LU505125B1 - Intelligent error detection system for mobile phone pos machine in offline store - Google Patents
Intelligent error detection system for mobile phone pos machine in offline store Download PDFInfo
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- LU505125B1 LU505125B1 LU505125A LU505125A LU505125B1 LU 505125 B1 LU505125 B1 LU 505125B1 LU 505125 A LU505125 A LU 505125A LU 505125 A LU505125 A LU 505125A LU 505125 B1 LU505125 B1 LU 505125B1
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- phone pos
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- 238000001514 detection method Methods 0.000 title claims abstract description 249
- 230000007613 environmental effect Effects 0.000 claims abstract description 66
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 230000002159 abnormal effect Effects 0.000 claims description 16
- 238000000034 method Methods 0.000 claims description 14
- 230000005856 abnormality Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/24—Arrangements for testing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
- H04L43/0817—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The present invention provides an intelligent error detection system for a mobile phone POS machine in an offline store, including a use analysis module configured to analyze the use condition of the POS machine, an environment detection module configured to detect the use environment of the POS machine, a hardware detection module configured to detect the hardware condition of the POS machine, and an intelligent detection module configured to intelligently detect the POS machine after receiving an environmental deviation value and a hardware performance value. The present invention provides matching detection standards for error detection of the mobile phone POS machine in the offline store.
Description
INTELLIGENT ERROR DETECTION SYSTEM FOR MOBILE PHONE POS 0505125
MACHINE IN OFFLINE STORE
The present invention belongs to the field of mobile phone POS machines, and relates to error detection technology, in particular to an intelligent error detection system for a mobile phone POS machine in an offline store.
A mobile phone POS machine is an RF-SIM card terminal reader. A reader terminal is connected to a data server through CDMA, GPRS, and TCP/IP. At work, the "swiping card" of a mobile phone equipped with RF-SIM is performed on the mobile phone POS machine and relevant business information (transaction type, amount, points, etc.) are inputted, and the POS machine sends the obtained information to the data server through various networks.
When a mobile phone POS machine in an offline store is in use, users can't detect the abnormal condition of the mobile phone POS machines. Although some mobile phone POS machines are equipped with corresponding abnormal alarm functions, corresponding error detection measures can't be matched with the mobile phone POS machine according to the use condition. Therefore, an intelligent error detection system for a mobile phone POS machine in an offline store 1s proposed.
Aiming at the shortcomings of the prior art, an object of the present invention 1s to provide an intelligent error detection system for a mobile phone POS machine in an offline store.
The technical problem to be solved by the present invention is as follows.
How to match corresponding detection standards for error detection of the mobile phone
POS machine in the offline store based on the use condition.
The object of the present invention is achieved by the following technical solutions.
An intelligent error detection system for a mobile phone POS machine in an offline store includes a data acquisition module, a user terminal, a use analysis module, an environment detection module, an intelligent detection module, a hardware detection module, a payment terminal and a server. The payment terminal includes an alarm unit configured to alarm the abnormality generated by the mobile phone POS machine.
The data acquisition module is configured to acquire hardware data and environmental data 905125 of the mobile phone POS machine and send the data to the server. The server sends the hardware data to a hardware detection module and sends the environmental data to an environment detection module.
The user terminal is configured to input an out-of-factory time of the mobile phone POS machine and send the out-of-factory time to the use analysis module through the server. The use analysis module is configured to analyze the use condition of the mobile phone POS machine to obtain a detection grade of the mobile phone POS machine, and to feed back the detection grade to the server. A server is configured to send preset detection data of the mobile phone POS machine to the intelligent detection module according to the detection grade.
The server stores standard environment parameters and standard hardware parameters of the mobile phone POS machine, and sends the standard environment parameters corresponding to the mobile phone POS machine to the environment detection module and the corresponding standard hardware parameters to the hardware detection module.
The environment detection module is configured to detect the use environment of the mobile phone POS machine to obtain an environmental deviation value of the mobile phone POS machine in an intelligent detection period or generate an environmental anomaly signal.
The hardware detection module is configured to detect the hardware condition of the mobile phone POS machine to obtain a hardware performance value or a hardware anomaly signal of the mobile phone POS machine in the intelligent detection period.
The intelligent detection module is configured to intelligently detect the mobile phone POS machine after receiving the environmental deviation value and the hardware performance value and generate a detection normal signal or a detection abnormal signal.
Further, the hardware data includes a power supply temperature value of the mobile phone
POS machine and use durations of individual components.
The environmental data includes an upload network speed value and a download network speed value of the mobile phone POS machine.
The preset detection data includes an environmental deviation threshold and a hardware performance threshold.
The standard environment parameters include an upload network speed threshold and a download network speed threshold. 10505125
The standard hardware parameters include a power supply preset temperature value and preset use durations of the individual components.
Further, the analysis process of the use analysis module includes the following steps: acquiring the out-of-factory time of the mobile phone POS machine and a current time of the server; calculating a use duration of the mobile phone POS machine; and determining, in comparison with the use duration and a duration threshold, the detection grade of the mobile phone POS machine as a third detection grade, a second detection grade or a first detection grade.
Further, the first detection grade 1s higher than the second detection grade, and the second detection grade is higher than the third detection grade.
Further, the detection process of the environment detection module includes the following steps: setting an intelligent detection period of the mobile phone POS machine, setting a plurality of time points in the intelligent detection period, and acquiring upload network speed values and download network speed values corresponding to the plurality of time points of the mobile phone POS machine; generating the environmental anomaly signal if the upload network speed values at all time points are less than the upload network speed threshold or the download network speed values at all the time points are less than the download network speed threshold, calculating differences between the upload network speed values and the upload network speed threshold at various time points if the upload network speed value at any time point is greater than or equal to the upload network speed threshold or the download network speed value at any time point is less than the download network speed threshold to obtain upload network speed differences of the mobile phone POS machine at the various time points, and calculating differences between the download network speed values and the download network speed threshold at the various time points to obtain download network speed differences of the mobile phone POS machine at the various time points; summing the upload network speed differences at the various time points with division by the number of time points to obtain an upload network speed deviation value of the mobile 905125 phone POS machine in the intelligent detection period, and summing the download network speed differences at the various time points with division by the number of time points to obtain a download network speed deviation value of the mobile phone POS machine in the intelligent detection period; and calculating the environmental deviation value of the mobile phone POS machine in the intelligent detection period.
Further, the environment detection module feeds back the environmental deviation value of the mobile phone POS machine in the intelligent detection period or generates the environmental anomaly signal to the server, if the server receiving the environmental anomaly signal, an alarm instruction being generated and loaded to an alarm unit, and the alarm unit sending out an alarm sound after receiving the alarm instruction, and if the server receiving the environmental deviation value HPu of the mobile phone POS machine in the intelligent detection period, the environmental deviation value HPu being transmitted to the intelligent detection module.
Further, the detection process of the hardware detection module includes the following steps: acquiring the preset use durations of the individual components in the mobile phone POS machine, and generating the hardware anomaly signal if the use duration of the mobile phone
POS machine exceeds a preset use duration of any component; subtracting the use duration of the mobile phone POS machine from the preset use duration if the use duration of the mobile phone POS machine does not exceed the preset use durations of all components to obtain remaining use durations of the individual components in the mobile phone POS machine, traversing and comparing the remaining use durations of the individual components in the mobile phone POS machine to obtain a minimum remaining use duration, and calibrating the minimum remaining use duration in the mobile phone POS machine as a hardware use duration of the mobile phone POS machine; combining with the intelligent detection period of the mobile phone POS machine and setting the plurality of time points in the intelligent detection period to acquire power supply temperature values corresponding to the plurality of time points of the mobile phone poy 505125 machine: generating the hardware anomaly signal if the power supply temperature values at all the time points are greater than or equal to the power supply preset temperature value; 5 calculating differences between the power supply temperature values at the individual components and the power supply preset temperature value if the power supply temperature value at any time point is less than the power supply preset temperature value to obtain power supply temperature differences of the mobile phone POS machine at the various time points; summing the power supply temperature differences at the various time points with division by the number of time points to obtain a power supply temperature deviation value of the mobile phone POS machine in the intelligent detection period; and calculating the hardware performance value of the mobile phone POS machine in the intelligent detection period.
Further, the hardware detection module feeds back the hardware performance value or the hardware anomaly signal of the mobile phone POS machine in the intelligent detection period to the server, if the server receiving the hardware anomaly signal, an alarm instruction being generated and loaded to the alarm unit, and the alarm unit sending out an alarm sound after receiving the alarm instruction, and if the server receiving the hardware performance value YXu of the mobile phone POS machine in the intelligent detection period, the hardware performance value YXu being transmitted to the intelligent detection module.
Further, the working process of the intelligent detection module includes the following steps: obtaining a corresponding environmental deviation threshold and the hardware performance threshold according to the detection grade of the mobile phone POS machine; comparing the environmental deviation threshold to the environmental deviation value, and comparing the hardware performance threshold to the hardware performance value; and generating the detection normal signal or the detection abnormal signal according to comparison results.
Further, the intelligent detection module feeds back the detection normal signal or the 905125 detection abnormal signal to the server, if the server receiving the detection normal signal, no operation being performed, and if the server receiving the detection abnormal signal, an alarm instruction being generated and loaded to the alarm unit, and the alarm unit sending out an alarm sound after receiving the alarm instruction.
Compared with the prior art, the present invention has the following beneficial effects.
According to the present invention, the use condition of the mobile phone POS machine is analyzed by the use analysis module, the detection grade of the mobile phone POS machine is obtained through analysis, and the standard environment parameters, standard hardware parameters and preset detection data of the mobile phone POS machine are sent to corresponding functional modules according to the detection grade. Meanwhile, the use environment of the mobile phone POS machine is detected by the environment detection module, and the environmental deviation value or environmental anomaly signal feedback of the mobile phone
POS machine in the intelligent detection period is detected, and then the hardware condition of the mobile phone POS machine is detected by the hardware detection module to obtain the hardware performance value or the hardware anomaly signal of the mobile phone POS machine in the intelligent detection period. The mobile phone POS machine is intelligently detected by the intelligent detection module after combining the environmental deviation value and the hardware performance value, generating a detection normal signal or a detection abnormal signal, and corresponding operations are performed according to signals. The present invention provides matching detection standards for the error detection of the mobile phone POS machine combining the use condition of the mobile phone POS machine in the offline store.
In order to facilitate the understanding of those skilled in the art, the present invention will be further described with reference to the attached drawings.
FIG. 1 is an overall system block diagram according to the present invention.
Technical solutions of the present invention will be described clearly and completely in the following with reference to examples. Obviously, the described examples are only some, rather than all examples of the present invention. Based on the examples in the present invention, all 0°12 other examples obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.
In an example, referring to FIG. 1, FIG. 1 shows an intelligent error detection system for a mobile phone POS machine in an offline store. The mobile phone POS machine in the present application are mainly used in the offline store, and the system specifically includes a data acquisition module, a user terminal, a use analysis module, an environment detection module, an intelligent detection module, a hardware detection module, a payment terminal and a server.
The server is connected to a plurality of payment terminals, and each payment terminal includes an alarm unit arranged in the payment terminal and configured to alarm the abnormality generated by the mobile phone POS machine.
In the specific implementation, the payment terminal can be a mobile phone POS machine, a private mobile phone, and a computer, which is the mobile phone POS machine in the present invention.
The data acquisition module is configured to acquire hardware data and environmental data of the mobile phone POS machine and send the hardware data and the environmental data to the server.
What needs to be specified is that the hardware data includes a power supply temperature value of the mobile phone POS machine and use durations of individual components. The environmental data includes an upload network speed value and a download network speed value of the mobile phone POS machine.
In the specific implementation, the data acquisition module is a sensor module, a timer and other related equipment arranged in the mobile phone POS machine, and the data acquisition module can also be a network speed measuring module in the mobile phone POS machine. The sensor module can acquire temperature data of the mobile phone POS machine, the timer can record the use duration of the mobile phone POS machine, and the network speed measuring module is configured to measure the upload network speed value and the download network speed value of the mobile phone POS machine and display values.
The server sends the hardware data to the hardware detection module, and sends the environmental data to the environment detection module.
The user terminal is configured for the staff to register and log in the system after inputting 20° 2 personal information.
The personal information includes the name of the staff, the mobile phone number of real-name authentication, etc.
The staff inputs an out-of-factory time of the mobile phone POS machine through the user terminal and sends the out-of-factory time to the server, and the server sends the out-of-factory time of the mobile phone POS machine to the use analysis module.
The use analysis module is configured to analyze the use condition of the mobile phone
POS machine, and the analysis process includes the following steps.
At step one: the mobile phone POS machine is marked as u, where u=1, 2, ..., z, z being a positive integer; the out-of-factory time of the mobile phone POS machine is acquired, and the out-of-factory time is marked as TCu.
At step two, a current time of the server is acquired and the current time of the server is marked as TDu.
At step three, the use duration TSu of the mobile phone POS machine is calculated by the formula TSu = TDu - TCu.
At step four, if TSu << XI, the detection grade of the mobile phone POS machine is the third detection grade; if X1 << TSu < X2, the detection grade of the mobile phone POS machine is the second detection grade; and if X2 =< TSu, the detection grade of the mobile phone POS machine is the first detection grade, where X1 and X2 are duration thresholds, and X1 < X2.
The use analysis module feeds back the detection grade of the mobile phone POS machine to the server, and the server sends preset detection data of the mobile phone POS machine to the intelligent detection module according to the detection grade.
Understandably, the preset detection data includes an environmental deviation threshold and a hardware performance threshold. Meanwhile, the first detection grade is higher than the second detection grade, and the second detection grade 1s higher than the third detection grade.
For example, if the detection grade of the mobile phone POS machine is the first detection grade, the environmental deviation threshold is 20 and the hardware performance threshold is
100; if the detection grade of the mobile phone POS machine is the second detection grade, the 905125 environmental deviation threshold is 10 and the hardware performance threshold is 150; and if the detection grade of the mobile phone POS machine is the third detection grade, the environmental deviation threshold is 5 and the hardware performance threshold is 200. Therefore, the higher the detection grade, the greater the corresponding hardware performance threshold.
In the example, the server stores standard environment parameters and standard hardware parameters of the mobile phone POS machine, and sends the standard environment parameters corresponding to the mobile phone POS machine to the environment detection module and the corresponding standard hardware parameters to the hardware detection module.
Specifically, the standard environment parameters include an upload network speed threshold and a download network speed threshold. The standard hardware parameters include a power supply preset temperature value and preset use durations of individual components.
Specifically, the corresponding standard environment parameters and standard hardware parameters can be obtained according to the brand and model of the mobile phone POS machine.
The environment detection module is configured to detect the use environment of the mobile phone POS machine, and the detection process includes the following steps.
At step S1, an intelligent detection period of the mobile phone POS machine is set, a plurality of time points are set in the intelligent detection period, and the upload network speed value and download network speed value corresponding to the mobile phone POS machine at a plurality of time points are acquired.
At step S2, if the upload network speed values at all time points are less than the upload network speed threshold or the download network speed values at all the time points are less than the download network speed threshold, an environmental anomaly signal is generated; and if the upload network speed value at any time point is greater than or equal to the upload network speed threshold or the download network speed value at any time point is less than the download network speed threshold, the next step is executed.
At step S3, differences between the upload network speed values at various time points and the upload network speed threshold are calculated to obtain upload network speed differences of the mobile phone POS machine at the various time points; and differences between the download network speed values and the download network speed threshold at the various time points are calculated to obtain download network speed differences. 905125 of the mobile phone POS machine at the various time points.
At step S4, the upload network speed differences at the various time points are summed with division by the number of time points to obtain an upload network speed deviation value
SSPu of the mobile phone POS machine in the intelligent detection period, and the download network speed differences at the various time points are summed with division by the number of time points to obtain a download network speed deviation value XSPu of the mobile phone POS machine in the intelligent detection period.
At step S5, an environmental deviation value HPU of the mobile phone POS machine in the intelligent detection period is calculated by the formula HPu = SSPu x al + XSPu x a2. In the formula, al and a2 are weight coefficients of fixed values, and values of al and a2 are greater than zero. In the specific implementation, the values of the weight coefficients do not affect the positive-negative ratio relationship between the parameters and a result value.
The environment detection module feeds back the environmental deviation value HPu or environmental anomaly signal of the mobile phone POS machine in the intelligent detection period to the server, if the server receives the environmental anomaly signal, an alarm instruction is generated and loaded to an alarm unit, and the alarm unit sends out an alarm sound after receiving the alarm instruction, and if the server receives the environmental deviation value HPu of the mobile phone POS machine in the intelligent detection period, the environmental deviation value HPu is transmitted to the intelligent detection module.
The hardware detection module is configured to detect the hardware condition of the mobile phone POS machine, and the detection process includes the following steps.
At step SS1: the preset use durations of the individual components in the mobile phone POS machine are acquired.
At step SS2: if the use duration of the mobile phone POS machine exceeds a preset use duration of any component, the hardware anomaly signal is generated; and if the use duration of the mobile phone POS machine does not exceed the preset use durations of all components, the next step is executed.
At step SS3, the use duration of the mobile phone POS machine is subtracted from the 905725 preset use duration to obtain remaining use durations of the individual components in the mobile phone POS machine, the remaining use durations of the individual components in the mobile phone POS machine are traversed and compared to obtain a minimum remaining use duration, and the minimum remaining use duration in the mobile phone POS machine is calibrated as a hardware use duration TYu of the mobile phone POS machine.
At step SS4, the intelligent detection period of the mobile phone POS machine is combined and the plurality of time points in the intelligent detection period are set to acquire power supply temperature values corresponding to the plurality of time points of the mobile phone POS machine; if the power supply temperature values at all the time points are greater than or equal to the power supply preset temperature value, the hardware anomaly signal is generated; and if the power supply temperature value at any time point is less than the power supply preset temperature value, the next step is executed.
At step SSS: differences between the power supply temperature values at the individual components and the power supply preset temperature value are calculated to obtain power supply temperature differences of the mobile phone POS machine at the various time points; and the power supply temperature differences at the various time points are summed with division by the number of time points to obtain a power supply temperature deviation value
DWPu of the mobile phone POS machine in the intelligent detection period.
At step SS6: a hardware performance value YXu of the mobile phone POS machine in the intelligent detection period is calculated by the formula YXu = [Yu xbl In the formula, bl
DWPu x b2 and b2 are proportional coefficients of fixed values, values of bl and b2 are greater than zero, and e is a natural constant; the hardware detection module feeds back the hardware performance value YXu or the hardware anomaly signal of the mobile phone POS machine in the intelligent detection period to the server; if the server receives the hardware anomaly signal, an alarm instruction is generated and loaded to the alarm unit, and the alarm unit sends out an alarm sound after receiving the alarm instruction, and 0505125 if the server receives the hardware performance value YXu of the mobile phone POS machine in the intelligent detection period, the hardware performance value YXu 1s transmitted to the intelligent detection module.
The intelligent detection module is configured to intelligently detect the mobile phone POS machine after receiving the environmental deviation value and the hardware performance value, and the working process includes the following steps.
At step Pl: a corresponding environmental deviation threshold and the hardware performance threshold are obtained according to the detection grade of the mobile phone POS machine, and the environmental deviation threshold and the hardware performance threshold are marked as YHPu and YY Xu, respectively.
At step P2, if HPu < YHPu and YXu > YYXu, the detection normal signal is generated.
At step P3,if HPu = YHPu and YXu > YYXu, or HPu < YHPu and YXu < YYXu, orHPu = YHPu and YXu < YYXu, the detection anomaly signal is generated.
The intelligent detection module feeds back the detection normal signal or the detection abnormal signal to the server, if the server receiving the detection normal signal, no operation being performed, and if the server receiving the detection abnormal signal, an alarm instruction being generated and loaded to the alarm unit, and the alarm unit sending out an alarm sound after receiving the alarm instruction.
In the present application, if there is a corresponding calculation formula, the above calculation formulas perform de-dimensionalized numerical calculation. The sizes of the coefficients such as weight coefficient and proportional coefficient existing in the formula are set to quantify various parameters to obtain a result value. The sizes of the weight coefficient and proportional coefficient do not affect the proportional relationship between the parameters and the result value.
In another example, based on another idea of the unified invention, a working method of an intelligent error detection system for a mobile phone POS machine in an offline store is proposed.
The working method is as follows.
At step S100, staff inputs an out-of-factory time of the mobile phone POS machine through 20> 12° a user terminal and sends the out-of-factory time to a server, and the server sends the out-of-factory time of the mobile phone POS machine to a use analysis module; the use condition of the mobile phone POS machine is analyzed through the use analysis module, and the mobile phone POS machine is marked as u; then the out-of-factory time TCu of the mobile phone POS machine and a current time TDu of the server are acquired; a use duration TSu of the mobile phone POS machine is calculated by the formula TSu = TDu - TCu; if TSu < XI, a detection grade of the mobile phone POS machine is a third detection grade, if X1 << TSu<X2, a detection grade of the mobile phone POS machine is a second detection grade, and if X2 <
TSu, a detection grade of the mobile phone POS machine is a first detection grade; and the use analysis module feeds back the detection grade of the mobile phone POS machine to the server, the server sends standard environment parameters of the mobile phone POS machine to an environment detection module, sends standard hardware parameters of the mobile phone POS machine to a hardware detection module, and sends preset detection data of the mobile phone
POS machine to an intelligent detection module according to the detection grade.
At step S200, hardware data and environmental data of the mobile phone POS machine are acquired by a data acquisition module and sent to the server, the server sends the hardware data to the hardware detection module, and sends the environmental data to the environment detection module. Meanwhile, the server stores the standard environment parameters and the standard hardware parameters of the mobile phone POS machine, sends the standard environment parameters to the environment detection module, and sends the preset hardware number of the mobile phone POS machine to the hardware detection module.
At step S300, the use environment of the mobile phone POS machine is detected by an environment detection module, an intelligent detection period of the mobile phone POS machine is set, a plurality of time points are set in the intelligent detection period, and upload network speed values and download network speed values corresponding to the plurality of time points of the mobile phone POS machine are obtained; if the upload network speed values at all time points are less than a upload network speed threshold or the download network speed values at all the time points are less than a download network speed threshold, an environmental abnormality signal is generated, and if the upload network speed value at any time point is greater than or equal to the upload network speed threshold or the download network speed value 905125 at any time point is less than the download network speed threshold, differences between the upload network speed values at the various time points and the upload network speed threshold are calculated to obtain upload network speed differences of the mobile phone POS machine at the various time points, and differences between the download network speed values at the various time points and the download network speed threshold are calculated to obtain download network speed differences of the mobile phone POS machine at the various time points; the upload network speed differences at the various time points are summed with division by the number of time points to obtain an upload network speed deviation value SSPu of the mobile phone POS machine in the intelligent detection period, and the download network speed differences at the various time points are summed with division by the number of time points to obtain a download network speed deviation value XSPu of the mobile phone POS machine in the intelligent detection period; an environmental deviation value HPu of the mobile phone POS machine in the intelligent detection period is calculated by the formula HPu = SSPu x al + XSPu x a2, and the environment detection module feeds back the environmental deviation value HPu or an environmental anomaly signal of the mobile phone POS machine in the intelligent detection period to the server; if the server receives the environmental anomaly signal, an alarm instruction is generated and loaded to an alarm unit, and the alarm unit sends out an alarm sound after receiving the alarm instruction, and if the server receives the environmental deviation value
HPu of the mobile phone POS machine in the intelligent detection period, the environmental deviation value HPu is transmitted to the intelligent detection module.
At step S400, the hardware condition of the mobile phone POS machine is detected by the hardware detection module to obtain preset use durations of individual components in the mobile phone POS machine; if the use duration of the mobile phone POS machine exceeds a preset use duration of any component, a hardware anomaly signal is generated, and if the use duration of the mobile phone POS machine does not exceed the preset use durations of all components, the use duration of the mobile phone POS machine is subtracted from the preset use duration to obtain remaining use durations of the individual components in the mobile phone POS machine, the remaining use durations of the individual components in the mobile phone POS machine are traversed and compared to obtain a minimum remaining use duration, and the minimum remaining use duration in the mobile phone POS machine is calibrated as a hardware use 905725 duration TYu of the mobile phone POS machine; the intelligent detection period of the mobile phone POS machine is combined and the plurality of time points are set in the intelligent detection period to acquire power supply temperature values corresponding to the plurality of time points of the mobile phone POS machine; if the power supply temperature values at all the time points are greater than or equal to the power supply preset temperature value, the hardware anomaly signal is generated; if the power supply temperature value at any time point is less than the power supply preset temperature value, differences between the power supply temperature values at the individual components and the power supply preset temperature value are calculated to obtain power supply temperature differences of the mobile phone POS machine at the various time points; the power supply temperature differences DWPu at the various time points are summed with division by the number of time points to obtain a power supply temperature deviation value of the mobile phone POS machine in the intelligent detection period; the hardware performance value YXu of the mobile phone POS machine in the intelligent detection period is calculated by the formula YXu TU xbl and the hardware detection
DWPu x b2 module feeds back the hardware performance value YXu or the hardware anomaly signal of the mobile phone POS machine in the intelligent detection period to the server, if the server receives the hardware anomaly signal, an alarm instruction is generated and loaded to an alarm unit, and the alarm unit sends out an alarm sound after receiving the alarm instruction, and if the server receives the hardware performance value YXu of the mobile phone POS machine in the intelligent detection period, the hardware performance value YXu is transmitted to the intelligent detection module.
At step S500, the intelligent detection module intelligently detects the mobile phone POS machine after receiving the environmental deviation value and the hardware performance value; a corresponding environmental deviation threshold and a hardware performance threshold are obtained according to the detection grade of the mobile phone POS machine, and the environmental deviation threshold and the hardware performance threshold are marked as YHPu and YYXu, respectively; if HPU < YHPU and YXU > YYXu, a detection normal signal is generated, and if HPu = YHPu and YXu > YYXu, or HPu < YHPu and YXu<<YYXu, or HPu
= YHPu and YXu < YYXu, a detection abnormal signal is generated; the intelligent 905725 detection module feeds back the detection normal signal or the detection abnormal signal to the server; if the server receives the detection normal signal, no operation is performed, and if the server receives the detection abnormal signal, an alarm instruction is generated and loaded to an alarm unit, and the alarm unit sends out an alarm sound after receiving the alarm instruction.
The preferred examples of the present invention disclosed above are only used to help explain the present invention. The preferred examples do not set forth all the details and do not limit to the specific examples of the present invention. Obviously, many modifications and variations can be made according to the contents of in the specification. These examples are selected and described in detail in the specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the technical field can better understand and make use of the present invention. The present invention is limited only by the appended claims and full scope and equivalents thereof.
Claims (8)
1. An intelligent error detection system for a mobile phone POS machine in an offline store, comprising: a data acquisition module, configured to acquire hardware data and environmental data of the mobile phone POS machine; a user terminal, configured to input an out-of-factory time of the mobile phone POS machine: a use analysis module, configured to analyze the use condition of the mobile phone POS machine to obtain a detection grade of the mobile phone POS machine; a server, configured to send preset detection data of the mobile phone POS machine to an intelligent detection module according to the detection grade, the server being further configured to store standard environment parameters and standard hardware parameters of the mobile phone POS machine; an environment detection module, configured to detect the use environment of the mobile phone POS machine to obtain an environmental deviation value of the mobile phone POS machine in an intelligent detection period or generate an environmental anomaly signal; a hardware detection module, configured to detect the hardware condition of the mobile phone POS machine to obtain a hardware performance value or a hardware anomaly signal of the mobile phone POS machine in the intelligent detection period; and the intelligent detection module, configured to intelligently detect the mobile phone POS machine and generate a detection normal signal or a detection abnormal signal; the detection process of the environment detection module comprising the following steps: setting an intelligent detection period of the mobile phone POS machine, setting a plurality of time points in the intelligent detection period, and acquiring upload network speed values and download network speed values corresponding to the plurality of time points of the mobile phone POS machine, generating the environmental anomaly signal if the upload network speed values at all time points are less than the upload network speed threshold or the download network speed values at all the time points are less than the download network speed threshold, calculating differences between the upload network speed values and the upload network speed threshold at various time points if the upload network speed value at any time point TS 905125 greater than or equal to the upload network speed threshold or the download network speed value at any time point is less than the download network speed threshold to obtain upload network speed differences of the mobile phone POS machine at the various time points, and at the same time, calculating differences between the download network speed values and the download network speed threshold at the various time points to obtain download network speed differences of the mobile phone POS machine at the various time points, summing the upload network speed differences at the various time points with division by the number of time points to obtain an upload network speed deviation value of the mobile phone POS machine in the intelligent detection period, and summing the download network speed differences at the various time points with division by the number of time points to obtain a download network speed deviation value of the mobile phone POS machine in the intelligent detection period, and calculating an environmental deviation value HPu of the mobile phone POS machine in the intelligent detection period by the formula HPu = SSPu X al + XSPu X a2, in the formula, al and a2 being weight coefficients of fixed values, and values of al and a2 being greater than zero; the environment detection module feeding back the environmental deviation value of the mobile phone POS machine in the intelligent detection period or generating the environmental anomaly signal to the server, if the server receiving the environmental anomaly signal, an alarm instruction being generated and loaded to an alarm unit, and the alarm unit sending out an alarm sound after receiving the alarm instruction, and if the server receiving the environmental deviation value HPu of the mobile phone POS machine in the intelligent detection period, the environmental deviation value HPu being transmitted to the intelligent detection module.
2. The intelligent error detection system for a mobile phone POS machine in an offline store according to claim 1, wherein the hardware data comprises a power supply temperature value of the mobile phone POS machine and use durations of individual components; the environmental data comprises an upload network speed value and a download network speed value of the mobile phone POS machine; 0505125 the preset detection data comprises an environmental deviation threshold and a hardware performance threshold; the standard environment parameters comprise an upload network speed threshold and a download network speed threshold; and the standard hardware parameters comprise a power supply preset temperature value and preset use durations of the individual components.
3. The intelligent error detection system for a mobile phone POS machine in an offline store according to claim 2, wherein the analysis process of the use analysis module comprises the following steps: acquiring the out-of-factory time of the mobile phone POS machine and a current time of the server; calculating a use duration of the mobile phone POS machine; and determining, in comparison with the use duration and a duration threshold, the detection grade of the mobile phone POS machine as a third detection grade, a second detection grade or a first detection grade.
4. The intelligent error detection system for a mobile phone POS machine in an offline store according to claim 3, wherein the first detection grade 1s higher than the second detection grade, and the second detection grade 1s higher than the third detection grade.
5. The intelligent error detection system for a mobile phone POS machine in an offline store according to claim 4, wherein the detection process of the hardware detection module comprises the following steps: acquiring the preset use durations of the individual components in the mobile phone POS machine, and generating the hardware anomaly signal if the use duration of the mobile phone POS machine exceeds a preset use duration of any component, subtracting the use duration of the mobile phone POS machine from the preset use duration if the use duration of the mobile phone POS machine does not exceed the preset use durations of all components to obtain remaining use durations of the individual components in the mobile phone POS machine, traversing and comparing the remaining use durations of the individual components in the mobile phone POS machine to obtain a minimum remaining use duration, and calibrating the minimum remaining use duration in the mobile phone POS machine as a 905725 hardware use duration of the mobile phone POS machine; combining with the intelligent detection period of the mobile phone POS machine and setting the plurality of time points in the intelligent detection period to acquire power supply temperature values corresponding to the plurality of time points of the mobile phone POS machine; generating the hardware anomaly signal if the power supply temperature values at all the time points are greater than or equal to the power supply preset temperature value; calculating differences between the power supply temperature values at the individual components and the power supply preset temperature value if the power supply temperature value at any time point is less than the power supply preset temperature value to obtain power supply temperature differences of the mobile phone POS machine at the various time points; summing the power supply temperature differences at the various time points with division by the number of time points to obtain a power supply temperature deviation value of the mobile phone POS machine in the intelligent detection period; and calculating the hardware performance value of the mobile phone POS machine in the intelligent detection period.
6. The intelligent error detection system for a mobile phone POS machine in an offline store according to claim 5, wherein the hardware detection module feeds back the hardware performance value or the hardware anomaly signal of the mobile phone POS machine in the intelligent detection period to the server, if the server receiving the hardware anomaly signal, an alarm instruction being generated and loaded to the alarm unit, and the alarm unit sending out an alarm sound after receiving the alarm instruction, and if the server receiving the hardware performance value YXu of the mobile phone POS machine in the intelligent detection period, the hardware performance value YXu being transmitted to the intelligent detection module.
7. The intelligent error detection system for a mobile phone POS machine in an offline store according to claim 6, wherein the working process of the intelligent detection module comprises the following steps:
obtaining a corresponding environmental deviation threshold and the hardware performance 905725 threshold according to the detection grade of the mobile phone POS machine; comparing the environmental deviation threshold to the environmental deviation value, and comparing the hardware performance threshold to the hardware performance value; and generating the detection normal signal or the detection abnormal signal according to comparison results.
8. The intelligent error detection system for a mobile phone POS machine in an offline store according to claim 7, wherein the intelligent detection module feeds back the detection normal signal or the detection abnormal signal to the server, if the server receiving the detection normal signal, no operation being performed, and if the server receiving the detection abnormal signal, an alarm instruction being generated and loaded to the alarm unit, and the alarm unit sending out an alarm sound after receiving the alarm instruction.
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| CN202310641028.2A CN116366757B (en) | 2023-06-01 | 2023-06-01 | Intelligent detection system for errors of mobile phone POS machine of off-line store |
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| JP2011210117A (en) * | 2010-03-30 | 2011-10-20 | Sharp Corp | Pos terminal device and pos terminal control method |
| CN109408268B (en) * | 2018-10-09 | 2021-11-19 | 浪潮软件股份有限公司 | New retail code scanning data anomaly detection method based on cloud POS machine |
| CN114422405B (en) * | 2022-03-16 | 2022-07-01 | 深圳市思迅软件股份有限公司 | Safety detection system and method for payment environment |
| CN115166500A (en) * | 2022-06-30 | 2022-10-11 | 南京南瑞信息通信科技有限公司 | Direct current breaker equipment state analysis system based on power grid resource business middle platform |
| CN115238933B (en) * | 2022-09-23 | 2022-12-09 | 西安德纳检验检测有限公司 | Wind turbine generator inertia response detection method, device and system based on multipoint measurement |
| CN115437301B (en) * | 2022-10-14 | 2023-06-16 | 淄博瀚泓环保科技有限公司 | Water treatment unit operating condition monitoring management and control system |
| CN115663312B (en) * | 2022-12-27 | 2023-05-30 | 深圳市今朝时代股份有限公司 | Battery operation monitoring system and method based on battery protection |
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