Card reading method, system and device of card reader

Abstract

The invention discloses a card reading method, system and device for a card reader, relates to the field of signal transmission technology, and is used to solve the problems of card reading recognition errors and low card reading efficiency caused by different initialization requirements of a card reader and a device. The method comprises utilizing a principal component regression method and a covariance analysis to analyze the initialization requirements of a card reader and a device, and judging whether a combination of a current card reader and a current device is to be marked in combination according to the analysis results in combination with environmental factors. When the environmental factors are large or the combination of the card reader and the device is not compatible, the card reader and the device are tested for card reading and the test data are collected. The card reading accuracy is calculated according to the test data, and the error rate is comprehensively reported. The method further analyzes and judges whether a combination of the current card reader and the device is to be marked in combination using a logistic regression method, thereby improving the accuracy of card reading and recording the combination of the card reader and the device, facilitating future query and matching, and reducing costs and increasing efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of signal transmission, and more specifically, to a card reading method, system and device for a card reader. Background Art

[0002] Signal transmission technology is a very important field in electronic engineering and communication engineering. By encoding, modulating signal machines and constructing network protocols and system architectures, it can receive and identify various signals. When signal transmission technology is applied to the card reading of card readers, it can make the card reading efficiency higher and at the same time ensure the security of information transmission.

[0003] The prior art has the following deficiencies:

[0004] In the past, when a card reader reads a card, it will initialize the device after loading the driver program and then read the card, without considering the different requirements for initialization of different devices and card readers. When some devices are read without initialization, the card cannot be recognized and the card reading fails. Or some devices have a long initialization time, resulting in low card reading efficiency and other problems.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a card reading method, system and device for a card reader, which classify and combine and mark the devices and card readers respectively, and perform secondary judgment and collection on the combination of devices and card readers according to the feedback data to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A card reading system for a card reader, comprising: a data acquisition module, a data processing module, a data classification module and a feedback adjustment module, and the modules are signal-connected to each other;

[0009] The data acquisition module is used to collect card reader data, device data and environmental data and send them to the data processing module for processing;

[0010] The data processing module is used to receive various types of data and perform different types of processing on various types of data. The principal component regression method is used to process the card reader data and calculate the card reader data frame coefficient and the card reader transmission coefficient; the device data is randomly sampled and screened, and the covariance formula is used to calculate the covariance to set the device classification threshold; according to the environmental data, the voltage coefficient and the temperature coefficient are calculated using the average value and the standard deviation, and the various coefficients and thresholds calculated by the data processing module are sent to the data classification module for classification processing;

[0011] The data classification module is used to receive various coefficients and thresholds calculated by the data processing module, compare them respectively, classify the card reader and the device, and determine whether the current card reading environment is good. According to the judgment result, it selects whether to perform classification matching and combination marking on the card reader and the device; if the card reader and the device are not combined and marked, the data classification module automatically collects feedback data and transmits it to the feedback adjustment module;

[0012] The feedback adjustment module receives the feedback data, calculates the combined coefficient of the card reader and the device using the logistic regression method, and determines whether to perform combined marking on the current combination by comparing the device combined coefficient with the preset combined coefficient threshold;

[0013] The specific steps for the data classification module to classify are as follows:

[0014] The data classification module compares the received card reader data frame coefficient, card reader transmission coefficient with the data frame threshold and the transmission threshold. If the card reader data frame coefficient is lower than the data frame threshold and the card reader transmission coefficient exceeds the transmission threshold, it is judged that the initialization requirement of the card reader is low, otherwise it is judged that the initialization requirement of the card reader is high;

[0015] The data classification module compares the card reader initialization time, device current consumption with the device classification threshold to judge the device initialization requirement and classify the device according to the initialization requirement. After the data classification module finishes classifying the card reader and the device, it judges whether the environment is good and determines whether to perform combined marking on the card reader and the device according to the environmental situation.

[0016] In a preferred embodiment, the specific steps for the data acquisition module to acquire each data are as follows:

[0017] The card reader data includes the card reader data frame length and the card reader data transmission speed, the device data includes the card reader initialization time and the real-time current during device operation, and the environmental data includes the device real-time voltage and the card reading environment humidity; the data acquisition module selects a period of time as the sample time and selects multiple time points within the sample time to collect the card reader data transmission speed;

[0018] The data acquisition module connects card readers to the device for initialization, records the initialization time of card readers and records the device current consumption during the initialization process; selects the median of the initialization times of

[0019] In a preferred embodiment, when the data processing module receives the card reader data, it takes the average of the card reader transmission speeds collected at multiple time points selected by the same type of card reader during the sample time as the average card reader transmission speed, and merges the data frame lengths of multiple card readers into a data frame data set; collects the average card reader transmission speeds of an equal number of card readers and merges them into an average speed data set, normalizes the data in the data frame data set and the average speed data set, and sequentially replaces the calculated normalized results with the corresponding data in the data set to obtain a data frame coefficient data set and an average speed coefficient data set.

[0020] In a preferred embodiment, the specific steps for the data processing module to process the card reader data using the principal component regression method are as follows:

[0021] The data processing module sets the principal component interval, arranges the data in the data frame data set from largest to smallest, selects the median in the data frame data set, sets the principal component threshold ratio with the median to determine the principal component threshold interval, expands the median to both sides according to the principal component threshold interval ratio to obtain the principal component threshold interval, marks the data in the data frame data set that is within the principal component threshold interval as principal component data, and marks other data as secondary component data, and classifies and calculates the data to obtain the card reader data frame coefficient.

[0022] In a preferred embodiment, when calculating the card reader data frame coefficient, the data processing section performs weighted calculation on the principal component data and the secondary component data by setting the component weights to obtain the card reader data frame coefficient, where the principal component weight and the secondary component weight can be set by multiplying the principal component threshold interval ratio by the scaling ratio.

[0023] In a preferred embodiment, the specific steps for determining whether the environment is good are as follows:

[0024] When the voltage coefficient and the temperature coefficient are lower than the voltage threshold and the temperature threshold, it is determined that the card reading environment is good; if the card reading environment is good and the initialization requirements for both the card reader and the device are low, then the current card reader and the current device are combined and marked and recorded in the corresponding data storage database; if the current card reader and the current device have not been combined and marked, the data classification module performs multiple card reading operations on the device and the card reader, and accesses the log data stored inside the card reader after the card reading operation to obtain the number of card readings, the number of correct card readings, and the error rate when the card reader and the device perform card reading work, and transmits them to the feedback adjustment module.

[0025] In a preferred embodiment, the feedback adjustment module receives the number of card readings, the number of correct card readings, and the error rate passed in by the data classification module and calculates the card reading accuracy rate, where the card reading accuracy rate is the ratio of the number of correct card readings to the number of card readings;

[0026] Use the logistic regression method to calculate the card reader and device combination coefficient, and its formula can be: , where is the combination coefficient, e is the natural base of logarithms, and z is the combination factor. If the combination coefficient of the card reader and the device exceeds the combination coefficient threshold, the combination of the card reader and the device is marked and recorded in the corresponding data storage database.

[0027] A card reading method for a card reader, used to implement the card reading system of the above-mentioned card reader, characterized by including the following steps:

[0028] Step 1: Collect card reader data, device data, and environmental data;

[0029] Step 2: Analyze the initialization requirements of the card reader using the principal component regression method based on the card reader data, analyze the initialization requirements of the device based on the device data, and use the environmental data to determine whether the card reading environment is good;

[0030] Step 3: Select whether to classify and match the card reader and the device and perform a combination mark according to the judgment result of the card reading environment;

[0031] Step 4: If the card reader and the device are not combined and marked, collect feedback data, and use the logistic regression method to determine whether to perform a combination mark according to the feedback data.

[0032] A card reading device for a card reader, including a memory, a processor, and a computer program stored in the memory and operable on the processor.

[0033] Technical effects and advantages of the card reading method, system, and device of the card reader of the present invention:

[0034] By collecting card reader data, analyzing the card reader data to classify the card reader into a card reader with low initialization requirements and a card reader with high initialization requirements, collecting device data, analyzing the device data to classify the device into a device with low initialization requirements and a device with high initialization requirements, collecting environmental data, using the environmental data to determine whether to combine and mark the card reader and the device. If no combination mark is made, collect feedback data, perform a secondary judgment on the card reader and the device according to the feedback data, and select whether to combine and mark the card reader and the device according to the judgment result and record it in the corresponding data storage database. The card reading accuracy is improved, the combination situation of the card reader and the device is also included, which is convenient for future query and matching use, reduces costs and increases efficiency. Description of the Drawings

[0035] Figure 1 It is a flowchart of the card reading system and device of the card reader of the present invention,

[0036] Figure 2 It is a main structural schematic diagram of the card reading method of the card reader of the present invention,

[0037] Figure 3 This is a schematic secondary diagram of the card reading method structure of a card reader according to the present invention. Specific embodiments

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0039] The present invention classifies card readers into low-initialization-requirement card readers and high-initialization-requirement card readers by collecting card reader data and analyzing the card reader data, collects device data, classifies the devices into low-initialization-requirement devices and high-initialization-requirement devices according to the analysis of the device data, collects environmental data, uses the environmental data to judge whether to perform combined marking on the card reader and the device. If no combined marking is performed, feedback data is collected, the card reader and the device are rejudged according to the feedback data, and whether to perform combined marking on the card reader and the device is selected according to the judgment result and recorded in the corresponding data storage database. This improves the card reading accuracy, also includes the combination situation of the card reader and the device, facilitates future query and matching use, reduces costs and increases efficiency.

[0040] Embodiment 1, a card reading system and device for a card reader, as Figure 1 shown, includes: a data acquisition module, a data processing module, a data classification module, and a feedback adjustment module, and the modules are signal-connected to each other.

[0041] The functions of each module are as follows:

[0042] The data acquisition module is used to collect card reader data, device data, and environmental data and send them to the data processing module for processing. The card reader data includes the card reader data frame length and the card reader data transmission speed. The data acquisition module obtains the card reader data frame length by accessing and parsing the data stack and uses a performance test tool to detect the card reader data transmission speed; the device data includes the card reader initialization time and the real-time current when the device is working. The data acquisition module uses a logic analyzer to collect the card reader initialization time when the device is working and uses the built-in current monitoring of the power supply to obtain the real-time current; the environmental data includes the real-time voltage of the device and the humidity of the card reading environment. The data acquisition module uses the built-in voltage monitoring of the power supply to obtain the real-time voltage and obtains the humidity of the card reading environment through a humidity sensor.

[0043] The data processing module is used to receive various types of data sent by the data acquisition module. After normalizing the various types of data, it calculates the reader data frame coefficient and the reader transmission coefficient of the normalized reader data using the principal component regression method; it randomly samples and screens the normalized device data and then calculates the covariance using the covariance formula to set the device classification threshold and calculate the current device covariance as the classification basis; for the environmental data, it calculates the voltage coefficient and the temperature coefficient using the average value and the standard deviation, and sends the various coefficients and thresholds calculated by the data processing module to the data classification module for classification processing.

[0044] The data classification module is used to receive the various coefficients and thresholds calculated by the data processing module and compare them respectively. It classifies the current reader and the current device based on the comparison results. The data classification module compares the voltage coefficient and the temperature coefficient with the preset voltage threshold and temperature threshold to determine whether the current card reading environment is good. If the current card reading environment is good and the initialization requirements for both the current reader and the current device are low, it makes a combined mark for the current reader and the current device; if the current reader and the current device have not been combinedly marked, the data classification module automatically conducts a card reading test on the current reader and the current device, and transmits the card reading quantity, the correct card reading quantity, and the error rate obtained from the card reading test to the feedback adjustment module.

[0045] The feedback adjustment module receives the card reading quantity, the correct card reading quantity, and the error rate after the card reading test of the current reader and the current device, and calculates the card reading correct rate of the current reader and the current device using the card reading quantity and the correct card reading quantity. After calculating the card reading correct rate of the current reader, it constructs a logistic regression formula by integrating the error rate, and calculates the combined coefficient of the current reader and the current device according to the logistic regression formula. It determines whether to make a combined mark for the current combination by comparing the current device combined coefficient with the preset combined coefficient threshold.

[0046] It should be noted that when the reader uses the standard serial communication protocol, its data frame length is short, the initialization requirement of the reader is low, the data transmission speed is faster, the data exchange between the reader and the card is faster, and the initialization time is lower, so the initialization requirement is low; the initialization times of readers for different devices are different. For the same reader, the shorter the initialization time on different devices, the lower the device initialization requirement. Devices with low initialization requirements do not need to perform complicated processing and usually consume less current during operation; whether the device voltage is stable and the humidity of the card reading environment will both affect the card reading process.

[0047] The parsed data stack stores the text and numerical values before and after the parsing of the reader data frame, and the reader data frame length can be obtained through the parsed data stack. Performance testing tools are often used to monitor network performance and data transmission speed. Common performance detection tools are , which can be used to detect the data transmission speed of the card reader. A logic analyzer is a measuring instrument used to analyze and test data circuit signals. It can capture and display the waveforms of digital signals and analyze and decode the signals, and can be used to collect the initialization time of the card reader when the device is working. Built-in power current monitoring means that the device power adapter integrates a current detection circuit and can directly output a current signal for external devices to use. Built-in power voltage monitoring means that the power adapter integrates a voltage detection circuit, which can be used to obtain the real-time current and voltage conditions when the device is working respectively. A humidity sensor is a sensor device used to detect the water vapor content in the environment and can be used to detect the humidity of the card reading environment. The log data stored in the card reader includes system event records, from which the number of card readings, the number of correct card readings, and the error rate after the card reader is matched with the device can be obtained.

[0048] In the data acquisition module, respectively The specific steps for the card reader data of [X] card readers, the device data of an equal number of devices, and the environmental data are as follows:

[0049] Taking a certain card reader as an example, the data acquisition module accesses and parses the data stack to obtain the card reader data frame length; selects a period of time as the sample time, and selects multiple time points within the sample time to collect the card reader data transmission speed.

[0050] Taking a certain device as an example, connect [X] card readers to the device for initialization, record the initialization time of [X] card readers and record the device current consumption during the initialization process; select the median of the initialization times of [X] card readers, record the corresponding card readers, and set multiple time points during the card reader initialization time to collect the current voltage of the device and use the humidity sensor to detect the current card reading environment humidity.

[0051] The specific processing steps of the data processing module are as follows:

[0052] When the data processing module receives the card reader data, it takes the average of the card reader transmission speeds collected at multiple time points selected by the same type of card reader within the sample time as the average card reader transmission speed, and combines the data frame lengths of a sufficient number of card readers into a data frame data set; collects the average card reader transmission speeds of an equal number of card readers and combines them into an average speed data set.

[0053] Perform normalization processing on the data in the data frame data set and the average speed data set. The normalization formula can be: , where is the data in the data frame data set or the average speed data set, is the minimum data in the corresponding data set, is the maximum data in the corresponding data set, It is the result after corresponding data normalization. Replace the data after normalization of the calculated data with the corresponding data in the dataset in sequence to obtain the data frame coefficient dataset and the average speed coefficient dataset.

[0054] The principal component regression method can be used to calculate the reader initialization coefficient to classify the reader initialization requirements. Taking the data frame dataset as an example, set the principal component threshold interval, arrange the data in the data frame dataset from largest to smallest, select the median in the data frame dataset, and determine the principal component threshold interval by setting the principal component threshold ratio with the median. Set the principal component threshold interval ratio to , with the median Expand to both sides to obtain the principal component threshold interval. It can be set according to the actual situation. For example: set the principal component threshold interval ratio to , then expand to both sides according to the principal component threshold ratio of The obtained principal component threshold interval covers the middle data. Mark the data in the principal component threshold interval in the data frame dataset as principal component data, and mark other data as secondary component data.

[0055] The reader data frame coefficient is obtained by weighted calculation by setting the component weights for the principal component data and the secondary component data. Its formula can be , where is the reader data frame coefficient, and are the principal component weight and the secondary component weight respectively, is the average value of the principal component data, is the average value of the secondary component data. Further, the principal component weight and the secondary component weight can be set according to the principal component threshold interval ratio multiplied by the scaling ratio. Their formulas are respectively , , where s is the scaling ratio and can be adjusted according to the actual situation, is the principal component threshold interval ratio.

[0056] Similarly, use the average speed coefficient dataset to calculate the reader transmission coefficient marked as . Before classifying the current reader, the data processing module receives the current reader data frame length and the reader transmission speed. After normalization, use the same method to calculate the current reader data frame coefficient and the current reader transmission coefficient, which are marked as and respectively. After calculating and obtaining and , the data processing module will , , and Send it to the data classification module.

[0057] It should be noted that for the above data with the same normalization operation, when calculating the current card reader data frame coefficient and the current card reader transmission coefficient, the current data is used as the only principal component data, and the secondary component data is set to 0.

[0058] When the data processing module receives the device data, it normalizes the device data and then merges the data of the same type, respectively merging them into an initialization data set and a current consumption data set. A random sampling method is used for screening in the merged initialization data set and current consumption data set. Random sampling is a conventional method and will not be elaborated here. The covariance is calculated using the screened data as the raw material classification threshold.

[0059] The covariance calculation formula is: Where, are the screened initialization data and current consumption data, and are the random data in the screened initialization data and current consumption data, is the expectation of, which can be the average value of the screened initialization data, is the expectation of, which can be the average value of the screened current consumption data, m is the number of the two types of screened data. The two data sets are respectively subjected to m times of random sampling operations to obtain the same number of the two types of screened data.

[0060] Covariance can represent and 's correlation. When the covariance is greater than 0, it indicates that the change trends of the two variables are the same. When the covariance is less than 0, it indicates that the change trends of the two variables are opposite. From the and 's properties, it can be seen that the shorter the card reader initialization time or the less the device current consumption during the initialization process, the lower the device initialization requirement, that is, and are two positively correlated random variables, so .

[0061] The calculated covariance is used as the device classification threshold and marked as . After the data processing module receives the card reader initialization time and the device current consumption during the initialization process in the current device, it performs the above normalization process and uses them as and in the covariance calculation formula, and marks the calculation result as . After marking, the data processing module will and Send to the data classification module.

[0062] The data processing module receives the environmental data, combines the current voltages of the device at multiple time points into a voltage data set, calculates the average value and standard deviation of the voltage data set, and can use the sum of the average value and standard deviation of the voltage data set as the current device voltage coefficient, marked as , normalizes the current card reading environment humidity to obtain the temperature coefficient, and sends the voltage coefficient and temperature coefficient to the data classification module.

[0063] It should be noted that the formulas and methods for calculating various coefficients and setting thresholds above are not unique and can be adjusted according to actual situations.

[0064] The specific functions of the data classification module are as follows:

[0065] The data classification module uses the received reader data frame coefficients , reader transmission coefficients , current reader data frame coefficients and current reader transmission coefficients to classify the current reader. If and , then the current reader data frame coefficient is smaller and the current reader transmission coefficient is larger, so it is judged that the current reader has a low initialization requirement; otherwise, it is judged that the current reader has a high initialization requirement.

[0066] The data classification module judges the current device initialization requirement by comparing the received device classification thresholds and . If , then the initialization time of the reader in the current device and the power consumption of the device during the initialization process are larger, so it is judged that the current device has a high initialization requirement; otherwise, it is judged that the current device has a low initialization requirement.

[0067] After the data classification module finishes classifying the reader and the device, it starts to confirm the current card reading environment. The data classification module compares the received voltage coefficient and temperature coefficient with the preset voltage threshold and temperature threshold.

[0068] When the voltage coefficient and temperature coefficient are lower than the voltage threshold and temperature threshold, that is, the voltage change range is small, the voltage is relatively stable, the temperature is low, and the device impedance is low, it is judged that the current card reading environment is good. If the current card reading environment is good, and both the current reader and the current device have low initialization requirements, then the current reader and the current device are combined and marked and recorded in the corresponding data storage database.

[0069] If the current card reader and the current device are not combined and marked, the data classification module will automatically use the current device and the current card reader to perform multiple card reading operations, and automatically access the log data stored in the card reader after the card reading operation to obtain the number of card readings, the number of correct card readings, and the error rate when the current card reader and the current device perform card reading work, and transmit them to the feedback adjustment module.

[0070] It should be noted that the voltage threshold and the temperature threshold are set by professionals in the field according to experience and will not be analyzed here.

[0071] The feedback adjustment module receives the number of card readings, the number of correct card readings, and the error rate transmitted by the data classification module and marks them respectively as and . Calculate the card reading correct rate through the formula. The formula is: , where is the card reading correct rate, is the number of card readings, and b is the number of correct card readings. Use the logistic regression method to calculate the combination coefficient of the current card reader and the current device. The formula can be: , where is the combination coefficient, e is the natural base, and z is the combination factor. Its calculation formula can be: . After the feedback adjustment module calculates the combination coefficient of the current card reader and the current device, it compares it with the preset combination coefficient threshold to determine whether to perform a combination mark on the current combination.

[0072] If the combination coefficient of the current card reader and the current device exceeds the combination coefficient threshold, that is, the combination card reading correct rate of the current card reader and the current device is relatively high or the error rate is relatively low, then perform a combination mark on the combination of the current card reader and the current device and record it in the corresponding data storage database.

[0073] It should be noted that the combination coefficient threshold can be set according to the actual situation. The formulas mentioned in the above steps are not unique and can be adjusted according to the actual situation.

[0074] Embodiment 2, A card reading method for a card reader, as Figure 2 、 Figure 3 shown, includes the following steps:

[0075] Step 1, Collect card reader data, device data, and environmental data.

[0076] Step 2, Analyze the initialization requirements of the card reader using the principal component regression method according to the card reader data, analyze the device initialization requirements according to the device data, and use the environmental data to judge whether the card reading environment is good.

[0077] Step 3, Select whether to classify and match the card reader and the device according to the judgment result of the card reading environment and perform a combination mark.

[0078] Step 4: If the card reader and the device are not combined and marked, collect feedback data, and use the logistic regression method to determine whether to perform combined marking according to the feedback data.

[0079] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0080] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0081] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and the invention constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0082] In addition, in each embodiment of the present application, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0083] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0084] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A card reading system of a card reader, comprising: Data acquisition module, data processing module, data classification module and feedback adjustment module, and signal connections between the modules; The data acquisition module is used to collect card reader data, device data and environmental data and send them to the data processing module for processing; The data processing module is used to receive various types of data and perform different types of processing on the data, use the principal component regression method to process the card reader data and calculate the card reader data frame coefficient and the card reader transmission coefficient; After randomly sampling and filtering the equipment data, the covariance formula is used to calculate the covariance to set the equipment classification threshold; the voltage coefficient and temperature coefficient are calculated using the average value and standard deviation according to the environmental data, and the various coefficients and thresholds calculated by the data processing module are sent to the data classification module for classification processing; The data classification module is used to receive various coefficients and thresholds calculated by the data processing module and compare them respectively, classify the card reader and the device and judge whether the current card reading environment is good or not, and choose whether to classify and match the card reader and the device and make a combination mark according to the judgment result; if the card reader and the device are not marked as a combination, the data classification module automatically collects feedback data and transmits it to the feedback adjustment module; The feedback adjustment module receives the feedback data and uses the logistic regression method to calculate the combination coefficient of the card reader and the device, and determines whether to mark the current combination by comparing the device combination coefficient with a preset combination coefficient threshold; The specific steps of data classification module classification are as follows: The data classification module compares the received card reader data frame coefficient, the card reader transmission coefficient, the data frame threshold and the transmission threshold. If the card reader data frame coefficient is lower than the data frame threshold and the card reader transmission coefficient exceeds the transmission threshold, it is determined that the card reader initialization requirement is low, otherwise it is determined that the card reader initialization requirement is high; The data classification module compares the card reader initialization time, device current consumption and device classification threshold to determine the device initialization requirements and classify the device according to the initialization requirements. After the data classification module classifies the card reader and the device, it determines whether the environment is good and whether to combine the card reader and the device according to the environmental conditions.

2. The card reading system of the card reader according to claim 1, characterized in that: The specific steps for the data acquisition module to collect various data are as follows: The card reader data includes the card reader data frame length and the card reader data transmission speed; the device data includes the card reader initialization time and real-time current when the device is working; the environmental data includes the device real-time voltage and the card reading environment humidity; the data acquisition module selects a period of time as the sample time, and selects multiple time points within the sample time to collect the card reader data transmission speed; The data acquisition module will A card reader is connected to the device for initialization and recording The card reader initialization time and record the device current consumption during the initialization process; select The median initialization time of each card reader is recorded, and multiple time points are set in the initialization time of the card reader to collect the current voltage of the device and detect the current card reading environment humidity.

3. The card reading system of a card reader according to claim 1, characterized in that: When the data processing module receives the card reader data, the average of the card reader transmission speeds collected by the same card reader at multiple time points selected in the sample time is taken as the card reader transmission average speed, and the data frame lengths of multiple card readers are merged into a data frame data set; the average transmission speeds of equal card readers are collected and merged into an average speed data set, the data in the data frame data set and the average speed data set are normalized, and the normalized results of the calculated data are replaced with the corresponding data in the data set in turn to obtain a data frame coefficient data set and an average speed coefficient data set.

4. A card reading system for a card reader according to claim 1, characterized in that ； The data processing module uses the principal component regression method to process the card reader data. The specific steps are as follows: The data processing module sets the principal component interval, arranges the data in the data frame data set from large to small, selects the median in the data frame data set, sets the principal component threshold ratio with the median to determine the principal component threshold interval, expands the median to both sides according to the principal component threshold interval ratio to obtain the principal component threshold interval, marks the data in the data frame data set that is in the principal component threshold interval as the principal component data, and marks the other data as the secondary component data, and classifies and calculates the data to obtain the card reader data frame coefficient.

5. The card reading system of the card reader according to claim 4, characterized in that: When calculating the card reader data frame coefficient, the data processing module obtains the card reader data frame coefficient by setting component weights for the main component data and the sub-component data, where the main component weight and the sub-component weight can be set by multiplying the main component threshold interval ratio by the scaling ratio.

6. The card reading system of a card reader according to claim 1, characterized in that: The specific steps to determine whether the environment is good are as follows: When the voltage coefficient and the temperature coefficient are lower than the voltage threshold and the temperature threshold, the card reading environment is judged to be good; if the card reading environment is good and the initialization requirements of the card reader and the device are low, the current card reader and the current device are combined and marked and recorded in the corresponding data storage database; if the current card reader and the current device are not combined, the data classification module performs multiple card reading operations on the device and the card reader, and accesses the log data stored in the card reader after the card reading operation to obtain the number of cards read, the number of correct cards read and the error rate when the card reader and the device perform card reading operations, and transmits them to the feedback adjustment module.

7. The card reading system of the card reader according to claim 6, characterized in that: The feedback adjustment module uses the logistic regression method to determine whether to mark the current combination. The specific steps are as follows: The feedback adjustment module receives the card reading number, the correct card reading number and the error rate transmitted by the data classification module to calculate the card reading accuracy rate, which is the ratio of the correct card reading number to the card reading number; The logistic regression method is used to calculate the card reader and device combination coefficient, and the formula is: ,in is the combination coefficient, is a natural base, z is a combination factor, and if the card reader and device combination coefficient exceeds the combination coefficient threshold, the card reader and device combination is marked and recorded in the corresponding data storage database.

8. A card reading method of a card reader, based on a card reading system of a card reader according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1, collect card reader data, device data and environment data; Step 2: Analyze the initialization requirements of the card reader using the principal component regression method based on the card reader data, analyze the initialization requirements of the device based on the device data, and use the environmental data to determine whether the card reading environment is good; Step 3: Choose whether to classify and match the card reader and the device and perform combination marking according to the card reading environment judgment result; Step 4: If the card reader and the device do not perform combined marking, collect feedback data and use the logistic regression method to determine whether to perform combined marking based on the feedback data.

9. A card reader device of a card reader, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to claim 8 is implemented.

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