Self-service terminal maintenance detection method, system, medium and equipment

Abstract

The invention discloses a self-service terminal maintenance detection method and system, a medium and equipment, and the method comprises the following steps: S1, AI model training: creating a software system, enabling the software system to automatically analyze the influence of the changes of various external factors on a detection process and a detection result, obtaining an AI model most suitable for the software system through training, and carrying out the training of the AI model; the instruction is used for follow-up execution of operation parameter configuration; s2, operating parameter self-adaption: after a software system collects environment and battery data, executing an AI model to obtain operating parameters suitable for current detection, and issuing the operating parameters to detection equipment as operating configuration; s3, endurance detection: after the software system issues operation parameters to the detection equipment, the user initiates detection; and S4, outputting a report: after the endurance detection is completed, the detection equipment outputs the health value of the battery in the form of the report. The device has the advantages that the problem that detection standards and detection results are different due to different battery models is effectively solved, and power utilization resources are effectively saved.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a self-service terminal maintenance and detection method, system, medium and equipment. Background Art

[0002] With the development of science and technology, more and more self-service terminals are moving towards government, enterprises, finance and other industries, greatly reducing the labor costs and work pressure of service personnel. Portable self-service terminals are favored by more government, enterprises and institutions due to their small size and strong mobile deployment capabilities, and the accompanying after-sales maintenance needs are also potentially increasing. As we all know, the battery life of portable devices is affected by battery life. When the battery life reaches below the maintenance threshold, the after-sales process can be initiated. Most of the common battery life detection equipment on the market rely on experience values ​​to set detection parameters, and consume the released electricity in the form of electromagnetic or heat, which not only affects the accuracy of detection, but also greatly wastes energy, violating the current low-carbon and environmentally friendly development concept. The portable self-service terminal battery life detection system based on artificial intelligence technology can easily solve the above problems. The equipment can automatically adapt the detection parameters based on the specific detection environment scenario to ensure the rationality and accuracy of the detection process data; at the same time, the released energy is collected locally to avoid resource waste.

[0003] Most of the existing battery life testing equipment on the market sets operating parameters based on the experience of practitioners. First, the testing standards and strictness of different batteries are different, which leads to large differences in testing. Second, affected by the testing environment, the test results of the same battery in different temperature and humidity environments also vary greatly. Third, electricity cannot be recycled, resulting in a waste of resources.

[0004] In view of this, it is necessary to provide a self-service terminal maintenance and detection method, system, medium and equipment. Summary of the invention

[0005] The present invention provides a self-service terminal maintenance and detection method, system, medium and device, which effectively solve the problems of inaccurate terminal maintenance and detection results, waste of power resources and high cost caused by the detection process.

[0006] The technical solution adopted by the present invention is:

[0007] A self-service terminal maintenance and detection method comprises the following steps:

[0008] S1. AI model training: Create a software system that analyzes the impact of various external factors on the detection process and results. Through training, the most suitable AI model for the software system is obtained as a guide for subsequent execution of operating parameter configuration.

[0009] S2, operating parameter adaptation: After the software system collects environmental and battery data, it executes the AI ​​model to obtain operating parameters suitable for this test and sends them to the test equipment as operating configuration;

[0010] S3, battery life test: After the software system sends the complete operating parameters to the test equipment, the user initiates the test;

[0011] S4. Output report: After completing the battery life test, the testing device outputs the battery health value in the form of a report.

[0012] Furthermore, the external factors in S1 include ambient temperature, ambient humidity, ambient wind speed, battery capacity, and factory time.

[0013] Furthermore, the results of the AI ​​model trained in S1 that is most suitable for the software system include the current and voltage of battery charging and discharging, the termination current and voltage, and the static time, and the operating parameters in S2 are the results of the AI ​​model training in S1.

[0014] Furthermore, the S3 battery life detection method is to discharge the remaining power of the battery to be tested, then charge it, record the charged power during the process, and then discharge it again, record the discharged power during the process, and determine the battery health factor by comparing the charged / discharged power with the standard threshold.

[0015] Furthermore, during the charging and discharging process of the endurance test, the discharged electricity will be collected by the battery carried by the software system.

[0016] Furthermore, the electricity charged into the battery is preferably taken from the storage battery, and the insufficient remaining electricity of the battery is supplemented from the outside.

[0017] Furthermore, the health value reported by the S4 output includes the charge and discharge time and power of the battery, the standard charge and discharge time and power value of the battery, and the health score.

[0018] The system based on self-service terminal maintenance and detection includes:

[0019] The AI ​​training module is used by the software system to analyze the impact of various external factors on the detection process and detection results. Through training, the most suitable AI model for the software system is obtained as a guide for subsequent execution of operation parameter configuration;

[0020] The operating parameter adaptive module is used to execute the AI ​​model, obtain the operating parameters suitable for this test, and send them to the test equipment as the operating configuration;

[0021] The battery life detection module is used to send the running parameters to the detection equipment under the software system, and then the user initiates the detection;

[0022] The output report module is used to output the battery health value in the form of a report after completing the battery life test.

[0023] A computer-readable storage medium stores a computer program, which implements the steps of the self-service terminal maintenance and detection method when being processed and executed.

[0024] A computer device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus:

[0025] The memory is used to store computer programs;

[0026] The processor is used to execute the steps of the self-service terminal maintenance and detection method by running the program stored in the memory. The communication bus mentioned in the above terminal can be a peripheral component interconnect standard (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. The communication interface is used for communication between the above terminal and other devices. The memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Optionally, the memory can also be at least one storage device located away from the aforementioned processor. The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0027] Beneficial effects of the invention:

[0028] 1. This method adopts AI model training, operating parameter adaptation, battery life detection, and output report functions. It customizes the operating parameters of the detection equipment through artificial intelligence technology, combined with battery properties and environmental factors and other parameters, which can effectively solve the problem of different detection standards and detection results caused by different battery models; the system integrates advanced energy harvesting technology and combines intelligent power management to achieve efficient conversion and utilization of energy in maintenance, effectively saving electricity resources.

[0029] 2. The present invention does not require payment of usage fees based on traffic flow, and the self-service terminal maintenance company no longer needs to spend manpower to preset detection parameters based on personal experience. The implementation standards are unified, and the interests of customers and companies are protected.

[0030] 3. The present invention uses artificial intelligence to maintain terminal equipment, which effectively improves the efficiency of maintenance, greatly shortens the maintenance time, and can reduce the workload of maintenance personnel.

[0031] 4. By collecting the current discharged by the battery of the maintained equipment during the test with a storage battery, and then using the storage battery to charge the battery of the maintained equipment, the recycling of electric resources is achieved, the testing cost is reduced and it helps to protect the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of a self-service terminal maintenance and detection method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0034] like Figure 1 As shown, the first embodiment provided by the present application is a self-service terminal maintenance and detection method, comprising the following steps:

[0035] S1. AI model training: Create a software system that analyzes the impact of various external factors on the detection process and results. Through training, the most suitable AI model for the software system is obtained as a guide for subsequent execution of operating parameter configuration.

[0036] S2, operating parameter adaptation: After the software system collects environmental and battery data, it executes the AI ​​model to obtain operating parameters suitable for this test and sends them to the test equipment as operating configuration;

[0037] S3, battery life test: After the software system sends the complete operating parameters to the test equipment, the user initiates the test;

[0038] S4. Output report: After completing the battery life test, the testing device outputs the battery health value in the form of a report.

[0039] The above design combines AI and an integrated power collection device to formulate exclusive detection parameters based on battery properties and environmental factors, and recycles and reuses the electricity released by the detection. This not only greatly improves the uniformity of detection standards and the accuracy of results, but also saves a lot of energy.

[0040] Specifically: the external factors in S1 include ambient temperature, ambient humidity, ambient wind speed, battery capacity, factory time, etc.

[0041] In actual use, external factors such as ambient temperature, ambient humidity, ambient wind speed, battery capacity, and factory time automatically adjust the operating parameters through the machine learning model. The following is the implementation formula for adjusting the operating parameters of each external factor:

[0042] (1) Factors affecting ambient temperature: Where T is the ambient temperature, T0 is the reference temperature, a1 and b1 are model parameters, and k1 is the temperature influence coefficient.

[0043] (2) Environmental humidity influence factor: fH(H) = a2·log(1+k2H)+b2, where H is the environmental humidity, a2 and b2 are model parameters, and k2 is the humidity influence coefficient.

[0044] (3) Environmental wind speed factor: fW(W) = a3·W k3 +b3, where W is the ambient wind speed, a3 and b3 are model parameters, and k3 is the wind speed influence coefficient.

[0045] (4) Battery capacity influencing factor: fC(C) = a4·C k4 +b4, where C is the battery capacity, a4 and b4 are model parameters, and k4 is the capacity influence coefficient.

[0046] (5) Factory time impact factor: fA(A) = a5·k5A+b5, where A is the factory time, a5 and b5 are model parameters, and k5 is the time impact coefficient.

[0047] In the above design, ambient temperature, ambient humidity, ambient wind speed, battery capacity, factory time, etc. are included in the external factors for testing, which can ensure that the AI ​​model can be adaptively operated in different detection environments to improve the accuracy of the detection results.

[0048] Specifically: the results of the AI ​​model trained in S1 that is most suitable for the software system include the current and voltage of battery charging and discharging, the termination current and voltage, and the static time, and the operating parameters in S2 are the results of the AI ​​model training in S1.

[0049] In actual use, the current and voltage of the battery are the core parameters of the battery. During the battery charging and discharging process, the final termination current and voltage are also key parameters for measuring charging performance.

[0050] Charge and discharge current and voltage formula: ICC = fT(T)·fH(H)·fW(W)·fC(C)·fA(A), where ICC is the charge and discharge current. Termination current and voltage formula: Vtermination = V0-m·ICC, where V0 is the initial voltage and m is the voltage drop coefficient. Static time formula: tstatic = n·ICCk6, where n is the time coefficient and k6 is the static time influence coefficient.

[0051] In the above design, the current and voltage of battery charging and discharging, the termination current and voltage, and the static time are included in the training to obtain the results of the AI ​​model that is most suitable for the software system. This can detect the key performance of the battery and ensure all-round monitoring of the battery performance.

[0052] Specifically: the S3 battery life detection method is to discharge the remaining power of the battery to be tested, then charge it, record the charged power during the process, and then discharge it again, record the discharged power during the process, and determine the battery health factor by comparing the charged / discharged power with the standard threshold.

[0053] In actual use, the battery can be discharged by constant current discharge method, constant power discharge method, pulse discharge method or other methods.

[0054] In the above design, by comparing the ratio of the power required to fully charge the battery to the power fully output with the size of the marked threshold, it is determined whether the performance of the input power and output power of the battery is normal.

[0055] Specifically: during the charging and discharging process of the endurance test, the discharged electricity will be collected by the battery carried by the software system.

[0056] In actual use, the terminal equipment power is generally large, and the internal battery power is large. If the traditional heating resistor is used to discharge the battery, the current will be lost in the form of heat.

[0057] In the above design, the power discharged by the battery of the terminal device being detected is stored by the storage battery, which can effectively save power resources.

[0058] Specifically: the electricity charged into the battery is preferably taken from the storage battery, and the insufficient remaining electricity of the battery is supplemented from the outside.

[0059] In the above design, by outputting the electricity originally input from the battery of the terminal to be tested to the battery of the terminal to be tested, the power loss caused by the discharge-charging process of the battery of the terminal to be tested can be minimized, effectively saving costs and resources.

[0060] Specifically: the health value reported by the S4 output includes the charge and discharge time and power of the battery, the standard charge and discharge time and power value of the battery, and the health score.

[0061] In the above design, the battery charging and discharging time and power, the standard charging and discharging time and power value of the battery, and the health score are output in the report, so that the inspection personnel can intuitively understand the battery performance of the terminal under maintenance.

[0062] The second embodiment provided by the present application is a system based on self-service terminal maintenance and detection, including:

[0063] The AI ​​training module is used by the software system to analyze the impact of various external factors on the detection process and detection results. Through training, the most suitable AI model for the software system is obtained as a guide for subsequent execution of operation parameter configuration;

[0064] The operating parameter adaptive module is used to execute the AI ​​model, obtain the operating parameters suitable for this test, and send them to the test equipment as the operating configuration;

[0065] The battery life detection module is used to send the running parameters to the detection equipment under the software system, and then the user initiates the detection;

[0066] The output report module is used to output the battery health value in the form of a report after completing the battery life test.

[0067] The third embodiment provided by the present application is a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is processed and executed, the steps of the self-service terminal maintenance and detection method are implemented.

[0068] In addition, the computer-readable storage medium of this embodiment may adopt any combination of one or more computer-readable storage media, wherein the computer-readable storage medium includes electrical, optical, electromagnetic, infrared or semiconductor systems, devices or components, or any combination thereof.

[0069] The fourth embodiment provided by the present application is a computer device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus: wherein:

[0070] The memory is used to store computer programs;

[0071] The processor is used to execute the steps of the self-service terminal maintenance and detection method by running the program stored in the memory.

[0072] As an embodiment of the present invention, the communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0073] As an implementation mode of the present invention, the communication interface is used for communication between the above-mentioned terminal and other devices.

[0074] As an embodiment of the present invention, the memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0075] As an embodiment of the present invention, the above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0076] To further explain in detail, it should be understood that the above is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A self-service terminal maintenance and detection method, characterized in that: The steps include: S1. AI model training: Create a software system that analyzes the impact of various external factors on the detection process and results. Through training, the most suitable AI model for the software system is obtained as a guide for subsequent execution of operating parameter configuration. S2, operating parameter adaptation: After the software system collects environmental and battery data, it executes the AI ​​model to obtain operating parameters suitable for this test and sends them to the test equipment as operating configuration; S3, battery life test: After the software system sends the complete operating parameters to the test equipment, the user initiates the test; S4. Output report: After completing the battery life test, the testing device outputs the battery health value in the form of a report.

2. The self-service terminal maintenance and detection method according to claim 1, characterized in that: The external factors in S1 include ambient temperature, ambient humidity, ambient wind speed, battery capacity, and factory time.

3. The self-service terminal maintenance and detection method according to claim 1, characterized in that: The results of the AI ​​model trained in S1 that is most suitable for the software system include the current and voltage of battery charging and discharging, the termination current and voltage, and the static time, and the operating parameters in S2 are the results of the AI ​​model training in S1.

4. The self-service terminal maintenance and detection method according to claim 1, characterized in that: The S3 battery life detection method is to discharge the remaining power of the battery to be tested, then charge it, record the charged power during the process, and then discharge it again, record the discharged power during the process, and determine the battery health factor by comparing the charged / discharged power with the standard threshold.

5. The self-service terminal maintenance and detection method according to claim 4, characterized in that: During the charging and discharging process of the endurance test, the discharged electricity will be collected by the battery carried by the software system.

6. The self-service terminal maintenance and detection method according to claim 5, characterized in that: The electricity charged into the battery is preferably taken from the storage battery, and the insufficient remaining electricity of the battery is supplemented from the outside.

7. The self-service terminal maintenance and detection method according to claim 1, characterized in that: The health value reported by the S4 output includes the charge and discharge time and power of the battery, the standard charge and discharge time and power value of the battery, and the health score.

8. The system based on self-service terminal maintenance and detection is characterized by: include, The AI ​​training module is used by the software system to analyze the impact of various external factors on the detection process and detection results. Through training, the most suitable AI model for the software system is obtained as a guide for subsequent execution of operation parameter configuration; The operating parameter adaptive module is used to execute the AI ​​model, obtain the operating parameters suitable for this test, and send them to the test equipment as the operating configuration; The battery life detection module is used to send the running parameters to the detection equipment under the software system, and then the user initiates the detection; The output report module is used to output the battery health value in the form of a report after completing the battery life test.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is processed and executed, the steps of the self-service terminal maintenance and detection method according to any one of claims 1 to 7 are implemented.

10. A computer device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus: The memory is used to store computer programs; The processor is used to execute the steps of the self-service terminal maintenance and detection method according to any one of claims 1 to 7 by running the program stored in the memory.

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