CN112734565B - Fluidity coverage prediction method and device - Google Patents
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Abstract
A fluidity coverage prediction method and a device belong to the technical field of big data and artificial intelligence, and the method comprises the following steps: acquiring service data of each service application server system at a historical moment and mobility coverage rate data at the historical moment; training the first training model according to the business data at the historical moment and the mobility coverage rate data at the historical moment; screening the fluidity coverage rate data and the business data at the corresponding time according to the preset conditions, and training the second training model according to the screening result; obtaining a mobility coverage rate predicted value according to the first training model and predicted business data; comparing the predicted value with a preset threshold value, and carrying the fluidity coverage rate predicted value into a second training model according to the comparison result to obtain a plurality of business data configuration scheme data; and obtaining a service data adjustment scheme according to the comparison result of the service data configuration scheme data and the predicted service data.
Description
Technical Field
The invention relates to the technical fields of big data and artificial intelligence, which can be applied to the fields of banking data processing and finance, in particular to a mobility coverage prediction method and a mobility coverage prediction device.
Background
Mobility coverage is a regulatory core indicator that manages short-term mobility risks. The liquidity coverage is intended to ensure that commercial banks have sufficient, acceptable, quality liquidity assets that can meet future liquidity requirements by rendering such assets under liquidity pressure scenarios specified by the silver-supervision.
In the prior art, when the flowability coverage rate calculation of the commercial bank is displayed in a business report form, the timeliness and the automation degree are not high, and meanwhile, the future flowability coverage rate of the commercial bank cannot be predicted, so that the capability of the bank for controlling the risk level is improved.
Disclosure of Invention
The invention aims to provide a mobility coverage prediction method and a mobility coverage prediction device, which are used for early warning of mobility coverage risks in time and predicting and obtaining business data configuration optimization early warning according to predicted mobility coverage conditions; the prediction result is effectively utilized, the capability of controlling the risk level is improved, and the resistance of unexpected loss of the bank is enhanced; the accuracy of index measurement and calculation can be improved, and the prospective and intelligent degree of index management can be improved.
In order to achieve the above object, the present invention provides a fluidity coverage prediction method, which includes: acquiring service data of each service application server system at a historical moment and mobility coverage rate data at the historical moment; training a pre-established machine learning model according to the business data at the historical moment and the mobility coverage rate data at the historical moment to obtain a first training model; screening the fluidity coverage rate data and the business data at the corresponding moment according to preset conditions, and training a pre-established machine learning model according to screening results to obtain a second training model; obtaining a mobility coverage rate predicted value according to the first training model and predicted service data; comparing the predicted value with a preset threshold value, and carrying the fluidity coverage rate predicted value into the second training model according to a comparison result to obtain a plurality of business data configuration scheme data; and obtaining a service data adjustment scheme according to the comparison result of the service data configuration scheme data and the predicted service data.
The present invention also provides a fluidity coverage prediction device, the device comprising: the system comprises a data acquisition module, a training module, a prediction module, a configuration scheme calculation module and a matching module; the data acquisition module is used for acquiring service data of each service application server system at the historical moment and mobility coverage rate data at the historical moment; the training module is used for training a pre-established machine learning model according to the business data at the historical moment and the mobility coverage rate data at the historical moment to obtain a first training model; screening the fluidity coverage rate data and the business data at the corresponding moment according to preset conditions, and training a pre-established machine learning model according to screening results to obtain a second training model; the prediction module is used for obtaining a mobility coverage rate predicted value according to the first training model and predicted service data; comparing the predicted value with a preset threshold value, and providing the mobility coverage predicted value to the second training module according to a comparison result; the configuration scheme calculation module is used for bringing the mobility coverage rate predicted value into the second training model to obtain a plurality of business data configuration scheme data; the matching module is used for obtaining a service data adjustment scheme according to the comparison result of the service data configuration scheme data and the predicted service data.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above method when executing the computer program.
The present invention also provides a computer readable storage medium storing a computer program for executing the above method.
The beneficial technical effects of the invention are as follows: on one hand, the mobility coverage rate target can be determined according to the self operation condition, on the other hand, when the actual mobility coverage rate deviates from the target, adjustment measures can be actively taken to reduce the deviation degree, and the resistance of mobility coverage rate risks under external impact is improved; moreover, through prediction of the mobility coverage rate, the probability that the mobility coverage rate does not reach the standard is reduced; the mentioned fluidity coverage prediction can also provide a certain research reference for the fluidity coverage of other emerging markets, and has wide application range and high portability.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate and together with the description serve to explain the invention. In the drawings:
FIG. 1A is a flow chart of a flow coverage prediction method provided by the present invention;
FIG. 1B is a flowchart of a flow coverage prediction method provided by the present invention;
FIG. 2 is a flow chart of a flow coverage prediction method provided by the present invention;
FIG. 3 is a flowchart of a flow coverage prediction method provided by the present invention;
FIG. 4 is a block diagram of a fluidity coverage prediction device provided by the present invention;
FIG. 5 is a block diagram of a data acquisition module provided by the present invention;
FIG. 6 is a block diagram of a machine learning 1 module provided by the present invention;
FIG. 7 is a block diagram of a machine learning 2 module provided by the present invention;
FIG. 8 is a block diagram of a fluidity coverage calculation and prediction module provided by an embodiment of the present invention;
fig. 9 is a block diagram of a service data configuration scheme calculation module provided in an embodiment of the present invention;
FIG. 10 is a block diagram of a component similarity matching module provided by an embodiment of the present invention;
FIG. 11 is a block diagram of a data display module according to an embodiment of the present invention;
fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The following will describe embodiments of the present invention in detail with reference to the drawings and examples, thereby solving the technical problems by applying technical means to the present invention, and realizing the technical effects can be fully understood and implemented accordingly. It should be noted that, as long as no conflict is formed, each embodiment of the present invention and each feature of each embodiment may be combined with each other, and the formed technical solutions are all within the protection scope of the present invention.
Additionally, the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that herein.
Referring to fig. 1A, the method for predicting the flowability coverage according to the present invention includes:
s1011, acquiring service data of each service application server system at a historical moment and mobility coverage rate data at the historical moment;
s1012, training a pre-established machine learning model according to the business data at the historical moment and the mobility coverage rate data at the historical moment to obtain a first training model;
s1013, screening the fluidity coverage rate data and the service data at the corresponding moment according to preset conditions, and training a pre-established machine learning model according to screening results to obtain a second training model;
s1014, obtaining a mobility coverage rate predicted value according to the first training model and predicted business data;
s1015 compares the predicted value with a preset threshold value, and brings the mobility coverage predicted value into the second training model according to the comparison result to obtain a plurality of business data configuration scheme data;
S1016, obtaining a service data adjustment scheme according to the comparison result of the service data configuration scheme data and the predicted service data.
In actual operation, the application principle of the fluidity coverage prediction method is as follows: acquiring service data of each service application server system at a historical moment and mobility coverage rate data at the historical moment; training and calculating a pre-established machine learning model 1 according to the business data at the historical moment and the mobility coverage rate data at the historical moment to obtain a training model 1; according to preset conditions, screening history; training a pre-established machine learning model 2 according to the set conditions, wherein the mobility coverage rate data at the historical moment after screening and the business data at the corresponding moment to obtain a training model 2; calculating prediction mobility coverage rate data by using the trained machine learning model 1; analyzing the obtained mobility coverage rate predicted value, and forming mobility coverage rate early warning according to a preset threshold value; calculating to obtain a plurality of business data configuration scheme data by using the trained machine learning model 2 according to the mobility coverage rate prediction data; respectively carrying out component proportion similarity matching on the plurality of service data configuration scheme data and the predicted service data to obtain similarity of the plurality of service data configuration schemes and the predicted service data; and obtaining a service data adjustment scheme according to the similarity and the user preference.
Referring to fig. 2, in an embodiment of the present invention, training a pre-established machine learning model according to the service data at the historical time and the mobility coverage data at the historical time to obtain a first training model includes:
s201, randomly dividing the characteristic value information and the fluidity coverage rate data under the historical time into K misaligned sub-data sets, taking the K-1 sub-data sets as training sets, and remaining 1 as verification sets;
s202, respectively inputting training sets selected each time into a pre-established machine learning model in an iterative mode, training, and carrying out model learning optimization by using corresponding verification sets; acquiring a first training model through multiple linear regression integrated learning training of a genetic algorithm and a support vector machine regression algorithm; k is a positive integer greater than 1.
Wherein, according to the first training model and the predicted business data, obtaining the mobility coverage rate predicted value comprises: and calculating to obtain a mobility coverage rate predicted value through the feature value information corresponding to the predicted service data and the first training model.
In actual work, the original quantity of historical time business data and historical time fluidity coverage rate data can be randomly divided into K misaligned sub-data sets, K-1 sets are selected as training sets each time, and 1 set is left as verification set. Respectively inputting K-1 selected each time as training sets into a pre-established machine learning model in an iterative mode to perform initial training; and optimizing the initially trained machine learning model by taking the rest 1 data set as a test sample, and generating the trained machine learning model in an iterative mode. Specifically, a genetic algorithm GA model and a support vector machine regression algorithm (SVR) model dual-function model are adopted for the machine learning model. The GA algorithm model is used for obtaining the correspondence at future time Optimal service data configuration scheme corresponding to the predicted value. At a certain time in the GA algorithm model, the characteristic attribute set WP of the banking data represents an individual, wherein P is characteristic data, W is a corresponding weight, and a certain value W in the WP i P i Represents a genetic factor in an individual, i.gtoreq.1. The suitability evaluation function is Fn. Species are classified according to individual similarity (multiple individuals make up one species). And then carrying out objective function confidence evaluation on the individual by the adaptive evaluation function to obtain confidence evaluation. The evolution process of the GA algorithm model is that survival competition is firstly carried out, a certain percentage of individuals are reserved in the same species with the superior and inferior jigs, then object competition is carried out, offspring individuals are bred through inter-species genetic variation, and individuals with high environmental adaptability survive. After multiple iterative evolution, the objective function confidence evaluation is made to be optimal, and the set WP at the moment is the optimal individual genetic combination at the current moment. The regression confidence of the SVR model is taken as a convergence target in the GA model, namely, the regression equation f (x) of the SVR model is an objective function of the GA model. The SVR model is multiple linear regression of a support vector machine regression (SVR) algorithm, and by giving a new input sample x, deducing what output Y corresponds to the new input sample x according to the given data sample, wherein the output Y is a real number. Regression problems can be described in mathematical language as: a given set of data samples is { (x) i ,y i )|x i ∈R n ,y i E R, i=1, 2,3. Find R n The last function f (x), a regression equation is derived to infer the y value for any x input with y=f (x).
In an embodiment of the present invention, according to a comparison result between the service data configuration scheme data and the predicted service data, obtaining the service data adjustment scheme includes: respectively carrying out component proportion similarity matching on the plurality of service data configuration scheme data and the predicted service data to obtain the similarity of each service data configuration scheme data and the predicted service data; and screening the service data configuration scheme data according to the similarity to obtain a service data adjustment scheme meeting preset conditions.
Referring to fig. 3, in an embodiment of the present invention, training a pre-established machine learning model according to a screening result to obtain a second training model includes:
s301, acquiring preference data of a user, screened service data and mobility coverage rate data at historical time, randomly dividing the preference data, the screened service data and the mobility coverage rate data into K misaligned sub-data sets, taking the K-1 sub-data sets as training sets, and taking the left 1 sub-data sets as verification sets;
s302, respectively inputting training sets selected each time into a pre-established machine learning model in an iterative mode, training, and carrying out model learning optimization by using corresponding verification sets; training through a neural network clustering algorithm to obtain a second training model; k is a positive integer greater than 1.
Thus, the step of bringing the fluidity coverage prediction value into the second training model to obtain a plurality of service data configuration scheme data according to the comparison result may further include: and carrying the fluidity coverage rate predicted value and the preference data into the second training model according to the comparison result to obtain a plurality of business data configuration scheme data. Thus, the user's preference is incorporated into the scaling recommendation, further increasing the friendliness and applicability of scaling.
In actual work, the original quantity of historical time business data and historical time fluidity coverage rate data can be randomly divided into K misaligned sub-data sets, K-1 sets are selected as training sets each time, and 1 set is left as verification set. Respectively inputting K-1 selected each time as training sets into a pre-established machine learning model in an iterative mode to perform initial training; and optimizing the initially trained machine learning model by taking the rest 1 data set as a test sample, and generating the trained machine learning model in an iterative mode. Specifically, a neural network clustering algorithm SOM is adopted for the machine learning model to realize data clustering. And the preference degree data of the user, the screened service data and the mobility coverage rate data at the historical time form a high-dimensional data set as an input sample and an output sample of the data sample due to complex data types. And randomly selecting data as an initial value from the output samples, then selecting input vectors in the input samples according to random probability, finding out a weight vector with the smallest distance with the input vectors to define a local optimal set, and then adjusting weights in a region near the local optimal set to draw close to the input vectors. The factors such as the radius of the shrinkage domain, the learning rate, the repetition rate, the user preference and the like are taken as evaluation function factors. And (5) through iterative training, the evaluation is optimal, and the optimal aggregate classification is achieved. The data aggregation classification is a data set. The historical mobility coverage rate data at each moment can obtain an optimal aggregation classification data set, namely an optimal business data configuration scheme corresponding to the mobility coverage rate at the time point. And judging and scoring according to the input flowability coverage rate value and the user preference.
Referring to fig. 1B, in actual operation, the fluidity coverage prediction method provided by the present invention may be implemented as follows:
step S1021: business data of each business application server system at historical moment of the bank and mobility coverage rate data at the historical moment; and screening the business data of each business application server system at the historical moment of the bank, processing the missing data and removing the abnormal data to be used as source data.
Step S1022: and training a pre-established machine learning model 1 according to the business data of each business application server system at the historical moment of the bank and the mobility coverage rate data at the historical moment. The original quantity of historical time business data and historical time fluidity coverage rate data are randomly divided into K misaligned sub-data sets, K-1 sets are selected as training sets each time, and 1 set is left as verification set. Respectively inputting K-1 selected each time as training sets into a pre-established machine learning model in an iterative mode to perform initial training; the remaining 1 data set was used as a test sample to optimize the machine learning model after initial training. And obtaining a mobility coverage prediction model through multiple linear regression integrated learning training of a genetic algorithm and a support vector machine regression algorithm.
Step S1023: and training the pre-established machine learning model 2 according to the set conditions, the mobility coverage rate data at the historical time of the bank after screening and the business data of each business application server system at the corresponding historical time. The service data of the original number of the screened historical moment and the data of the historical moment mobility coverage rate are randomly divided into K misaligned sub-data sets, K-1 sets are selected as training sets each time, and 1 set is left as verification set. Respectively inputting K-1 selected each time as training sets into a pre-established machine learning model in an iterative mode to perform initial training; the remaining 1 data set was used as a test sample to optimize the machine learning model after initial training. And training through a neural network clustering algorithm to obtain a service data configuration scheme calculation model.
Step S1024: and calculating the mobility coverage rate of the predicted future time by using the trained mobility coverage rate prediction model of the machine learning model 1. Carrying out data processing on business data of each business system of the bank to be determined on the mobility coverage rate, and calculating and predicting to obtain the mobility coverage rate of the future time; and carrying out early warning on market risks according to the predicted mobility coverage rate and a preset mobility coverage rate early warning rule.
Step S1025: and carrying out early warning on the risk of the mobility coverage according to the predicted mobility coverage and a preset mobility coverage early warning rule.
Step S1026: and according to the future time fluidity coverage prediction data obtained in the step S1025, calculating and obtaining a plurality of business data configuration scheme data by utilizing the trained machine learning model 2.
Step S1027: and respectively carrying out component proportion similarity matching on the plurality of service data configuration scheme data and the predicted service data to obtain the similarity of the plurality of service data configuration schemes and the predicted service data.
Step S1028: and obtaining a service data adjustment scheme according to the similarity and the user preference.
The present invention also provides a fluidity coverage prediction device, the device comprising: the system comprises a data acquisition module, a training module, a prediction module, a configuration scheme calculation module and a matching module; the data acquisition module is used for acquiring service data of each service application server system at the historical moment and mobility coverage rate data at the historical moment; the training module is used for training a pre-established machine learning model according to the business data at the historical moment and the mobility coverage rate data at the historical moment to obtain a first training model; screening the fluidity coverage rate data and the business data at the corresponding moment according to preset conditions, and training a pre-established machine learning model according to screening results to obtain a second training model; the prediction module is used for obtaining a mobility coverage rate predicted value according to the first training model and predicted service data; comparing the predicted value with a preset threshold value, and providing the mobility coverage predicted value to the second training module according to a comparison result; the configuration scheme calculation module is used for bringing the mobility coverage rate predicted value into the second training model to obtain a plurality of business data configuration scheme data; the matching module is used for obtaining a service data adjustment scheme according to the comparison result of the service data configuration scheme data and the predicted service data.
In the above embodiment, the data acquisition module includes a data processing unit and a feature value extraction unit, where the data processing unit is configured to integrate, screen, process missing data and reject abnormal data of service data of each service system at the historical time of the bank and mobility coverage rate data at the historical time. The characteristic value extraction unit is used for obtaining the initial characteristic value attribute of the fluidity coverage rate prediction data basic service data.
In an embodiment of the present invention, the training module may include a first model training unit, where the first model training unit is configured to randomly divide the feature value information and the mobility coverage rate data at the historical time into K misaligned sub-data sets, and use the K-1 sub-data sets as a training set, and leave 1 as a verification set; respectively inputting each selected training set into a pre-established machine learning model for training in an iterative mode, and carrying out model optimization by using a corresponding verification set; obtaining a first training model through multiple linear regression training of a genetic algorithm and a support vector machine regression algorithm; k is a positive integer greater than 1. Wherein the training module may further comprise a second model training unit: and acquiring preference data of the user, training a pre-established machine learning model according to the preference data and the screening result, and training through a neural network clustering algorithm to acquire a second training model.
In an embodiment of the present invention, the configuration scheme calculation module further includes: and carrying the fluidity coverage rate predicted value and the preference data into the second training model according to the comparison result to obtain a plurality of business data configuration scheme data.
For the sake of more clear description, the fluidity coverage prediction device provided by the present invention will be further described with reference to the accompanying drawings, and those skilled in the art will recognize that this example is only for aiding in understanding the fluidity coverage prediction device provided by the present invention, and is not limited thereto.
Referring to fig. 4, the fluidity coverage prediction device provided by the present invention may include a data acquisition module 201, a machine learning 1 module 202, a machine learning 2 module 203, a fluidity coverage calculation module 204, a prediction module fluidity coverage early warning module 205, a service data configuration scheme calculation module 206, a component similarity matching module 207, and a data display module 208. The machine learning 1 module 202 and the machine learning 2 module 203 are training modules, the mobility coverage calculating module 204 and the prediction module mobility coverage early warning module 205 are prediction modules, the service data configuration scheme calculating module 206 is a configuration scheme calculating module, and the component similarity matching module 207 and the data display module 208 are matching modules.
Referring to fig. 1B in combination, the data obtaining module 201 is mainly configured to perform the above step S1021, that is, obtain service data of each service application server system at a historical time of a bank and mobility coverage data at the historical time.
Referring specifically to fig. 5, the device includes a data acquisition unit 11, a data processing unit 12, a feature value acquisition unit 13, and a data transmission unit 14.
A data acquisition unit 11: and receiving and loading service data of various service systems of the bank. Such as deposit system business data, loan system business data, air control system business data, clearing system business data, financial market system business data, etc., form a large data set as source data of the machine learning 1 module.
A data processing unit 12: and integrating, screening, processing missing data and eliminating abnormal data of the business data of each business system at the historical moment of the bank and the mobility coverage rate data at the historical moment. And carding the missing data of the system, and if the missing condition exists, carrying out interpolation processing according to the date data before and after. The interpolation mode can be linear interpolation, i.e. the characteristic value of the current day is taken as the average value of the data of the previous and subsequent days, i.e. x i-1 、x i+1 X is data of two days before and after i Is the data of the same day; if the characteristic value of a certain date increases or decreases sharply, for example, the characteristic value is greatly different from a certain range before and after, the characteristic value is considered to be caused by an emergency or an accidental event, and the part of data is rejected.
The feature value acquisition unit 13: the initial feature values are imported, as shown in table 1 below, as an initial feature value attribute and a corresponding attribute value table of the streaming coverage rate prediction data base service data in this embodiment.
TABLE 1
The characterization data known from flow coverage = qualifying premium mobile asset/future 30 day cash net outflow 100% includes two, one of which is qualifying premium mobile asset business data, denoted by P1The characteristic data set is W1P1 when the characteristic data is the characteristic value data of the qualified high-quality mobile asset business data and the weight is represented by W1; the other is cash net outflow service data of a future period, cash net outflow=cash outflow-cash inflow, P2 is characteristic value data which is cash net outflow service data of a future period, W2 is a weight, and the characteristic data set is W2P2. The eigenvalue vector of the flowability coverage is expressed as
The data transmission unit 14: and the processing module is used for transmitting the characteristic value data obtained by processing to the next module.
Referring to fig. 1B in combination, the machine learning 1 module 202 is mainly configured to perform the step S1022, that is, training the machine learning model 1 built in advance according to the service data of each service application server system at the historical moment of the bank and the mobility coverage rate data at the historical moment, and may specifically include a feature value importing unit, a historical data importing unit, and a model training unit to build a learning model.
As shown in fig. 6, the characteristic value data importing unit 21: the feature value is imported, and feature value data obtained by the device module 201 corresponding to step S1021 is imported.
History result data importing unit 22: the historical time flowability coverage rate data is used as a training set of machine learning, and the historical flowability coverage rate result data is matched according to different time points of the characteristic value data.
Machine learning model 1 training unit 23: the machine learning model 1 is a mobility coverage prediction model, and in this case, adopts a genetic algorithm GA model and a support vector machine regression algorithm (SVR) model dual-function model. The GA algorithm model is used for obtaining an optimal service data configuration scheme corresponding to the corresponding predicted value at the future moment. At a certain time in the GA algorithm model, the characteristic attribute set WP of the banking data represents an individual, wherein P is characteristic data, W is a corresponding weight, and a certain value W in the WP i P i Represents one of the genetic factors, i.gtoreq.1.The suitability evaluation function is Fn. Species are classified according to individual similarity (multiple individuals make up one species). And then carrying out objective function confidence evaluation on the individual by the adaptive evaluation function to obtain confidence evaluation. The evolution process of the GA algorithm model is that survival competition is firstly carried out, a certain percentage of individuals are reserved in the same species with the superior and inferior jigs, then object competition is carried out, offspring individuals are bred through inter-species genetic variation, and individuals with high environmental adaptability survive. After multiple iterative evolution, the objective function confidence evaluation is made to be optimal, and the set WP at the moment is the optimal individual genetic combination at the current moment. Regression confidence of SVR model is used as convergence target in GA model. I.e. the regression equation f (x) of the SVR model is the objective function of the GA model. The SVR model is multiple linear regression of a support vector machine regression (SVR) algorithm, and by giving a new input sample x, deducing what output Y corresponds to the new input sample x according to the given data sample, wherein the output Y is a real number. Regression problems can be described in mathematical language as: a given set of data samples is { (x) i ,y i )|x i ∈R n ,y i E R, i=1, 2,3. Find R n The last function f (x), a regression equation is derived to infer the y value for any x input with y=f (x).
Prediction result analysis unit 24: the prediction data and the historical fluidity coverage data obtained by the machine learning model 1 training unit 23 are imported into the historical result data importing unit 22 as samples. And randomly dividing the total quantity of historical time business data and historical time fluidity coverage rate data into K misaligned sub-data sets, selecting each K-1 data set as a training sample each time, and leaving 1 data set as a verification sample. And obtaining a regression equation by adjusting the iteration times and the test sample deviation.
The mobility coverage calculating module 204 and the prediction module mobility coverage early warning module 205 correspond to steps S1024 and S1025 in fig. 1B, where the mobility coverage predicting module is configured to predict mobility coverage data by using the trained machine learning model 1, and includes a data importing unit, a mobility coverage calculating and predicting processing unit, a mobility coverage early warning unit, and a data transmitting unit, and refer to fig. 8 specifically.
The data importing unit 41 imports business data of each business system of the bank to be calculated and predicted for the mobility coverage.
The mobility coverage calculation and prediction processing unit 42 loads data of the banking business data server every day by the system, forms a characteristic value sample, and is used for calculating and predicting mobility coverage by using the trained machine learning model 1, predicting mobility coverage of each banking business system business data to be calculated and predicted mobility coverage, and generating mobility coverage in future time;
the prediction management unit 43 is used for configuring early warning parameters and early warning models through the fluidity coverage rate calculation. Invoking a machine learning server through the mobility coverage calculation prediction processing module 42 to generate future mobility coverage data, and performing early warning according to the future data; the early warning model is used for generating early warning of excessively low mobility coverage rate when the mobility coverage rate is lower than a lower limit by setting a mobility coverage rate early warning threshold (for example, the mobility coverage rate of the supervision requirement cannot be lower than 100%) as the lower limit.
The user sets the mobility coverage rate early warning parameter, early warning reminding mode and frequency through the mobility coverage rate early warning unit 44, and early warning (such as highlighting, short message or mail reminding mode) is carried out on the predicted data reaching the early warning threshold value.
The mobility coverage prediction data structure may be as follows:
The data transmission unit 45 transmits the prediction data and the early warning information of the future mobility coverage rate, and transmits the information to the later module for processing.
The machine learning 2 module 203 is similar to the machine learning 1 module 202, and is configured to train the machine learning model 2 built in advance according to the service data of each service application server system at the history time of the bank after the screening and the mobility coverage rate data at the history time, and includes a feature value importing unit, a history data importing unit, a model training unit, a learning model building unit, and a transmitting unit for transmitting data to the next module.
Referring specifically to fig. 7, the characteristic value data importing unit 31 imports the characteristic value data obtained as described above as a data characteristic value.
History result data importing unit 32: the filtered historical time mobility coverage rate data is used as a training set of machine learning, and the filtered historical mobility coverage rate result data is matched aiming at different time points of the characteristic value data.
Machine learning model 2 training unit 33: data classification training: the characteristic value data input 31 and the historical time flowability coverage data input 32 are used as samples. And classifying the data in a clustering mode to realize the optimal aggregation of the data of each business product. In the case, a neural network clustering algorithm SOM is adopted to realize product data clustering, and the aggregation process can be described as follows: the resulting large dataset in block 202 forms a high-dimensional dataset due to the complexity of the data types, as input and output samples for the data samples. And randomly selecting data as an initial value from the output samples, then selecting input vectors in the input samples according to random probability, finding out a weight vector with the smallest distance with the input vectors to define a local optimal set, and then adjusting weights in a region near the local optimal set to draw close to the input vectors. The factors such as the radius of the shrinkage domain, the learning rate, the repetition rate, the user preference and the like are taken as evaluation function factors. And performing model training by iteratively repeating the above processes to optimize the evaluation, and classifying the optimal aggregation at the moment. Data future prediction training: the data aggregate classification is a data set. The historical mobility coverage rate data at each moment can obtain an optimal aggregation classification data set, namely an optimal business data configuration scheme corresponding to the mobility coverage rate at the time point. Evaluation scores are based on the entered flow coverage value (i.e., corresponding to the flow coverage prediction value reached in block 205), and user preference. In general, the greater the value, the higher the score, the fluidity coverage data at the historical time is compared with the fluidity coverage data input; the higher the user's preference for the optimal aggregate classification, the higher the score.
The result transmission unit 34: the prediction data and the historical fluidity coverage data obtained by the foregoing were imported as samples. And randomly dividing the total quantity of historical time business data and historical time fluidity coverage rate data into K misaligned sub-data sets, selecting each K-1 data set as a training sample each time, and leaving 1 data set as a verification sample. And obtaining an optimal configuration scheme by adjusting the calculation times and the deviation of the test sample.
The service data configuration scheme calculation module 206 is mainly configured to calculate and obtain a plurality of service data configuration scheme data according to the obtained future time flowability coverage prediction data by using the trained machine learning model 2. Referring specifically to fig. 9, the system includes a data importing unit 51, a service data configuration scheme calculating unit 52, and a data transmitting unit 53; the data importing unit 51 is configured to receive the data output by the mobility coverage early warning module 205, compute the data of the plurality of service data configuration schemes by the service data configuration scheme computing unit 52, and provide the data to the data transmitting unit 53 to be forwarded to the component similarity matching module 207.
The component similarity matching module 207 is mainly configured to perform component proportion similarity matching on the plurality of service data configuration scheme data and the predicted service data respectively, so as to obtain similarity between the plurality of service data configuration schemes and the predicted service data; as shown in fig. 10, the method may include:
The data importing unit 71: and receiving the data transmitted by the lead-in previous module. Component ratio similarity matching unit 72: and carrying out service component analysis on the configuration scheme to obtain a future configuration scheme and carrying out similarity analysis on the configuration schemes obtained by the previous module to obtain a similarity grading value. A data transmission unit 73: and transmitting the obtained similarity data and the corresponding service configuration scheme to a next module.
Referring to fig. 11, the data display module 208 is mainly configured to obtain a service data adjustment scheme according to the similarity and the user preference, and may specifically include:
a data importing unit 81: and receiving the data transmitted by the previous module.
The user interaction unit 82: the method is used for interacting with a user, obtaining the preference degree of the user for the service data configuration scheme, and feeding back the preference degree to the machine learning 2 model for making a subsequent data training sample.
Service data configuration adjustment scheme display unit 83: and comparing the obtained optimal configuration scheme with a configuration scheme at the future time to obtain a business data configuration adjustment scheme, and displaying the business data configuration adjustment scheme.
The beneficial technical effects of the invention are as follows: on one hand, the mobility coverage rate target can be determined according to the self operation condition, on the other hand, when the actual mobility coverage rate deviates from the target, adjustment measures can be actively taken to reduce the deviation degree, and the resistance of mobility coverage rate risks under external impact is improved; moreover, through prediction of the mobility coverage rate, the probability that the mobility coverage rate does not reach the standard is reduced; the mentioned fluidity coverage prediction can also provide a certain research reference for the fluidity coverage of other emerging markets, and has wide application range and high portability.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above method when executing the computer program.
The present invention also provides a computer readable storage medium storing a computer program for executing the above method.
As shown in fig. 12, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processing unit 130, a display 160, a power supply 170. It is noted that the electronic device 600 need not include all of the components shown in fig. 12; in addition, the electronic device 600 may further include components not shown in fig. 12, to which reference is made to the related art.
As shown in fig. 12, the central processor 100, also sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 100 receives inputs and controls the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 100 can execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides an input to the central processor 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, or the like. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. Memory 140 may also be some other type of device. Memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage 142, the application/function storage 142 for storing application programs and function programs or a flow for executing operations of the electronic device 600 by the central processor 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. A communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and to receive audio input from the microphone 132 to implement usual telecommunication functions. The audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 130 is also coupled to the central processor 100 so that sound can be recorded locally through the microphone 132 and so that sound stored locally can be played through the speaker 131.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (14)
1. A method of fluidity coverage prediction, the method comprising:
acquiring service data of each service application server system at a historical moment and mobility coverage rate data at the historical moment;
training a pre-established machine learning model according to the business data at the historical moment and the mobility coverage rate data at the historical moment to obtain a first training model;
Screening the fluidity coverage rate data and the business data at the corresponding moment according to preset conditions, and training a pre-established machine learning model according to screening results to obtain a second training model;
obtaining a mobility coverage rate predicted value according to the first training model and predicted service data;
comparing the predicted value with a preset threshold value, and carrying the fluidity coverage rate predicted value into the second training model according to a comparison result to obtain a plurality of business data configuration scheme data;
and obtaining a service data adjustment scheme according to the comparison result of the service data configuration scheme data and the predicted service data.
2. The mobility coverage prediction method according to claim 1, wherein acquiring the traffic data of each traffic application server system at the historic time and the mobility coverage data at the historic time comprises:
and screening out abnormal data in the service data to obtain the preprocessed service data serving as source data.
3. The mobility coverage prediction method according to claim 1, wherein training a machine learning model established in advance according to the business data at the historical moment and the mobility coverage data at the historical moment, to obtain a first training model comprises:
Randomly dividing the preprocessed business data and the mobility coverage rate data under the historical time into K misaligned sub-data sets, taking the K-1 sub-data sets as training sets, and taking the remaining 1 sub-data sets as verification sets;
respectively inputting each selected training set into a pre-established machine learning model in an iterative mode, training, and carrying out model learning optimization by using a corresponding verification set;
acquiring a first training model through multiple linear regression integrated learning training of a genetic algorithm and a support vector machine regression algorithm;
k is a positive integer greater than 1.
4. The mobility coverage prediction method according to claim 1, wherein obtaining a mobility coverage prediction value from the first training model and predicted traffic data comprises:
and calculating to obtain a mobility coverage rate predicted value through the feature value information corresponding to the predicted service data and the first training model.
5. The mobility coverage prediction method according to claim 1, wherein obtaining a traffic data adjustment scheme according to a comparison result of the traffic data configuration scheme data and the predicted traffic data comprises:
respectively carrying out component proportion similarity matching on the plurality of service data configuration scheme data and the predicted service data to obtain the similarity of each service data configuration scheme data and the predicted service data;
And screening the service data configuration scheme data according to the similarity to obtain a service data adjustment scheme meeting preset conditions.
6. The mobility coverage prediction method according to claim 1, wherein training a pre-established machine learning model according to the screening result further comprises: and acquiring preference data of the user, and training a pre-established machine learning model according to the preference data and the screening result to acquire a second training model.
7. The method of claim 6, wherein the step of bringing the fluidity coverage prediction value into the second training model to obtain a plurality of service data configuration scheme data according to the comparison result further comprises: and carrying the fluidity coverage rate predicted value and the preference data into the second training model according to the comparison result to obtain a plurality of business data configuration scheme data.
8. A fluidity coverage prediction device, characterized in that the device comprises: the system comprises a data acquisition module, a training module, a prediction module, a configuration scheme calculation module and a matching module;
the data acquisition module is used for acquiring service data of each service application server system at the historical moment and mobility coverage rate data at the historical moment;
The training module is used for training a pre-established machine learning model according to the business data at the historical moment and the mobility coverage rate data at the historical moment to obtain a first training model; screening the fluidity coverage rate data and the business data at the corresponding moment according to preset conditions, and training a pre-established machine learning model according to screening results to obtain a second training model;
the prediction module is used for obtaining a mobility coverage rate predicted value according to the first training model and predicted service data; comparing the predicted value with a preset threshold value, and providing the fluidity coverage predicted value to the second training model according to a comparison result;
the configuration scheme calculation module is used for bringing the mobility coverage rate predicted value into the second training model to obtain a plurality of business data configuration scheme data;
the matching module is used for obtaining a service data adjustment scheme according to the comparison result of the service data configuration scheme data and the predicted service data.
9. The fluidity coverage prediction device according to claim 8, wherein the data acquisition module comprises a data processing unit and a characteristic value extraction unit, and the data processing unit is used for integrating, screening, missing data processing and abnormal data removing the service data of each service system at the historical moment and the fluidity coverage data at the historical moment to obtain the preprocessed service data; the characteristic value extraction unit is used for obtaining initial characteristic value attribute information of the fluidity coverage rate prediction data basic service data.
10. The fluidity coverage prediction device according to claim 9, wherein the training module comprises a first model training unit, the first model training unit is configured to randomly divide fluidity coverage data under characteristic value information and historical time into K misaligned sub-data sets, and K-1 sub-data sets are used as training sets, and the remaining 1 is used as verification set; respectively inputting each selected training set into a pre-established machine learning model for training in an iterative mode, and carrying out model learning optimization by using a corresponding verification set; acquiring a first training model through multiple linear regression integrated learning training of a genetic algorithm and a support vector machine regression algorithm;
k is a positive integer greater than 1.
11. The flow coverage prediction device of claim 8, wherein the training module further comprises:
and acquiring preference data of the user, training a pre-established machine learning model according to the preference data and the screening result, and learning and training through a neural network clustering algorithm to acquire a second training model.
12. The flow coverage prediction apparatus of claim 11, wherein the configuration scheme calculation module further comprises: and carrying the fluidity coverage rate predicted value and the preference data into the second training model according to the comparison result to obtain a plurality of business data configuration scheme data.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 7 when executing the computer program.
14. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the method of any one of claims 1 to 7.
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