors; and acquiring a relevance factor corresponding to the target activity data, and performing multi-data source analysis on the relevance factor to obtain an activity data analysis result corresponding to the target banking activity.
In one embodiment, the computer program when executed by the processor further performs the steps of:
normalizing the target activity data to obtain normalized data; calculating a relevance value of the normalized data by using a relevance model, wherein the relevance model is a relevance data analysis model established based on gray relevance and gray entropy theory; comparing the association value with a preset association threshold value to obtain an association comparison result; and determining the relevance factor based on the relevance comparison result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the factor weights corresponding to the activity cost factor, the activity participation factor and the activity rewarding factor respectively; and inputting the activity cost factor, the activity participation factor, the activity rewarding factor and the respective corresponding factor weights into the multi-data source analysis model to obtain the activity data analysis result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inquiring the acquisition priority of the target banking activity according to a preset priority dividing standard; based on the acquisition priority, uploading the unique identification of the target banking activity to a task queue to obtain a data acquisition task list; and acquiring the target activity data by using a buried point acquisition model according to the sequence of the data acquisition task list.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the basic data of the target banking activity in each data acquisition channel by using the basic data buried point acquisition model; and acquiring the business data of the target banking activity under each data acquisition channel by using the business data embedded point acquisition model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
receiving an activity data display instruction triggered by a target user, wherein the activity data display instruction comprises at least one display index, a display time period and a display mode; screening data to be displayed corresponding to each display index from the activity data analysis result; and displaying the data to be displayed based on the display time period and the display mode.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
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, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method of analyzing banking data, the method comprising:
receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel;
collecting target activity data of the target banking activity in each data collecting channel by using a buried point collecting model;
Carrying out relevance processing on the target activity data to obtain corresponding relevance factors;
and carrying out multi-data source analysis on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity.
2. The method of claim 1, wherein the performing relevance processing on the target activity data to obtain a corresponding relevance factor comprises:
normalizing the target activity data to obtain normalized data;
calculating a relevance value of the normalized data by using a relevance model, wherein the relevance model is a relevance data analysis model established based on gray relevance and gray entropy theory;
comparing the association value with a preset association threshold value to obtain an association comparison result;
and determining the relevance factor based on the relevance comparison result.
3. The method of claim 1, wherein the relevance factors include an activity cost factor, an activity participation factor, and an activity rewards factor;
the multi-data source analysis is carried out on the relevance factors to obtain an activity data analysis result corresponding to the target banking activity, which comprises the following steps:
Acquiring the factor weights corresponding to the activity cost factor, the activity participation factor and the activity rewarding factor respectively;
and inputting the activity cost factor, the activity participation factor, the activity rewarding factor and the respective corresponding factor weights into the multi-data source analysis model to obtain the activity data analysis result.
4. The method of claim 1, wherein the acquiring the target banking activity data under each of the data acquisition channels using a buried point acquisition model comprises:
inquiring the acquisition priority of the target banking activity according to a preset priority dividing standard;
based on the acquisition priority, uploading the unique identification of the target banking activity to a task queue to obtain a data acquisition task list;
and acquiring the target activity data by using a buried point acquisition model according to the sequence of the data acquisition task list.
5. The method of claim 4, wherein the target activity data comprises base data and business data, and the buried point acquisition model comprises a base data buried point acquisition model and a business data buried point acquisition model;
The acquiring the target activity data by using the buried point acquisition model comprises the following steps:
acquiring the basic data of the target banking activity in each data acquisition channel by using the basic data buried point acquisition model;
and acquiring the business data of the target banking activity under each data acquisition channel by using the business data embedded point acquisition model.
6. The method of claim 1, further comprising, after the step of obtaining the activity data analysis result corresponding to the target banking activity:
receiving an activity data display instruction triggered by a target user, wherein the activity data display instruction comprises at least one display index, a display time period and a display mode;
screening data to be displayed corresponding to each display index from the activity data analysis result;
and displaying the data to be displayed based on the display time period and the display mode.
7. An apparatus for analyzing banking data, the apparatus comprising:
the channel acquisition module is used for receiving a data acquisition signal aiming at a target banking activity, wherein the data acquisition signal carries at least one data acquisition channel;
The data acquisition module is used for acquiring target activity data of the target banking activity in each data acquisition channel by using a buried point acquisition model;
the relevance analysis module is used for carrying out relevance processing on the target activity data to obtain corresponding relevance factors;
and the data analysis module is used for acquiring the relevance factor corresponding to the target activity data, and carrying out multi-data source analysis on the relevance factor to obtain an activity data analysis result corresponding to the target bank activity.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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|---|---|---|---|
| CN202310906849.4A CN117196801A (en) | 2023-07-24 | 2023-07-24 | Analysis method and device for banking activity data, computer equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310906849.4A CN117196801A (en) | 2023-07-24 | 2023-07-24 | Analysis method and device for banking activity data, computer equipment and storage medium |
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