CN111783487A - Fault early warning method and device for card reader equipment - Google Patents

Fault early warning method and device for card reader equipment Download PDF

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CN111783487A
CN111783487A CN202010585023.9A CN202010585023A CN111783487A CN 111783487 A CN111783487 A CN 111783487A CN 202010585023 A CN202010585023 A CN 202010585023A CN 111783487 A CN111783487 A CN 111783487A
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reading module
card reading
data
predicted
training
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CN111783487B (en
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高伟
郑广斌
钟春彬
杨洁琼
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/0095—Testing the sensing arrangement, e.g. testing if a magnetic card reader, bar code reader, RFID interrogator or smart card reader functions properly
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound

Abstract

The invention provides a fault early warning method and a fault early warning device for card reader equipment, wherein the method comprises the following steps: acquiring prediction data of each card reading module of the card reader equipment; performing characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module; obtaining the predicted use times of each card reading module according to the predicted characteristic data of each card reading module and the use time prediction model of each card reading module; obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module; and if the predicted accumulated use times of the card reading module are judged to be larger than the corresponding use threshold, sending out fault early warning information. The device is used for executing the method. The fault early warning method and the fault early warning device for the card reader equipment, provided by the embodiment of the invention, improve the reliability of the card reader equipment.

Description

Fault early warning method and device for card reader equipment
Technical Field
The invention relates to the technical field of equipment maintenance, in particular to a fault early warning method and device for card reader equipment.
Background
The card reader device can be configured on the counter of a bank outlet and used for reading information of a bank card and an identity card.
In the prior art, a maintenance mode of a card reader device used by a counter of a bank outlet is fault maintenance, namely, the card reader device is maintained when the card reader device fails, but the card reader device is maintained when the card reader device fails, so that the use of the card reader device is influenced, time is required for replacement and maintenance of the card reader device, service cannot be provided for a customer within a certain time, the handling of customer service is influenced, and customer service experience of the bank outlet is influenced.
Disclosure of Invention
To solve the problems in the prior art, embodiments of the present invention provide a method and an apparatus for early warning a failure of a card reader device, which can at least partially solve the problems in the prior art.
On one hand, the invention provides a fault early warning method for card reader equipment, which comprises the following steps:
acquiring prediction data of each card reading module of the card reader equipment;
performing characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module;
obtaining the predicted use times of each card reading module according to the predicted characteristic data of each card reading module and the use time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module;
obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module;
and if the predicted accumulated use times of the card reading module are judged to be larger than the corresponding use threshold, sending out fault early warning information.
In another aspect, the present invention provides a fault warning device for a card reader device, including:
the prediction data acquisition unit is used for acquiring prediction data of each card reading module of the card reader equipment;
the first characteristic processing unit is used for carrying out characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module;
the prediction unit is used for obtaining the predicted using times of each card reading module according to the predicted characteristic data of each card reading module and the using time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module;
the obtaining unit is used for obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module;
and the judging unit is used for sending out fault early warning information after judging that the predicted accumulated use times of the card reading module is larger than the corresponding use threshold.
In another aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method for early warning a failure of a card reader device according to any of the embodiments.
In yet another aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the fault pre-warning method of the card reader device according to any one of the above embodiments.
The method and the device for early warning the fault of the card reader equipment can acquire the prediction data of each card reading module of the card reader equipment, perform characteristic processing on the prediction data of each card reading module to acquire the prediction characteristic data of each card reading module, acquire the prediction using times of each card reading module according to the prediction characteristic data of each card reading module and the using time prediction model of each card reading module, acquire the prediction accumulated using times of each card reading module according to the prediction using times and the current accumulated using times of each card reading module, send out fault early warning information after judging that the prediction accumulated using times of the card reading modules are larger than the corresponding using threshold value, perform fault early warning before the service lives of the card reading modules expire to take actions in advance to maintain or replace the card reading modules, and realize the active maintenance of the card reader equipment, the reliability of the card reader device is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a schematic flowchart of a fault early warning method for a card reader device according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating a fault warning method for a card reader device according to another embodiment of the present invention.
Fig. 3 is a flowchart illustrating a fault warning method for a card reader device according to another embodiment of the present invention.
Fig. 4 is a flowchart illustrating a fault warning method for a card reader device according to still another embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a fault warning apparatus of a card reader device according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of a fault warning apparatus of a card reader device according to another embodiment of the present invention.
Fig. 7 is a schematic structural diagram of a fault warning apparatus of a card reader device according to still another embodiment of the present invention.
Fig. 8 is a schematic structural diagram of a fault warning apparatus of a card reader device according to still another embodiment of the present invention.
Fig. 9 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
Fig. 1 is a schematic flow chart of a fault early warning method for a card reader device according to an embodiment of the present invention, and as shown in fig. 1, the fault early warning method for a card reader device according to the embodiment of the present invention includes:
s101, obtaining prediction data of each card reading module of the card reader equipment;
specifically, by preprocessing the history data of the past first preset time period of each card reading module of the card reader device, the prediction data of each card reading module can be obtained, and the server can obtain the prediction data of each card reading module of the card reader device. The card reading module can be any one of a magnetic stripe type reading and writing module, a contact type IC card reading and writing module, a radio frequency IC card reading and writing module and an identity card reading module, and the card reader equipment comprises at least one card reading module. The historical record data of each card reading module may include device data, periodic data, salary and social security data, holiday data, usage times statistical data, contemporaneous usage times data, current usage times data, average usage times data, cycle usage times data, and the like, and is set according to actual needs, which is not limited in the embodiments of the present invention. The first preset time period is, for example, 3 months, and is set according to actual needs, which is not limited in the embodiment of the present invention. The executing subject of the fault early warning method of the card reader device provided by the embodiment of the invention includes but is not limited to a server.
For example, historical data of each card reading module of the card reader device can be acquired by an internal system of a bank or a web crawler. The device data may include information about the location of the home, the home network site, the number of card reader devices in the network site, the usage window (for public or private), etc. The periodic data may include month, beginning/middle/end of month, number of days of the month, week number of the month, day of the week, etc. information. The salary and social security data may include information such as whether it is a salary day, whether it is salary for the first N days, whether it is salary for the next N days, whether it is a social security day, whether it is social security for the first N days, whether it is social security for the next N days, etc. The holiday data includes information on whether it is a holiday, whether several days in the previous N days are holidays, and whether several days in the next N days are holidays. The usage statistics may include information such as a maximum of the number of uses on the first N days, a minimum of the number of uses in the first N days, an average of the number of uses on the first N days, a median of the number of uses on the first N days, and a variance of the number of uses on the first N days. The data of the number of usage times in the same period can comprise the number of usage times in the same day in the last week, the number of usage times in the same day in the last month, the number of usage times in the same period in the last year and the like. The past usage data may include yesterday usage, previous day usage, previous three days usage, previous four days usage, and the like. The average usage data may include information such as average usage of previous week, average usage of previous month, average usage of same week of previous month, etc. The ring ratio usage times data may include information such as a day-to-ring ratio for the first N days, a week-to-ring ratio for the first N days, a month-to-ring ratio for the first N days, and a year-to-ring ratio for the first N days. In order to predict the predicted use times of the card reading module in the next day, the value range of N is greater than or equal to 1 and less than or equal to 7, and the value range is set according to the actual situation, which is not limited in the embodiment of the invention.
For example, preprocessing the history data for each card reading module may include correcting inconsistencies in the history data by filling in missing values, smoothing noise, and identifying outliers. When filling missing values, filling by adopting a mean value and a median; when the noise data and the outliers are processed, the noise data are determined through the upper edge and the lower edge of a box line graph, the outliers are detected through a clustering algorithm, then the noise data and the outliers are labeled according to specific business experience, and finally the average values of the previous period and the later period are calculated to repair the noise data and the outliers.
The preprocessing of the history data of each card reading module may comprise integrating the history data. Because the data acquired by the system has various sources, and attributes representing the same concept may have different names or units in different data sources, which may cause data inconsistency and redundancy, in the embodiment of the present invention, a method of correlation analysis may be adopted to integrate the data.
Preprocessing the history data of each card reading module can include performing a specification on the history data of each card reading module. Simplified representation of data can be obtained through reduction technology, the occupied space of the simplified data becomes smaller, but approximately the same analysis result can be generated, and the data processing efficiency of the fault early warning method of the card reader equipment can be improved.
The preprocessing of the historical record data of each card reading module can comprise data transformation of the historical record data of each card reading module, and the historical record data of each card reading module is more suitable for data mining through the data transformation. For example, the conversion of the geographical position information classifies the geographical position information, and the same category uses the same number to represent, so that the text data is converted into discrete numerical data.
S102, performing characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module;
specifically, after obtaining the prediction data of each card reading module, the server may perform feature processing on the prediction data of each card reading module to obtain the prediction feature data of each card reading module.
For example, characterizing the prediction data for each card reading module may include feature construction. The feature construction is used for constructing different types of features from the prediction data of each card reading module, the device features can be constructed based on the device data, the period features can be constructed based on the periodic data, the salary starting social security features can be constructed based on the salary sending and social security data, the holiday saving features can be constructed based on the holiday data, the use times statistical features can be constructed based on the use times statistical data, the use times in the same period can be constructed based on the use times in the same period, the use times in the later period can be constructed based on the use times in the later period, the average use features can be constructed based on the average use times data, and the use times in the ring ratio can be constructed based on the use times in the ring ratio.
S103, obtaining the predicted using times of each card reading module according to the predicted characteristic data of each card reading module and the using time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module;
specifically, after obtaining the predicted feature data of each card reading module, the server may input the predicted feature data of each card reading module into the usage number prediction model of each card reading module, and output the predicted usage number of each card reading module through the processing of the usage number prediction model of each card reading module. The use number prediction model of each card reading module comprises a set number of prediction submodels, and the set number of prediction submodels are obtained according to the use number training data and the historical use number training of each card reading module. The set number is set according to actual conditions, and the embodiment of the invention is not limited.
S104, obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module;
specifically, after obtaining the predicted number of usage times of each card reading module, the server may further obtain the current accumulated number of usage times of each card reading module, where each card reading module is recorded during usage, and the current accumulated number of usage times of each card reading module may be obtained through statistics. For each card reading module, the server calculates the sum of the predicted use times and the current accumulated use times of the card reading module, and the sum result is used as the predicted accumulated use times of the card reading module. The current accumulated use times of the card reading module refers to the total use times of the card reading module from the beginning of use to the time of use prediction.
And S105, if the fact that the predicted accumulated use times of the card reading module are larger than the corresponding use threshold value is judged and known, sending out fault early warning information.
Specifically, after obtaining the predicted accumulated use times of each card reading module, the server compares the predicted accumulated use times of the card reading module with the use threshold corresponding to the card reading module for each card reading module, and if the predicted accumulated use times of the card reading module is greater than the use threshold corresponding to the card reading module, it is indicated that the card reading module is about to malfunction, the server may send out malfunction early warning information, so that a worker can perform operation and maintenance preparation work. And if the predicted accumulated use times of the card reading module is less than or equal to the use threshold corresponding to the card reading module, the card reading module can still be used without maintenance. The use threshold corresponding to each card reading module is set according to actual conditions, and the embodiment of the invention is not limited. It can be understood that the predicted accumulated use times of any card reading module of the card reader device is greater than the corresponding use threshold, fault early warning information can be sent out, and the card reading module needing to be maintained can be indicated in the fault early warning information.
For example, the card reading module is a magnetic stripe type read-write module, which is expected to be able to be refreshed 2000 times, that is, the usage threshold corresponding to the magnetic stripe type read-write module is 2000 times, if the server obtains that the predicted usage number of the next magnetic stripe type read-write module is 300 times and obtains that the current accumulated usage number of the magnetic stripe type read-write module is 1800 times, the server calculates that the predicted accumulated usage number of the magnetic stripe type read-write module is 1800+300 times to 2100 times, because the predicted accumulated usage number 2100 of the magnetic stripe type read-write module is greater than the usage threshold 2000 corresponding to the magnetic stripe type read-write module, the server may send out failure warning information, for example, to notify a worker in a manner of a mobile phone short message, and the magnetic stripe type read-write module may reach the.
The fault early warning method of the card reader equipment provided by the embodiment of the invention can acquire the prediction data of each card reading module of the card reader equipment, perform characteristic processing on the prediction data of each card reading module to acquire the prediction characteristic data of each card reading module, acquire the prediction using times of each card reading module according to the prediction characteristic data of each card reading module and the using time prediction model of each card reading module, acquire the prediction accumulated using times of each card reading module according to the prediction using times and the current accumulated using times of each card reading module, send out fault early warning information after judging that the prediction accumulated using times of the card reading module is larger than the corresponding using threshold value, perform fault early warning before the service life of the card reading module expires so as to take action in advance to maintain or replace the card reading module and realize active maintenance of the card reader equipment, the reliability of the card reader device is improved. In addition, interruption of business handling caused by the failure of the card reader equipment can be reduced, and the influence on customer service is reduced.
Fig. 2 is a schematic flow chart of a fault warning method for a card reader device according to another embodiment of the present invention, and as shown in fig. 2, on the basis of the foregoing embodiments, further, the obtaining the predicted number of usage times of each card reading module according to the predicted feature data of each card reading module and the usage time prediction model of each card reading module includes:
s1031, obtaining the number of use times of the card reading module according to the predicted characteristic data of the card reading module and the set number of prediction submodels;
specifically, the server inputs the predicted feature data of the card reading module into each prediction submodel, and may output the number of times of use of the card reading module in each prediction submodel, where the number of the prediction submodels is set, and the server may obtain the number of times of use of the card reading module in the set number.
S1032, obtaining the predicted using times of the card reading module by the set number of using times of the card reading module and the weight corresponding to each using time; and the weight corresponding to each use frequency of the card reading module is obtained in advance.
Specifically, after obtaining the set number of usage times of the card reading module, the server calculates the predicted usage times of the card reading module according to the set number of usage times of the card reading module and the weight corresponding to each usage time. The weight corresponding to each usage number is obtained in advance, for example, set in advance or generated dynamically.
For example, the card reading module is an identity card reading module, and the server obtains n usage times of the identity card reading module according to a formula
Figure BDA0002554327930000071
Calculating and obtaining the predicted use times P of the identity card reading module, wherein QiFor the ith number of uses of the ID card reading module, kiIs QiCorresponding weights, i is a positive integer and i is less than n.
The weight corresponding to each use number of each card reading module can be generated in advance according to the number of the predictor models and the weight vector generation algorithm. A first step, given N and H, where N is the number of predictor models, i.e. a set number, 1/H represents the granularity of weight change, and the set M ═ 1,1,1,. 1,1, and the set M contains H1 in total; secondly, dividing 1 in the set M into N groups by using an interpolation method, and obtaining a common matrix by a permutation and combination idea
Figure BDA0002554327930000072
A seed distribution mode; thirdly, adding 1 of each group in each distribution mode and dividing by H to obtain the total
Figure BDA0002554327930000073
A set of evenly distributed weight vectors.
For example, in the first step, assume that N-2 and H-3, i.e., the number of predictor models is 2, and the granularity of weight change is 3. And secondly, obtaining 4 allocation modes of zero 1, three 1, two 1, and zero 1 according to a null insertion method in the arrangement idea. Thirdly, 1 in each allocation mode is added to obtain { {0,3}, {1,2}, {2,1}, {3,0} }, and a weight vector set obtained by dividing H ═ 3 respectively is as follows: { {0,1}, {1/3,2/3}, {2/3,1/3}, {1,0} }, four sets of weights.
After the vector set of weights is obtained, the effect of each predictor model on each group of weight vectors in the weight vector set can be evaluated according to the verification set of the card reading module and the historical use times corresponding to the verification set, and an optimal group of weights is selected. Firstly, obtaining the use times corresponding to each predictor model according to the verification set and each predictor model; then, calculating the combined use times of the card reading module under each group of weights according to the use times corresponding to each predictor model and each group of weights; and finally, calculating the RMSE value of the combined use times and the historical use times corresponding to each group of weights by using an RMSE (standard error) method, namely calculating the sum of the root mean square error of the combined use times and the historical use times of the card reading module in the verification set for each group of weights, wherein the group of weights with the minimum RMSE value is the optimal weight vector. Wherein, the acquisition process of the verification set of the card reading module is described below.
Fig. 3 is a flowchart of a fault warning method for a card reader device according to yet another embodiment of the present invention, and as shown in fig. 3, on the basis of the foregoing embodiments, further obtaining a usage prediction model of each card reader module according to the usage training data and the historical usage training of each card reader module includes:
s301, acquiring use frequency training data and historical use frequency of the card reading module;
specifically, the use number training data of each card reading module of the card reader device can be obtained by preprocessing the historical record data of the past second preset time period of each card reading module. The historical use times can be the use times of each card reading module in the past second preset time period every day, and are set according to actual needs, and the embodiment of the invention is not limited. The server can obtain the use times training data and the historical use times of the card reading module. The second preset time period is, for example, 3 years, and is set according to actual needs, which is not limited in the embodiment of the present invention. Understandably, if the historical use times are the use times of each day, the use time prediction model of the card reading module obtained by training is used for predicting the use times of the next day of the card reading module; if the historical usage is every three days, then training the obtained usage prediction model of the card-reading module predicts the usage of the card-reading module for the next three days.
S302, obtaining the use times characteristic data of the card reading module according to the use times training data of the card reading module;
specifically, after obtaining the use number training data of the card reading module, the server may perform feature processing on the use number training data of the card reading module to obtain the use number feature data of the card reading module.
For example, different types of features are constructed from the use time training data of the card reading module, the device features can be constructed based on the device data, the cycle features can be constructed based on the periodic data, the departure salary social security features can be constructed based on the salary and social security data, the holiday features can be constructed based on the holiday data, the use time statistical features can be constructed based on the use time statistical data, the contemporaneous use features can be constructed based on the contemporaneous use time data, the current use features can be constructed based on the current use time data, the average use features can be constructed based on the average use time data, and the ring ratio use features can be constructed based on the ring ratio use time data.
S303, dividing the use frequency characteristic data of the card reading module into a training set and a verification set;
specifically, after obtaining the feature data of the number of times of use of the card reading module, the server may divide the feature data of the number of times of use of the card reading module into a training set and a verification set, where the training set is used for model training and the verification set is used for model verification.
For example, the training set accounts for 80% of the usage times characteristic data of the card reading module, the verification set accounts for 20% of the usage times characteristic data of the card reading module, and the training set and the verification set do not overlap.
S304, training to obtain a preset number of to-be-determined prediction sub-models according to the training set, the historical use times corresponding to the training set and a preset number of preset models; wherein the preset number is greater than or equal to the set number;
specifically, the server performs model training on each preset model according to the training set and the historical use times corresponding to the training set to obtain each to-be-determined prediction sub-model. The number of the to-be-determined predictor models is preset, and the preset to-be-determined predictor models can be obtained. Wherein the preset number is greater than or equal to the set number. The preset number is set according to actual needs, and the embodiment of the invention is not limited. The preset model includes, but is not limited to, a support vector machine regression algorithm, a K-nearest neighbor regression algorithm, a random forest regression algorithm, a GBDT regression algorithm, an xgboost regression algorithm, a Long Short-Term Memory network (LSTM) algorithm, etc., and is set according to actual needs, which is not limited in the embodiments of the present invention. The LSTM algorithm is a neural network algorithm, and the model can be trained in a back-propagation manner. It can be understood that, in the training process of each preset model, the hyper-parameter can be automatically adjusted and optimized until a parameter which enables the model prediction effect to be globally optimal or locally optimal is obtained, in the hyper-parameter adjusting and optimizing process, the hyper-parameter which enables the model prediction effect to be optimal is dynamically searched based on the preset parameter value range and value change step length, the used hyper-parameter search algorithm includes grid search (GridSearchCV) and random search (randomized searchcv), and the hyper-parameter search algorithm is set according to actual needs, and the embodiment of the invention is not limited.
S305, verifying the predetermined number of undetermined predictor models according to the verification set and the historical use times corresponding to the verification set to obtain an R square value of each undetermined predictor model;
specifically, after the server obtains the preset number of undetermined predictor models, for each undetermined predictor model, the verification set is input into the undetermined predictor model, and the estimated use times corresponding to each feature data in the verification set are output. The server may calculate an R-squared (R-squared) value of the to-be-determined predictor model according to the estimated number of uses corresponding to each feature data in the verification set and the historical number of uses corresponding to each feature data in the verification set. The server may obtain R-squared values of the preset number of to-be-determined predictor models. The larger the R square value is, the better the using frequency prediction effect of the undetermined predictor model is.
S306, selecting the set number of predictor models from the preset number of undetermined predictor models according to the R square value of each undetermined predictor model.
Specifically, after obtaining the R square values of the predetermined number of undetermined predictor models, the server may sort the R square values of the predetermined number of undetermined predictor models according to the magnitude of the R square values, and if the absolute value of the difference between the R square values of all two adjacent undetermined predictor models is smaller than a predetermined value, it indicates that the R square values of the predetermined number of undetermined predictor models are not greatly different, and may take the predetermined number of undetermined predictor models as the set number of predictor models. The preset value is set according to actual experience, and the embodiment of the invention is not limited.
If the absolute value of the difference value of the R square values of the two adjacent to-be-determined predictor models is larger than or equal to the preset value in the difference values of the R square values of the two adjacent to-be-determined predictor models, the difference of the R square values of the preset number of to-be-determined predictor models is large, the to-be-determined predictor models with the largest set number of R square values can be used as the set number of sales predictor models, and the set number is larger than or equal to 4.
The undetermined predictor model with the R square value meeting the requirement is selected from the preset number of undetermined predictor models to serve as the predictor model for predicting the use times, and the accuracy of the use time prediction of the card reading module can be improved.
Fig. 4 is a schematic flow chart of a fault early warning method for a card reader device according to still another embodiment of the present invention, as shown in fig. 4, and based on the foregoing embodiments, further, the obtaining the usage number feature data of the card reader module according to the usage number training data of the card reader module includes:
s3021, performing feature construction on the use times training data of the card reading module to obtain multiple types of training feature data;
specifically, after obtaining the use number training data of the card reading module, the server may perform feature construction on the use number training data of the card reading module to obtain multiple types of training feature data. The multi-class training feature data may include a device self feature, a period feature, a salary social security feature, a holiday feature, a usage frequency statistic feature, a contemporaneous usage feature, a past usage feature, an average usage feature, a cyclic usage feature, and the like, and may be set according to actual needs, which is not limited in the embodiment of the present invention.
S3022, selecting the use frequency characteristic data of the card reading module from the multi-class training characteristic data according to a characteristic selection algorithm.
Specifically, after obtaining the multiple types of training feature data, the server may select the feature data of the number of uses of the card reading module from the multiple types of training feature data by using a feature selection algorithm. The feature selection algorithm includes, but is not limited to, directional search, optimal priority search, sequence forward selection, sequence backward selection, sequence floating selection, and the like, and is set according to an actual situation, which is not limited in the embodiments of the present invention. And selecting the use frequency characteristic data of the card reading module from the multi-class training characteristic data through a characteristic selection algorithm, so that the use frequency of the card reading module can be more accurately predicted. It is understood that the usage characteristic data includes at least a usage statistic.
It can be understood that after the fault early warning method for the card reader device provided by the embodiment of the invention is put into use, the actual use times of each card reading module can be collected, when a certain number of the actual use times and the predicted use times of the card reading modules are collected, the R square value of the use time prediction model of the card reading module can be calculated according to the actual use times and the predicted use times of the card reading modules, and the use time prediction model of the card reading module is evaluated through the R square value. And when the R square value of the use time prediction model of the card reading module is smaller than a set value, retraining to obtain the use time prediction model of the card reading module. The certain number and the set value are set according to actual experience, and the embodiment of the invention is not limited.
Fig. 5 is a schematic structural diagram of a fault early warning apparatus for a card reader device according to an embodiment of the present invention, and as shown in fig. 5, the fault early warning apparatus for a card reader device according to an embodiment of the present invention includes a prediction data obtaining unit 501, a first feature processing unit 502, a prediction unit 503, an obtaining unit 504, and a determining unit 505, where:
the predicted data obtaining unit 501 is configured to obtain predicted data of each card reading module of the card reader device; the first feature processing unit 502 is configured to perform feature processing on the prediction data of each card reading module to obtain prediction feature data of each card reading module; the prediction unit 503 is configured to obtain the predicted number of usage times of each card reading module according to the predicted feature data of each card reading module and the usage time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module; the obtaining unit 504 is configured to obtain the predicted cumulative number of times of use of each card reading module according to the predicted number of times of use of each card reading module and the current cumulative number of times of use; the judging unit 505 is configured to send out failure early warning information after judging that the predicted accumulated usage times of the card reading module is greater than the corresponding usage threshold.
Specifically, by preprocessing the history data of the past first preset time period of each card reading module of the card reader device, the prediction data of each card reading module can be obtained, and the obtaining unit 501 can obtain the prediction data of each card reading module of the card reader device. The card reading module can be any one of a magnetic stripe type reading and writing module, a contact type IC card reading and writing module, a radio frequency IC card reading and writing module and an identity card reading module, and the card reader equipment comprises at least one card reading module. The historical record data of each card reading module may include device data, periodic data, salary and social security data, holiday data, usage times statistical data, contemporaneous usage times data, current usage times data, average usage times data, cycle usage times data, and the like, and is set according to actual needs, which is not limited in the embodiments of the present invention. The first preset time period is, for example, 3 months, and is set according to actual needs, which is not limited in the embodiment of the present invention.
After obtaining the predicted data of each card reading module, the first feature processing unit 502 may perform feature processing on the predicted data of each card reading module to obtain the predicted feature data of each card reading module.
After obtaining the predicted feature data of each card reading module, the prediction unit 503 may input the predicted feature data of each card reading module into the usage number prediction model of each card reading module, and output the predicted usage number of each card reading module through the processing of the usage number prediction model of each card reading module. The use number prediction model of each card reading module comprises a set number of prediction submodels, and the set number of prediction submodels are obtained according to the use number training data and the historical use number training of each card reading module. The set number is set according to actual conditions, and the embodiment of the invention is not limited.
After obtaining the predicted number of usage times of each card reading module, the obtaining unit 504 may further obtain a current accumulated number of usage times of each card reading module, where each card reading module is recorded during usage, and may obtain the current accumulated number of usage times of each card reading module through statistics. For each card reading module, the obtaining unit 504 calculates the sum of the predicted usage times and the current accumulated usage times of the card reading module, and takes the sum as the predicted accumulated usage times of the card reading module. The current accumulated use times of the card reading module refers to the total use times of the card reading module from the beginning of use to the time of use prediction.
After the predicted accumulated use times of each card reading module are obtained, for each card reading module, the judgment unit 505 compares the predicted accumulated use times of the card reading module with the use threshold corresponding to the card reading module, and if the predicted accumulated use times of the card reading module is greater than the use threshold corresponding to the card reading module, it indicates that the card reading module is about to fail, the server may send out failure early warning information, so that a worker can perform operation and maintenance preparation work. And if the predicted accumulated use times of the card reading module is less than or equal to the use threshold corresponding to the card reading module, the card reading module can still be used without maintenance. The use threshold corresponding to each card reading module is set according to actual conditions, and the embodiment of the invention is not limited. It can be understood that the predicted accumulated use times of any card reading module of the card reader device is greater than the corresponding use threshold, fault early warning information can be sent out, and the card reading module needing to be maintained can be indicated in the fault early warning information.
The fault early warning device of the card reader equipment provided by the embodiment of the invention can acquire the prediction data of each card reading module of the card reader equipment, perform characteristic processing on the prediction data of each card reading module to acquire the prediction characteristic data of each card reading module, acquire the prediction using times of each card reading module according to the prediction characteristic data of each card reading module and the using time prediction model of each card reading module, acquire the prediction accumulated using times of each card reading module according to the prediction using times and the current accumulated using times of each card reading module, send out fault early warning information after judging that the prediction accumulated using times of the card reading module is larger than the corresponding using threshold value, perform fault early warning before the service life of the card reading module expires so as to take action in advance to maintain or replace the card reading module and realize active maintenance on the card reader equipment, the reliability of the card reader device is improved. In addition, interruption of business handling caused by the failure of the card reader equipment can be reduced, and the influence on customer service is reduced.
Fig. 6 is a schematic structural diagram of a fault warning apparatus of a card reader device according to another embodiment of the present invention, as shown in fig. 6, on the basis of the foregoing embodiments, further, the predicting unit 503 includes a predicting sub-unit 5031 and a calculating sub-unit 5032, where:
the predictor 5031 is configured to obtain the number of usage times of the card reading module in the set number according to the predicted feature data of the card reading module and the set number of predictor models; the calculation subunit 5032 is configured to calculate the predicted number of usage times of the card reading module according to the set number of usage times of the card reading module and the weight corresponding to each usage time; and the weight corresponding to each use frequency of the card reading module is obtained in advance.
Specifically, the predictor 5031 inputs the predicted feature data of the card reading module into each predictor model, and may output the number of times of use of the card reading module in each predictor model, where the number of the predictor models is set, and the server may obtain the number of times of use of the card reading module set.
After obtaining the set number of usage times of the card reading module, the calculating subunit 5032 calculates the predicted usage times of the card reading module according to the set number of usage times of the card reading module and the weight corresponding to each usage time. The weight corresponding to each usage number is obtained in advance, for example, set in advance or generated dynamically.
Fig. 7 is a schematic structural diagram of a fault warning apparatus for a card reader device according to yet another embodiment of the present invention, as shown in fig. 7, on the basis of the foregoing embodiments, further, the fault warning apparatus for a card reader device according to the embodiment of the present invention further includes a training data obtaining unit 506, a second feature processing unit 507, a dividing unit 508, a training unit 509, a verification unit 510, and a selection unit 511, where:
the training data acquisition unit 506 is configured to acquire use number training data and historical use number of the card reading module; the second feature processing unit 507 is configured to obtain the use time feature data of the card reading module according to the use time training data of the card reading module; the dividing unit 508 is configured to divide the usage time characteristic data of the card reading module into a training set and a verification set; the training unit 509 is configured to train to obtain a preset number of to-be-predicted sub-models according to the training set, the historical usage times corresponding to the training set, and a preset number of preset models; wherein the preset number is greater than or equal to the set number; the verification unit 510 is configured to verify the predetermined number of undetermined predictor models according to the verification set and the historical usage times corresponding to the verification set, and obtain an R square value of each undetermined predictor model; the selecting unit 511 is configured to select the predetermined number of predictor models from the predetermined number of undetermined predictor models according to an R-square value of each undetermined predictor model.
Specifically, the use number training data of each card reading module of the card reader device can be obtained by preprocessing the historical record data of the past second preset time period of each card reading module. The historical use times can be the use times of each card reading module in the past second preset time period every day, and are set according to actual needs, and the embodiment of the invention is not limited. The training data obtaining unit 506 may obtain the number-of-use training data of the card reading module and the historical number of uses. The second preset time period is, for example, 3 years, and is set according to actual needs, which is not limited in the embodiment of the present invention. Understandably, if the historical use times are the use times of each day, the use time prediction model of the card reading module obtained by training is used for predicting the use times of the next day of the card reading module; if the historical usage is every three days, then training the obtained usage prediction model of the card-reading module predicts the usage of the card-reading module for the next three days.
After obtaining the use number training data of the card reading module, the second feature processing unit 507 may perform feature processing on the use number training data of the card reading module to obtain the use number feature data of the card reading module.
After obtaining the usage times characteristic data of the card reading module, the dividing unit 508 may divide the usage times characteristic data of the card reading module into a training set and a verification set, where the training set is used for model training and the verification set is used for model verification.
The training unit 509 performs model training on each preset model according to the training set and the historical use times corresponding to the training set, so as to obtain each to-be-determined predictor model. The number of the to-be-determined predictor models is preset, and the preset to-be-determined predictor models can be obtained. Wherein the preset number is greater than or equal to the set number. The preset number is set according to actual needs, and the embodiment of the invention is not limited. The preset model includes, but is not limited to, a support vector machine regression algorithm, a K-nearest neighbor regression algorithm, a random forest regression algorithm, a GBDT regression algorithm, an xgboost regression algorithm, an LSTM algorithm, etc., and is set according to actual needs, which is not limited in the embodiments of the present invention. The LSTM algorithm is a neural network algorithm, and the model can be trained in a back-propagation manner. It can be understood that, in the training process of each preset model, the hyper-parameter can be automatically adjusted and optimized until a parameter which enables the model prediction effect to be globally optimal or locally optimal is obtained, in the hyper-parameter adjusting and optimizing process, the hyper-parameter which enables the model prediction effect to be optimal is dynamically searched based on the preset parameter value range and the value change step length, the used hyper-parameter search algorithm comprises the algorithm which is not limited to GridSearchCV, RandomizedSearchCV and the like, and the method is set according to actual needs, and the embodiment of the invention is not limited.
After obtaining the preset number of undetermined predictor models, for each undetermined predictor model, the verification unit 510 inputs the verification set into the undetermined predictor model, and outputs the estimated use times corresponding to each feature data in the verification set. The verification unit 510 may calculate an R-squared (R-squared) value of the to-be-determined predictor model according to the estimated number of times of use corresponding to each feature data in the verification set and the historical number of times of use corresponding to each feature data in the verification set. The verification unit 510 may obtain an R-square value of the preset number of pending predictor models. The larger the R square value is, the better the using frequency prediction effect of the undetermined predictor model is.
After obtaining the R square values of the predetermined number of undetermined predictor models, the selecting unit 511 may sort the R square values of the predetermined number of undetermined predictor models according to the magnitude of the R square values, and if the absolute value of the difference between the R square values of all two adjacent undetermined predictor models is smaller than a predetermined value, it indicates that the R square values of the predetermined number of undetermined predictor models are not greatly different, and may take the predetermined number of undetermined predictor models as the set number of predictor models. The preset value is set according to actual experience, and the embodiment of the invention is not limited.
If the absolute value of the difference value of the R square values of the two adjacent to-be-determined predictor models is larger than or equal to the preset value in the difference values of the R square values of the two adjacent to-be-determined predictor models, the difference of the R square values of the preset number of to-be-determined predictor models is large, the to-be-determined predictor models with the largest set number of R square values can be used as the set number of sales predictor models, and the set number is larger than or equal to 4.
Fig. 8 is a schematic structural diagram of a fault warning apparatus of a card reader device according to still another embodiment of the present invention, and as shown in fig. 8, on the basis of the foregoing embodiments, further, the second feature processing unit 507 includes a building subunit 5071 and a selecting subunit 5072, where:
the construction subunit 5071 is configured to perform feature construction on the use number training data of the card reading module, so as to obtain multiple types of training feature data; the selecting subunit 5072 is configured to select, according to a feature selection algorithm, the usage number feature data of the card reading module from the multiple types of training feature data.
Specifically, after obtaining the training data of the number of uses of the card reading module, the constructing subunit 5071 may perform feature construction on the training data of the number of uses of the card reading module, so as to obtain multiple types of training feature data. The multi-class training feature data may include a device self feature, a period feature, a salary social security feature, a holiday feature, a usage frequency statistic feature, a contemporaneous usage feature, a past usage feature, an average usage feature, a cyclic usage feature, and the like, and may be set according to actual needs, which is not limited in the embodiment of the present invention.
After obtaining the plurality of types of training feature data, the selecting subunit 5072 may select the usage times feature data of the card reading module from the plurality of types of training feature data by using a feature selection algorithm. The feature selection algorithm includes, but is not limited to, directional search, optimal priority search, sequence forward selection, sequence backward selection, sequence floating selection, and the like, and is set according to an actual situation, which is not limited in the embodiments of the present invention. And selecting the use frequency characteristic data of the card reading module from the multi-class training characteristic data through a characteristic selection algorithm, so that the use frequency of the card reading module can be more accurately predicted. It is understood that the usage characteristic data includes at least a usage statistic.
The embodiment of the apparatus provided in the embodiment of the present invention may be specifically configured to execute the processing flows of the above method embodiments, and the functions of the apparatus are not described herein again, and refer to the detailed description of the above method embodiments.
Fig. 9 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 9, the electronic device may include: a processor (processor)901, a communication Interface (Communications Interface)902, a memory (memory)903 and a communication bus 904, wherein the processor 901, the communication Interface 902 and the memory 903 are communicated with each other through the communication bus 904. The processor 901 may call logic instructions in the memory 903 to perform the following method: acquiring prediction data of each card reading module of the card reader equipment; performing characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module; obtaining the predicted use times of each card reading module according to the predicted characteristic data of each card reading module and the use time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module; obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module; and if the predicted accumulated use times of the card reading module are judged to be larger than the corresponding use threshold, sending out fault early warning information.
In addition, the logic instructions in the memory 903 may be implemented in a software functional unit and stored in a computer readable storage medium when the logic instructions are sold or used as a separate product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments, for example, comprising: acquiring prediction data of each card reading module of the card reader equipment; performing characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module; obtaining the predicted use times of each card reading module according to the predicted characteristic data of each card reading module and the use time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module; obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module; and if the predicted accumulated use times of the card reading module are judged to be larger than the corresponding use threshold, sending out fault early warning information.
The present embodiment provides a computer-readable storage medium, which stores a computer program, where the computer program causes the computer to execute the method provided by the above method embodiments, for example, the method includes: acquiring prediction data of each card reading module of the card reader equipment; performing characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module; obtaining the predicted use times of each card reading module according to the predicted characteristic data of each card reading module and the use time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module; obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module; and if the predicted accumulated use times of the card reading module are judged to be larger than the corresponding use threshold, sending out fault early warning information.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
In the description herein, reference to the description of the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," "an example," "a particular example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A fault early warning method for card reader equipment is characterized by comprising the following steps:
acquiring prediction data of each card reading module of the card reader equipment;
performing characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module;
obtaining the predicted use times of each card reading module according to the predicted characteristic data of each card reading module and the use time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module;
obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module;
and if the predicted accumulated use times of the card reading module are judged to be larger than the corresponding use threshold, sending out fault early warning information.
2. The method of claim 1, wherein obtaining the predicted number of uses for each card reading module based on the predicted characteristic data for each card reading module and the predicted number of uses for each card reading module comprises:
obtaining the use times of the card reading module with the set number according to the predicted characteristic data of the card reading module and the prediction submodels with the set number;
calculating the predicted use times of the card reading module according to the set number of use times of the card reading module and the weight corresponding to each use time; and the weight corresponding to each use frequency of the card reading module is obtained in advance.
3. The method of claim 1 or 2, wherein obtaining the usage prediction model for each card reading module based on the usage training data for each card reading module and the historical usage training comprises:
acquiring use times training data and historical use times of the card reading module;
obtaining the use times characteristic data of the card reading module according to the use times training data of the card reading module;
dividing the use frequency characteristic data of the card reading module into a training set and a verification set;
training to obtain a preset number of to-be-determined prediction submodels according to the training set, the historical use times corresponding to the training set and a preset number of preset models; wherein the preset number is greater than or equal to the set number;
verifying the preset number of to-be-determined predictor models according to the verification set and the historical use times corresponding to the verification set to obtain an R square value of each to-be-determined predictor model;
and selecting the set number of predictor models from the preset number of undetermined predictor models according to the R square value of each undetermined predictor model.
4. The method according to claim 3, wherein the obtaining the usage number characteristic data of the card reading module according to the usage number training data of the card reading module comprises:
performing feature construction on the use times training data of the card reading module to obtain various types of training feature data;
and selecting the use frequency characteristic data of the card reading module from the multi-class training characteristic data according to a characteristic selection algorithm.
5. A fault early warning device of a card reader device, comprising:
the prediction data acquisition unit is used for acquiring prediction data of each card reading module of the card reader equipment;
the first characteristic processing unit is used for carrying out characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module;
the prediction unit is used for obtaining the predicted using times of each card reading module according to the predicted characteristic data of each card reading module and the using time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module;
the obtaining unit is used for obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module;
and the judging unit is used for sending out fault early warning information after judging that the predicted accumulated use times of the card reading module is larger than the corresponding use threshold.
6. The apparatus of claim 5, wherein the prediction unit comprises:
the prediction subunit is used for obtaining the use times of the card reading module with the set number according to the prediction characteristic data of the card reading module and the prediction submodels with the set number;
the calculation subunit is used for calculating the predicted use times of the card reading module according to the set number of use times of the card reading module and the weight corresponding to each use time; and the weight corresponding to each use frequency of the card reading module is obtained in advance.
7. The apparatus of claim 5 or 6, further comprising:
the training data acquisition unit is used for acquiring the use times training data and the historical use times of the card reading module;
the second characteristic processing unit is used for acquiring the use time characteristic data of the card reading module according to the use time training data of the card reading module;
the dividing unit is used for dividing the use frequency characteristic data of the card reading module into a training set and a verification set;
the training unit is used for training to obtain a preset number of to-be-predicted submodels according to the training set, the historical use times corresponding to the training set and a preset number of preset models; wherein the preset number is greater than or equal to the set number;
the verification unit is used for verifying the preset number of to-be-determined prediction submodels according to the verification set and the historical using times corresponding to the verification set to obtain an R square value of each to-be-determined prediction submodel;
and the selection unit is used for selecting the set number of the predictor models from the preset number of the undetermined predictor models according to the R square value of each undetermined predictor model.
8. The apparatus of claim 7, wherein the second feature processing unit comprises:
the construction subunit is used for carrying out feature construction on the use times training data of the card reading module to obtain various types of training feature data;
and the selecting subunit is used for selecting the use frequency characteristic data of the card reading module from the multiple types of training characteristic data according to a characteristic selection algorithm.
9. 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 steps of the method of any of claims 1 to 4 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 4.
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