A POS machine data remote management method and management system
Through the remote management method of POS machine data, the fault prediction model is used to identify potential faults and dynamically adjust resource allocation, which solves the problem of unreasonable resource allocation in the traditional method, realizes efficient fault prevention and resource optimization, and improves system stability and operation efficiency.
Patent Information
- Application Number
- CN202411225442.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-03
- Publication Date
- 2024-12-17
- Estimated Expiration
- 2044-09-03
AI Technical Summary
Traditional POS machine data management methods are difficult to identify potential failures in advance, which increases the possibility of service interruption, and unreasonable resource allocation, resulting in inefficient resource utilization.
Using the remote management method of POS machine data, the operating data of POS machine is collected and processed, and the fault prediction model is used to identify potential faults, dynamically adjust resource allocation priority and reporting cycle, ensuring that high-risk equipment is preferred to obtain resource support, and abnormal data detection is carried out. with warning.
Effectively prevent service interruptions, improve system stability, optimize resource allocation, improve operational efficiency, reduce maintenance costs, and achieve smart utilization of resources.
Smart Images
Figure CN118735229B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of POS machine data management, and in particular to a POS machine data remote management method and management system. Background Art
[0002] In the current business environment, POS (Point of Sale) machines are indispensable payment tools in the retail, catering and other industries. Their stable operation is crucial to ensure smooth transactions, improve customer satisfaction and protect corporate revenue. However, with the increasing number of POS machines and the increasing complexity of their functions, how to effectively remotely manage these POS machines and ensure their continuous and efficient operation has become a technical problem that needs to be solved.
[0003] Traditional POS data management methods usually rely on regular data collection, analysis and manual intervention, which is not only inefficient, but also difficult to accurately predict and prevent potential failures. At the same time, since the allocation of resources (such as bandwidth, storage and computing power) is often based on fixed rules or human judgment, high-risk POS machines may not receive the necessary support in a timely manner, while low-risk POS machines may waste a lot of resources. It is difficult to intelligently allocate resources such as bandwidth, storage and computing power according to the actual risk situation of the POS machine, resulting in inefficient resource utilization.
[0004] Therefore, it is necessary to provide a POS machine data remote management method and management system to solve the above technical problems. Summary of the invention
[0005] In order to solve the above technical problems, the present invention provides a POS machine data remote management method and management system for solving the problem that traditional POS machine data management methods are difficult to identify potential failures in advance, resulting in an increased possibility of service interruption and unreasonable resource allocation.
[0006] The present invention provides a POS machine data remote management method, the management method comprising the following steps:
[0007] S1. Collecting the operation data of each POS terminal and processing the operation data to obtain the processed operation data;
[0008] S2, inputting the processed operation data into a preset fault prediction model to obtain the prediction results of each POS machine;
[0009] S3. Allocate a priority of reporting resources to each POS terminal according to the prediction result of each POS terminal, wherein the reporting resources specifically include bandwidth, storage space and computing power;
[0010] S4. Based on the prediction results, the risk level of each POS terminal is divided, and the reporting cycle of each POS terminal is dynamically adjusted according to the risk level;
[0011] S5. Allocate reporting resources based on the priority of each POS machine and report POS machine data according to the reporting cycle of each POS machine;
[0012] S6. Check the data reported by each POS machine and issue a warning when abnormal data is detected.
[0013] Preferably, the step S1 specifically includes the following steps:
[0014] S1.1. Collect operation data from each remote POS terminal, including transaction data, hardware status data and network status data;
[0015] S1.2. Clean and format the collected operation data to obtain processed operation data.
[0016] Preferably, in step S2, the process of constructing the preset fault prediction model specifically includes the following steps:
[0017] Collect historical operation data of POS machines, including transaction data, hardware status data, and network status data of POS machines;
[0018] Identify the variable features related to faults in the historical operation data of the POS machine, and identify the mapping relationship between the variable features and the faults;
[0019] A deep learning architecture is selected, and the mapping relationship between the identification variable features and the fault is used as the training set to train and build a fault prediction model.
[0020] Preferably, the step S3 specifically includes the following steps:
[0021] S3.1. Define the indicators of POS machine prediction results, including failure probability, failure type and data sensitivity;
[0022] S3.2, setting corresponding scoring sections for the indicators of the POS machine prediction results, and performing priority scoring on each POS machine by judging the scoring section to which the indicators of the prediction results of each POS machine belong;
[0023] S3.3. Set the resource allocation section corresponding to the priority score, and allocate corresponding reporting resources to each POS machine according to the priority score of each POS machine.
[0024] Preferably, the step S4 specifically includes the following steps:
[0025] S4.1. Quantify the prediction results of each POS terminal output by the fault prediction model to obtain a fault probability value;
[0026] S4.2. Setting risk segments based on the failure probability value, wherein the risk segments specifically include low risk segments, medium risk segments and high risk segments, and determining the risk segment to which the failure probability value of each POS machine prediction result belongs, and dividing the risk level of each POS machine;
[0027] S4.3. Set corresponding reporting cycles based on the risk levels of each POS machine, and dynamically adjust the reporting cycle of each POS machine.
[0028] Preferably, the step S5 specifically includes the following steps:
[0029] S5.1. Determine the reporting resources allocated to each POS terminal, including bandwidth, storage space and computing power;
[0030] S5.2. According to the reporting cycle of each POS terminal, the reporting resources allocated to each POS terminal are mobilized to upload data;
[0031] S5.3. After the data is uploaded, it is confirmed and the data is identified to determine its integrity.
[0032] If the data is judged to be complete, the transmission is stopped; if the data is judged to be lost, the data of the POS machine is retransmitted.
[0033] Preferably, the step S6 specifically includes the following steps:
[0034] S6.1. Check the data reported by each POS machine to identify and determine whether there is any abnormal data in the data reported by each POS machine.
[0035] If it is determined that there is abnormal data in the data reported by each POS machine, a warning notification is issued to the corresponding POS machine. If it is determined that there is no abnormal data in the data reported by each POS machine, no warning is issued;
[0036] S6.2. Based on sending a warning notification to the corresponding POS machine, take processing measures, including contacting POS machine maintenance personnel and strengthening monitoring.
[0037] A POS machine data remote management system, the management system comprising:
[0038] The collection and processing module is used to collect the operation data of each POS machine, and process the operation data to obtain the processed operation data;
[0039] The fault prediction analysis module is used to input the processed operation data into the preset fault prediction model to obtain the prediction results of each POS machine;
[0040] An allocation and division module is used to allocate the priority of reporting resources to each POS terminal according to the prediction results of each POS terminal, wherein the reporting resources specifically include bandwidth, storage space and computing power;
[0041] A dynamic adjustment module is used to classify the risk level of each POS terminal based on the prediction results, and dynamically adjust the reporting cycle of each POS terminal according to the risk level;
[0042] The data reporting and transmission module is used to allocate reporting resources based on the priority of each POS machine and report POS machine data according to the reporting cycle of each POS machine;
[0043] The inspection and warning module is used to inspect the data reported by each POS machine and issue an alarm after detecting abnormal data.
[0044] Compared with the related art, the POS machine data remote management method and management system provided by the present invention has the following beneficial effects:
[0045] The present invention utilizes a fault prediction model to identify potential faults in advance, effectively prevent service interruptions, and enhance system stability. Secondly, by intelligently allocating key resources such as bandwidth, storage, and computing power, it ensures that high-risk devices are supported first, greatly improving system efficiency. In addition, the reporting cycle is dynamically adjusted according to the risk level, which not only strengthens the monitoring of high-risk devices, but also avoids the waste of resources of low-risk devices, thereby achieving smart use of resources. This method integrates fault prediction, resource optimization allocation, dynamic reporting cycle adjustment, and abnormal data detection functions, effectively improving the operating efficiency and stability of the system and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] Figure 1 A flowchart of a method for remote management of POS machine data according to the present invention;
[0047] Figure 2 The present invention is a system block diagram of a POS machine data remote management system. DETAILED DESCRIPTION
[0048] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.
[0049] Embodiment 1
[0050] Reference Figure 1 This embodiment provides a method for remote management of POS machine data, comprising the following steps:
[0051] S1. Collect the operation data of each POS terminal, process the operation data, and obtain the processed operation data.
[0052] Specifically, the operation data collected from each remote POS machine, including transaction data, hardware status data and network status data, is collected through the built-in or external sensors of the POS machine, and the collected operation data is preprocessed to make it easier to analyze and understand, which can reduce noise and bias and improve the accuracy of the prediction model.
[0053] In the specific implementation process, step S1 specifically includes the following steps:
[0054] S1.1. Collect operation data from each remote POS machine, including transaction data, hardware status data and network status data.
[0055] Specifically, a connection is established with POS machines distributed in various locations through a secure and reliable network connection, such as VPN, dedicated line or cloud network, and a data collection request is sent to each POS machine to collect operating data such as transaction data, hardware status data and network status data from each remote POS machine.
[0056] S1.2. Clean and format the collected operation data to obtain processed operation data.
[0057] Specifically, the erroneous data, redundant data, missing data and incomplete data in the collected operation data are cleaned, corrected or filled, and the cleaned operation data is formatted. All data fields are uniformly named, typed and lengthed to facilitate subsequent analysis and processing, and the timestamps are converted to a unified time zone or format to avoid misunderstandings caused by time zone differences, and finally the processed operation data is obtained.
[0058] S2. Input the processed operation data into a preset fault prediction model to obtain the prediction results of each POS machine.
[0059] Specifically, the processed operating data is used as input and imported into a pre-trained fault prediction model. The prediction model can be used to identify potential problems in advance and achieve preventive maintenance.
[0060] In the specific implementation process, in step S2, the construction process of the preset fault prediction model specifically includes the following steps:
[0061] Collect historical operation data of POS machines, including transaction data, hardware status data and network status data of POS machines.
[0062] Identify the variable features related to faults in the historical operation data of the POS machine, and identify the mapping relationship between the variable features and the faults.
[0063] A deep learning architecture is selected, and the mapping relationship between the identification variable features and the fault is used as the training set to train and build a fault prediction model.
[0064] S3. Allocate a priority of reporting resources to each POS terminal according to the prediction result of each POS terminal, wherein the reported resources specifically include bandwidth, storage space and computing power.
[0065] Specifically, each POS machine is given a priority score based on the prediction results of each POS machine output by the fault prediction model, and corresponding reporting resources are allocated to each POS machine based on the priority score of each POS machine, ensuring that each key POS machine can reasonably obtain resource support according to the situation, which is conducive to achieving optimal resource allocation and risk management.
[0066] In the specific implementation process, step S3 specifically includes the following steps:
[0067] S3.1. Define the indicators of POS machine prediction results, including failure probability, failure type and data sensitivity.
[0068] Specifically, the evaluation indicators of the prediction results are clearly defined for subsequent resource allocation priority judgment, including: the possibility of POS machine failure in the future calculated by the fault prediction model; the specific fault type predicted, different fault types may have different degrees of impact on the business; the importance of the data type processed by the POS machine to the business.
[0069] For example, the failure probability can be a value between 0 and 1, indicating the possibility of a failure; the failure type can be a hardware failure, software failure, network failure, etc.; data sensitivity can be divided into three levels: low, medium, and high according to the transaction amount.
[0070] S3.2. Set corresponding scoring sections for the indicators of the prediction results of the POS machines, and perform priority scoring on each POS machine by judging the scoring section to which the indicators of the prediction results of each POS machine determine its belonging.
[0071] Specifically, different scoring sections are set for the indicators of the POS machine prediction results. In this embodiment, the failure probability is divided into three scoring sections: low, medium, and high according to its numerical value; the failure type is divided into three scoring sections: serious, general, and slight according to the degree of impact on the business; the data sensitivity directly uses the three levels of low, medium, and high defined previously. According to the prediction results of each POS machine, its scoring section on each indicator is judged and a comprehensive score is performed. In this embodiment, the comprehensive score uses the weighted average method to perform priority scoring on each POS machine to ensure that the impact of each indicator can be reasonably reflected.
[0072] S3.3. Set the resource allocation section corresponding to the priority score, and allocate corresponding reporting resources to each POS machine according to the priority score of each POS machine.
[0073] Specifically, according to the priority score of each POS terminal, it will be classified into the corresponding resource allocation section and the corresponding reporting resources will be allocated to it. Resource allocation should ensure that key POS terminals can obtain sufficient resource support to ensure timely reporting and processing of data.
[0074] Exemplarily, three resource allocation sections are set: low, medium, and high; POS machines with high priority scores will be placed in the high resource allocation section and obtain higher bandwidth, storage, and computing power; POS machines with medium priority scores will obtain medium-level resources; and POS machines with low priority scores will obtain fewer resources.
[0075] S4. Based on the prediction results, the risk level of each POS terminal is divided, and the reporting cycle of each POS terminal is dynamically adjusted according to the risk level.
[0076] Specifically, according to the prediction results of the fault prediction model, each POS machine is divided into different risk levels. According to the risk level, the data reporting cycle of each POS machine is dynamically adjusted. The dynamic adjustment of the reporting cycle can flexibly adjust the data reporting frequency according to the actual situation, so as to realize the rational use of resources and the timely discovery of problems.
[0077] For example, for POS machines with high risk levels, the reporting cycle is adjusted to a short cycle so that potential problems can be discovered and handled in a timely manner; for POS machines with low risk levels, the reporting cycle is adjusted to a long cycle to reduce resource waste.
[0078] In the specific implementation process, step S4 specifically includes the following steps:
[0079] S4.1. Quantify the prediction results of each POS machine output by the fault prediction model to obtain a fault probability value.
[0080] Specifically, the prediction results output by the fault prediction model are quantified. For example, the fault prediction model outputs a fault probability value between 0 and 1, where 0 indicates no fault and 1 indicates a complete certainty of a fault. For a certain POS machine, the model may output a fault probability value of 0.6, indicating that the POS machine has a 60% chance of failing in the future.
[0081] S4.2. Risk segments are set based on the failure probability value, wherein the risk segments specifically include low-risk segments, medium-risk segments and high-risk segments, and the risk segment to which the failure probability value of the prediction result of each POS machine belongs is determined, and the risk level of each POS machine is divided.
[0082] Specifically, the failure probability value range of each risk section is determined according to actual needs. In this embodiment, the failure probability value range is set from 0 to 0.3 as a low-risk section, 0.3 to 0.7 as a medium-risk section, and 0.7 to 1 as a high-risk section; then, according to the failure probability value of each POS machine, the risk section to which it belongs is determined, and the corresponding risk level is divided.
[0083] S4.3. Set corresponding reporting cycles based on the risk levels of each POS machine, and dynamically adjust the reporting cycle of each POS machine.
[0084] Specifically, different reporting cycles are set according to the risk level. In this embodiment, the higher the risk level of a POS machine, the shorter its reporting cycle should be, so as to timely discover and handle potential problems, and dynamically adjust its reporting cycle according to the real-time operation of the POS machine and the update of the prediction results. For example, if the failure probability value of a POS machine suddenly increases or decreases to another risk segment, its reporting cycle needs to be adjusted accordingly.
[0085] S5. Allocate reporting resources based on the priority of each POS machine and report POS machine data according to the reporting cycle of each POS machine.
[0086] Specifically, according to the allocated reporting resource priority and the determined reporting cycle, it is convenient to formulate a data reporting plan. According to the plan, data is reported to each POS machine in turn to ensure that key data and important data can be reported and processed first, thereby improving the efficiency and accuracy of data reporting.
[0087] In the specific implementation process, step S5 specifically includes the following steps:
[0088] S5.1. Determine the reporting resources allocated to each POS terminal, including bandwidth, storage space and computing power.
[0089] S5.2. According to the reporting cycle of each POS machine, the reporting resources allocated to each POS machine are mobilized to upload data.
[0090] Specifically, according to the set reporting cycle, the upload task is triggered at the beginning of each cycle. At the same time, according to the resources allocated to each POS machine, the network bandwidth, storage space and computing power are scheduled. Before uploading, the POS machine will perform pre-processing operations such as compression and encryption on the data to reduce transmission time and improve data security, and finally upload the data.
[0091] S5.3. After the data is uploaded, it is confirmed and the data is identified to determine its integrity. If the data is determined to be complete, the transmission is stopped. If the data is determined to be lost, the data of the POS machine is retransmitted.
[0092] Specifically, a verification algorithm, specifically a CRC verification method, is used to perform integrity verification on the received data. By comparing the verification values before and after the data is transmitted, it is determined whether the data is damaged or lost during the transmission process. If the data is determined to be complete, the transmission is stopped; if the data is determined to be lost, the data of the POS machine is retransmitted.
[0093] S6. Check the data reported by each POS machine and issue a warning when abnormal data is detected.
[0094] Specifically, the reported data is checked for completeness, accuracy, and consistency. If abnormal data is detected, including missing or incorrect data, a warning will be issued. Data verification is an important part of ensuring data quality and reliability, and can avoid misjudgments and wrong decisions caused by data problems.
[0095] In the specific implementation process, step S6 specifically includes the following steps:
[0096] S6.1. Check the data reported by each POS machine to identify and determine whether there is abnormal data in the data reported by each POS machine. If it is determined that there is abnormal data in the data reported by each POS machine, a warning notification is issued to the corresponding POS machine. If it is determined that there is no abnormal data in the data reported by each POS machine, no warning is issued.
[0097] S6.2. Based on sending a warning notification to the corresponding POS machine, take processing measures, including contacting POS machine maintenance personnel and strengthening monitoring.
[0098] Specifically, based on sending a warning notification to the corresponding POS machine, the POS machine maintenance personnel are notified through the preset contact methods, including phone, email, SMS, etc., and informed of the specific POS machine number, abnormal data type and possible cause of the failure. After receiving the notification, the maintenance personnel should go to the site in time to troubleshoot and repair the failure, and strengthen monitoring.
[0099] Embodiment 2
[0100] Reference Figure 2 As shown, a POS machine data remote management system, the management system includes:
[0101] The collection and processing module is used to collect the operation data of each POS machine, and process the operation data to obtain the processed operation data.
[0102] Specifically, the collection and processing module is used to collect operation data from each remote POS machine through built-in or external sensors of the POS machine, including transaction data, hardware status data and network status data, and pre-process the collected operation data to make it easier to analyze and understand, which can reduce noise and bias and improve the accuracy of the prediction model.
[0103] The fault prediction analysis module is used to input the processed operating data into a preset fault prediction model to obtain the prediction results of each POS machine.
[0104] Specifically, the fault prediction analysis module is used to take the processed operating data as input and import the pre-trained fault prediction model. The prediction model can identify potential problems in advance and achieve preventive maintenance.
[0105] The allocation and division module is used to allocate the priority of reporting resources to each POS machine according to the prediction results of each POS machine, wherein the reporting resources specifically include bandwidth, storage space and computing power.
[0106] Specifically, the allocation and division module is used to perform priority scoring on each POS machine according to the prediction results of each POS machine output by the fault prediction model, and to allocate corresponding reporting resources to each POS machine according to the priority score of each POS machine, so as to ensure that each key POS machine can reasonably obtain resource support according to the situation, which is conducive to the optimal allocation of resources and risk management.
[0107] The dynamic adjustment module is used to classify the risk level of each POS terminal based on the prediction results, and dynamically adjust the reporting period of each POS terminal according to the risk level.
[0108] Specifically, the dynamic adjustment module is used to divide each POS machine into different risk levels according to the prediction results of the fault prediction model, and dynamically adjust the data reporting cycle of each POS machine according to the risk level. The dynamic adjustment of the reporting cycle can flexibly adjust the data reporting frequency according to actual conditions, thereby realizing the rational use of resources and timely discovery of problems.
[0109] For example, for POS machines with high risk levels, the reporting cycle is adjusted to a short cycle so that potential problems can be discovered and handled in a timely manner; for POS machines with low risk levels, the reporting cycle is adjusted to a long cycle to reduce resource waste.
[0110] The data reporting and transmission module is used to allocate reporting resources based on the priority of each POS machine and report POS machine data according to the reporting cycle of each POS machine.
[0111] Specifically, the data reporting and transmission module is used to facilitate the formulation of a data reporting plan based on the allocated reporting resource priority and the determined reporting cycle. According to the plan, data is reported to each POS machine in turn to ensure that key data and important data can be reported and processed first, thereby improving the efficiency and accuracy of data reporting.
[0112] The inspection and warning module is used to inspect the data reported by each POS machine and issue an alarm after detecting abnormal data.
[0113] Specifically, the inspection and warning module is used to verify the integrity, accuracy, and consistency of the reported data. If abnormal data is detected, including missing or incorrect data, a warning will be issued. Data inspection is an important part of ensuring data quality and reliability, and can avoid misjudgments and wrong decisions caused by data problems.
[0114] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0115] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.
[0116] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
Claims
1. A method for remote management of POS machine data, characterized in that: The management method comprises the following steps: S1. Collecting the operation data of each POS terminal and processing the operation data to obtain the processed operation data; S2, inputting the processed operation data into a preset fault prediction model to obtain the prediction results of each POS machine; In step S2, the process of constructing the preset fault prediction model specifically includes the following steps: Collect historical operation data of POS machines, including transaction data, hardware status data, and network status data of POS machines; Identify the variable features related to faults in the historical operation data of the POS machine, and identify the mapping relationship between the variable features and the faults; Select a deep learning architecture and use the mapping relationship between the identification variable features and the fault as the training set to build a fault prediction model; S3. Allocate a priority of reporting resources to each POS terminal according to the prediction result of each POS terminal, wherein the reporting resources specifically include bandwidth, storage space and computing power; The step S3 specifically comprises the following steps: S3.
1. Define the indicators of POS machine prediction results, including failure probability, failure type and data sensitivity; S3.2, setting corresponding scoring sections for the indicators of the POS machine prediction results, and performing priority scoring on each POS machine by judging the scoring section to which the indicators of the prediction results of each POS machine belong; S3.3, setting the resource allocation section corresponding to the priority score, and allocating the corresponding reporting resources to each POS terminal according to the priority score of each POS terminal; S4. Based on the prediction results, the risk level of each POS terminal is divided, and the reporting cycle of each POS terminal is dynamically adjusted according to the risk level; The step S4 specifically comprises the following steps: S4.
1. Quantify the prediction results of each POS terminal output by the fault prediction model to obtain a fault probability value; S4.
2. Setting risk segments based on the failure probability value, wherein the risk segments specifically include low risk segments, medium risk segments and high risk segments, and determining the risk segment to which the failure probability value of each POS machine prediction result belongs, and dividing the risk level of each POS machine; S4.
3. Set corresponding reporting cycles based on the risk levels of each POS terminal and dynamically adjust the reporting cycle of each POS terminal; S5. Allocate reporting resources based on the priority of each POS machine and report POS machine data according to the reporting cycle of each POS machine; S6. Check the data reported by each POS machine and issue a warning when abnormal data is detected.
2. A POS machine data remote management method according to claim 1, characterized in that: The step S1 specifically includes the following steps: S1.
1. Collect operation data from each remote POS terminal, including transaction data, hardware status data and network status data; S1.
2. Clean and format the collected operation data to obtain processed operation data.
3. A POS machine data remote management method according to claim 1, characterized in that: The step S5 specifically comprises the following steps: S5.
1. Determine the reporting resources allocated to each POS terminal, including bandwidth, storage space and computing power; S5.
2. According to the reporting cycle of each POS terminal, the reporting resources allocated to each POS terminal are mobilized to upload data; S5.
3. After the data is uploaded, it is confirmed and the data is identified to determine its integrity. If the data is judged to be complete, the transmission is stopped; if the data is judged to be lost, the data of the POS machine is retransmitted.
4. A POS machine data remote management method according to claim 1, characterized in that: The step S6 specifically comprises the following steps: S6.
1. Check the data reported by each POS machine to identify and determine whether there is any abnormal data in the data reported by each POS machine. If it is determined that there is abnormal data in the data reported by each POS machine, a warning notification is issued to the corresponding POS machine. If it is determined that there is no abnormal data in the data reported by each POS machine, no warning is issued; S6.
2. Based on sending a warning notification to the corresponding POS machine, take processing measures, including contacting POS machine maintenance personnel and strengthening monitoring.
5. A POS machine data remote management system, applied to a POS machine data remote management method as claimed in any one of claims 1 to 4, characterized in that: The management system comprises: The collection and processing module is used to collect the operation data of each POS machine, and process the operation data to obtain the processed operation data; The fault prediction analysis module is used to input the processed operation data into the preset fault prediction model to obtain the prediction results of each POS machine; An allocation and division module is used to allocate the priority of reporting resources to each POS terminal according to the prediction results of each POS terminal, wherein the reporting resources specifically include bandwidth, storage space and computing power; A dynamic adjustment module is used to classify the risk level of each POS terminal based on the prediction results, and dynamically adjust the reporting cycle of each POS terminal according to the risk level; The data reporting and transmission module is used to allocate reporting resources based on the priority of each POS machine and report POS machine data according to the reporting cycle of each POS machine; The inspection and warning module is used to inspect the data reported by each POS machine and issue an alarm after detecting abnormal data.