A cloud computing-based intelligent device management system and method

Abstract

The present application relates to the field of intelligent device management technology, and in particular to an intelligent device management system and method based on cloud computing. The method includes: obtaining a POS machine working status data set, analyzing the POS machine working status data set, and determining timing status evaluation information; obtaining a real-time business transaction data set, and determining a local encryption policy set based on the real-time business transaction data set and timing status evaluation information; analyzing the local encryption policy set based on the real-time business transaction data set, and determining a cloud decryption update policy set; transmitting and updating transaction data based on the local encryption policy set and the cloud decryption update policy set, and determining and outputting a POS machine collaborative processing report. The present application ensures a good balance between data security and data real-time performance, reduces the mutual impact between data security and data real-time performance, and effectively supports the availability of transaction data.

Description

Technical Field

[0001] The present application relates to the field of intelligent device management technology, and in particular to an intelligent device management system and method based on cloud computing. Background Art

[0002] Against the backdrop of the rapid development of the digital economy, the widespread application of cloud computing and smart payment technologies has enabled the functions of POS machines to continue to expand, enabling more powerful data analysis and processing capabilities, and improving merchants' operational efficiency and transaction data security.

[0003] However, in the process of collaborative processing of multiple POS machines, the existing intelligent POS management technology is difficult to ensure a good balance between data security and data real-time, resulting in mutual influence between data security and data real-time, resulting in the inability to effectively support the availability of transaction data. Summary of the invention

[0004] The present application provides a cloud computing-based intelligent device management system and method to solve the above technical problems.

[0005] In a first aspect, the present application provides a smart device management method based on cloud computing, the method comprising: obtaining a POS machine working status data set, analyzing the POS machine working status data set, and determining timing status evaluation information; obtaining a real-time business transaction data set, and determining a local encryption policy set based on the real-time business transaction data set and the timing status evaluation information; analyzing the local encryption policy set based on the real-time business transaction data set, and determining a cloud decryption update policy set; transmitting and updating transaction data based on the local encryption policy set and the cloud decryption update policy set, and determining and outputting a POS machine collaborative processing report.

[0006] Through this solution, the POS machine working status data set is analyzed to determine the timing status evaluation information used to characterize the working status of each POS machine during the timing change process. On this basis, combined with the real-time business transaction data set, a local encryption strategy set is obtained that can achieve a good balance between data security and real-time performance in the current POS machine working state. Then, a cloud decryption update strategy set suitable for different types of transaction data availability requirements is analyzed and obtained. According to the local encryption strategy set and the cloud decryption update strategy set, the transaction data is transmitted and updated, and the POS machine collaborative processing report is determined and output to ensure a good balance between data security and data real-time performance, reduce the mutual impact between data security and data real-time performance, and effectively support the availability of transaction data.

[0007] Optionally, the POS machine working state data set includes a performance indicator set, a network indicator set and a load indicator set, and the analyzing the POS machine working state data set to determine the timing state evaluation information includes:

[0008] Analyze the performance indicator set, the network indicator set and the load indicator set to determine the total number of indicators corresponding to the POS machine working status data set; extract the indicator type corresponding to each indicator according to the performance indicator set, the network indicator set and the load indicator set; determine the minimum indicator boundary value and the maximum indicator boundary value corresponding to each indicator according to the indicator type; based on the performance indicator set, the network indicator set and the load indicator set, determine the real-time status index corresponding to each POS machine according to the minimum indicator boundary value and the maximum indicator boundary value; determine the load change amplitude corresponding to each POS machine according to the load indicator set, and determine the predicted time span for each POS machine according to the load change amplitude; determine the predicted status index of each POS machine according to the predicted time span based on the performance indicator set, the network indicator set and the load indicator set; construct the time series status evaluation information according to the real-time status index and the predicted status index.

[0009] Through this solution, the performance indicator set, network indicator set and load indicator set are comprehensively analyzed to obtain a real-time status index for reflecting the current working status of each POS machine and a predicted status index for reflecting the changing working status of each POS machine within a specified time period in the future. Based on the real-time status index and the predicted status index, time series status evaluation information is constructed to serve as the basic data for subsequent dynamic adjustment of the POS machines. At the same time, through the dual effects of the real-time status index and the predicted status index, data delays caused by adjustment lags can be significantly reduced.

[0010] Optionally, based on the performance indicator set, the network indicator set and the load indicator set, according to the minimum indicator boundary value and the maximum indicator boundary value, the real-time status index corresponding to each POS terminal is determined, specifically as follows:

[0011] ;

[0012] in, is the real-time status index, is the total number of indicators, For the The preset indicator influence coefficient corresponding to each indicator is: For the The indicator value corresponding to the indicator is For the The minimum indicator boundary value corresponding to the indicator, For the The maximum indicator boundary value corresponding to the indicator.

[0013] Through this scheme, mathematical analysis is used to quantify the real-time status index reflecting the current working status of the POS machine based on the performance indicator set, network indicator set and load indicator set according to the minimum indicator boundary value and the maximum indicator boundary value, so as to achieve a scientific evaluation of the real-time working status of the POS machine and improve the accuracy of subsequent adjustments to the POS machine.

[0014] Optionally, the prediction state index of each POS terminal is determined based on the performance indicator set, the network indicator set and the load indicator set according to the prediction time span, specifically as the following formula:

[0015] ;

[0016] in, is the predicted state index, To predict the time span, For the current time point The next The indicator value corresponding to the indicator is is the intercept term, For the The regression coefficient corresponding to the indicator is is the regression random error term.

[0017] Through this solution, mathematical analysis methods are used to quantify the predicted state index of each POS machine based on the performance indicator set, network indicator set and load indicator set according to the predicted time span, so as to reflect the workload performance of each POS machine within the future predicted time span, improve the real-time performance of the subsequent dynamic adjustment of the POS machine according to the working state of the POS machine, and reduce the data delay caused by adjustment lag.

[0018] Optionally, the determining a local encryption policy set based on the real-time business transaction data set and according to the time series state evaluation information includes:

[0019] Analyze the real-time business transaction data set, extract the real-time transaction data set corresponding to each POS machine and the total amount of real-time transaction data; determine the real-time occupied resources of each POS machine at different time points according to the time series status evaluation information; determine the business sensitivity level of each POS machine and the data sensitivity index of each transaction data corresponding to it according to the real-time business transaction data set; analyze the real-time transaction data set according to the data sensitivity index, assign a corresponding type of encryption method to each transaction data, generate an encryption method mapping value and an encryption status value corresponding to each transaction data, and thereby determine the total number of data packets; the encryption method includes single data encryption and data packet encryption; analyze the real-time transaction data set, determine the data size of each transaction data, and thereby analyze the corresponding Encryption method, determine the single data encryption time cost corresponding to each transaction data and the data packet encryption time cost corresponding to each transaction data packet; based on the data sensitivity index, according to the encryption method mapping value, the encryption status value, the single data encryption time cost and the data packet encryption time cost, respectively determine the single data encryption total utility, data packet encryption total utility, single data encryption total cost and data packet encryption total cost; based on the real-time occupied resources and the business sensitivity level, according to the single data encryption total utility, the data packet encryption total utility, the single data encryption total cost and the data packet encryption total cost, maximize the total encryption utility; according to the encryption method mapping value of each transaction data corresponding to the maximized total encryption utility, construct the local encryption strategy set.

[0020] Through this solution, based on the characteristics of different transaction data in the real-time business transaction data set and combined with the working status of each POS machine, the encryption methods of different transaction data are targetedly allocated through the cooperation of single data encryption and data packet encryption. With the goal of maximizing the total encryption utility, a local encryption strategy set is constructed according to the encryption method mapping value of each transaction data corresponding to the maximized total encryption utility, so as to achieve a good balance between data security and data real-time performance.

[0021] Optionally, based on the real-time occupied resources and the service sensitivity level, according to the total utility of single data encryption, the total utility of data packet encryption, the total cost of single data encryption and the total cost of data packet encryption, the total utility of encryption is maximized, specifically as follows:

[0022] ;

[0023] in, is the total encrypted utility, The total utility of the single data encryption, encrypting a total utility for said data packet, is the total cost of encrypting the single data, the total encryption cost for the data packet, is the preset balance adjustment coefficient, For resource budget, For the real-time occupation of resources, To preset the data sensitivity impact coefficient, is the business sensitivity level.

[0024] Through this solution, mathematical analysis is used to maximize the total encryption utility based on real-time occupied resources and business sensitivity levels, according to the total utility of single data encryption, the total utility of data packet encryption, the total cost of single data encryption and the total cost of data packet encryption, thereby improving the local encryption efficiency of transaction data and ensuring the real-time nature of data.

[0025] Optionally, based on the data sensitivity index, according to the encryption mode mapping value, the encryption state value, the single data encryption time cost and the data packet encryption time cost, the single data encryption total utility, the data packet encryption total utility, the single data encryption total cost and the data packet encryption total cost are determined respectively, specifically as the following formulas:

[0026] ;

[0027] in, For the single data encryption utility, Encryption utility for said data packet, is the total cost of encrypting the single data, the total encryption cost for the data packet, is the total amount of transaction data, is the single data encryption cost, For the The single data encryption utility value of each transaction data, Mapping value for the encryption method, For the The encrypted state value of the transaction data, To preset the data sensitivity impact coefficient, For the The data sensitivity index corresponding to the transaction data, is the total number of packets, For the The packet encryption utility value of the transaction packet, For the The single data encryption time cost required for each transaction data, For the The data packet encryption time cost of each transaction data packet.

[0028] Through this scheme, mathematical analysis methods are used based on the data sensitivity index, according to the encryption method mapping value, encryption status value, the shown single data encryption time cost and data packet encryption time cost, to quantify the total utility of single data encryption, the total utility of data packet encryption, the total cost of single data encryption and the total cost of data packet encryption respectively, to achieve automated batch analysis of massive transaction data, improve the accuracy of utility and cost analysis, and at the same time improve data analysis efficiency.

[0029] Optionally, analyzing the local encryption policy set based on the real-time business transaction data set to determine the cloud decryption update policy set includes:

[0030] Based on the local encryption strategy set, according to the encryption method mapping value corresponding to each transaction data in the local encryption strategy set, analyze the real-time transaction data set, extract the single data encryption time series data set and the data packet encryption time series data set; analyze the single data encryption time series data set, and build a sensitive data update queue according to the transaction data in the single data encryption time series data set and the corresponding transaction time; analyze the data packet encryption time series data set, and build a high-frequency data cache according to the transaction data packets in the data packet encryption time series data set and the corresponding transaction time; determine the overall write time span according to the data scale corresponding to all transaction data and transaction data packets in the sensitive data update queue and the high-frequency data cache; determine the cloud decryption update timeline based on the overall write time span and the transaction time corresponding to each transaction data and transaction data packet; build the cloud decryption update strategy set according to the sensitive data update queue, the high-frequency data cache and the cloud decryption update timeline.

[0031] Through this solution, based on the local encryption policy set, single data encryption time series data sets and data packet encryption time series data sets with time tags are extracted, and a sensitive data update queue is constructed according to the transaction data in the single data encryption time series data set and its corresponding transaction time. At the same time, a high-frequency data cache is constructed according to the transaction data packets in the data packet encryption time series data set and its corresponding transaction time. Through the cooperation of the sensitive data update queue and the high-frequency data cache, targeted decryption and writing of single data and data packets are realized, and based on the overall writing time span and according to the transaction time corresponding to each transaction data and transaction data packet, a cloud-based decryption update timeline containing the time point for decryption and writing of each transaction data and transaction data packet is constructed. According to the sensitive data update queue, the high-frequency data cache and the cloud-based decryption update timeline, a cloud-based decryption update policy set is constructed to improve the overall data decryption and writing efficiency and ensure the high availability of key data.

[0032] Optionally, the determining of the cloud decryption update timeline based on the overall writing time span and according to the transaction time corresponding to each transaction data and transaction data packet includes:

[0033] Analyze the overall write time span to determine the write span start time point; determine the earliest transaction time point, the latest transaction time point and the representative timestamp corresponding to each transaction data and transaction data packet according to the transaction time corresponding to each transaction data and transaction data packet; based on the overall write time span, determine the write time point corresponding to each transaction data and transaction data packet according to the write span start time point, the earliest transaction time point and the latest transaction time point, specifically the following formula:

[0034] ;

[0035] in, Transaction data / transaction data package The corresponding writing time point, is the start time point of the write span, Transaction data / transaction data package The corresponding representative timestamp, is the earliest transaction time point, is the latest transaction time point, for the overall write time span, Transaction data / transaction data package The corresponding write thread number, is the total number of parallel threads, updating a queue for said sensitive data, Cache the high-frequency data; and construct the cloud decryption update timeline according to the writing time point corresponding to each transaction data and transaction data packet.

[0036] Through this solution, mathematical analysis methods are used to analyze the writing time point corresponding to each transaction data and transaction data packet based on the overall writing time span and the transaction time corresponding to each transaction data and transaction data packet, and the cloud decryption update timeline is constructed to improve the decryption and writing efficiency of the cloud platform for transaction data and improve the availability of transaction data.

[0037] In a second aspect, the present application provides a cloud computing-based intelligent device management system, the system comprising:

[0038] A status assessment module is used to obtain a POS machine working status data set, analyze the POS machine working status data set, and determine timing status assessment information; an encryption policy analysis module is used to obtain a real-time business transaction data set, and determine a local encryption policy set based on the real-time business transaction data set and the timing status assessment information; a decryption policy analysis module is used to analyze the local encryption policy set based on the real-time business transaction data set, and determine a cloud decryption update policy set; a writing module is used to transmit and update transaction data according to the local encryption policy set and the cloud decryption update policy set, and determine and output a POS machine collaborative processing report. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0041] Figure 2 A flowchart of a cloud computing-based smart device management method provided in one embodiment of the present application;

[0042] Figure 3 A schematic diagram of the structure of a cloud computing-based intelligent device management system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0045] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0046] In the process of collaborative processing of multiple POS machines, the existing intelligent POS management technology is difficult to ensure a good balance between data security and data real-time, resulting in mutual influence between data security and data real-time, resulting in the inability to effectively support the availability of transaction data.

[0047] Based on this, the present application provides a cloud computing-based intelligent device management system and method. The POS machine working status data set is analyzed to determine the time sequence state evaluation information used to characterize the working status of each POS machine during the time sequence change process. On this basis, combined with the real-time business transaction data set, a local encryption strategy set that can achieve a good balance between data security and real-time performance under the current POS machine working state is obtained, and then a cloud decryption update strategy set suitable for different types of transaction data availability requirements is analyzed and obtained. According to the local encryption strategy set and the cloud decryption update strategy set, the transaction data is transmitted and updated, and the POS machine collaborative processing report is determined and output to ensure a good balance between data security and data real-time performance, reduce the mutual influence between data security and data real-time performance, and effectively support the availability of transaction data.

[0048] Figure 1 A schematic diagram of an application scenario provided by this application. In the process of multiple POS machines working together, the method provided by this application is applied to ensure a good balance between data security and data real-time performance, and reduce the mutual impact between data security and data real-time performance.

[0049] Specifically, the method of the present application is applied to any server, which communicates with a POS machine, obtains and analyzes a POS machine working status data set provided by the POS machine through the server, determines timing status evaluation information used to characterize the working status of each POS machine during the timing change process, and on this basis, combined with the real-time business transaction data set provided by the POS machine, obtains a local encryption strategy set that can achieve a good balance between data security and real-time performance under the current POS machine working state, and then analyzes and obtains a cloud decryption update strategy set suitable for different types of transaction data availability requirements. According to the local encryption strategy set and the cloud decryption update strategy set, the transaction data is transmitted and updated, and the corresponding POS machine collaborative processing report is provided to the user, so as to ensure a good balance between data security and data real-time performance, reduce the mutual influence between data security and data real-time performance, and effectively support the availability of transaction data.

[0050] For specific implementation methods, please refer to the following embodiments.

[0051] Figure 2 This is a flow chart of a cloud computing-based intelligent device management system and method provided in one embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes:

[0052] S201, obtaining a POS machine working status data set, analyzing the POS machine working status data set, and determining time series status evaluation information.

[0053] The POS machine working status data set may be a parameter set used to characterize the current workload status of each POS machine. The POS machine working status data set may be obtained through a log recording module built into the POS machine.

[0054] The time series status evaluation information may be indicator information obtained based on the quantification of the corresponding working status of the POS machine and used to characterize whether the current working status of the POS machine is good and whether the future workload change is optimistic.

[0055] Specifically, in the collaborative working process of multiple POS machines, the workload status of different POS machines directly reflects the data processing pressure of the POS machines, which in turn affects the real-time performance of the data transmission process of the POS machines. Through mathematical analysis, the POS machine working status data set is analyzed, and the indicator information used to characterize whether the current working status of the POS machine is good and whether the future workload changes are optimistic is quantified, which can be used as the targeted data basis for subsequent adjustments to the POS machine working strategy.

[0056] S202: Acquire a real-time business transaction data set, and determine a local encryption policy set based on the real-time business transaction data set and time series state evaluation information.

[0057] The real-time business transaction data set may be a set of real-time business data that each POS machine needs to process, and the real-time business transaction data set may be obtained by each POS machine. The local encryption policy set may be a policy set for each POS machine to locally encrypt business data before data transmission.

[0058] Specifically, when the workload of the POS machine is at a low level, since the amount of data that the POS machine needs to deal with is small, the difficulty of ensuring data real-time is low. The transaction data can be encrypted locally one by one and then transmitted to the cloud to ensure data security. However, when the workload of the POS machine is at a high level, the difficulty of ensuring data real-time is high. If the method of encrypting transaction data locally one by one is still adopted, the real-time nature of the data is easily damaged. Therefore, according to the time series state evaluation information reflecting the workload state of the POS machine, the local encryption strategy of each POS machine is adjusted to determine the local encryption strategy set, so that the local encryption strategy of the POS machine can achieve a good balance between data security and real-time under the current working state of the POS machine.

[0059] S203. Analyze the local encryption policy set based on the real-time business transaction data set, and determine the cloud decryption update policy set.

[0060] The cloud decryption and update policy set may be a set of policies for the cloud platform to decrypt and update the business transaction data provided by the POS terminal.

[0061] Specifically, after each POS machine encrypts and transmits the business transaction data according to the corresponding local encryption policy, the unified cloud platform decrypts and writes the business transaction data to complete the final data persistence. In this process, the cloud platform's decryption and writing policy for business transaction data has a direct impact on the real-time availability of business transaction data. In addition, since the real-time business transaction data set contains transaction data of different types and importance provided by each POS machine, in the case of a large amount of data, if the transaction data in the real-time business transaction data set is directly decrypted and written sequentially, it is difficult to ensure the availability of data with high real-time requirements in the real-time business transaction data set. Therefore, through a comprehensive analysis of the real-time business transaction data set and the local encryption policy set, a cloud decryption update policy set suitable for current transaction data needs is determined.

[0062] S204. According to the local encryption policy set and the cloud decryption update policy set, the transaction data is transmitted and updated, and a POS machine collaborative processing report is determined and output.

[0063] The POS machine collaborative processing report may be report information that evaluates and summarizes the collaborative working process of multiple POS machines.

[0064] Specifically, based on the local encryption policy set and the cloud decryption update policy set, the POS machine and the cloud platform are controlled to encrypt, transmit and dynamically update the business transaction data according to the local encryption policy set and the cloud decryption update policy set respectively, and the log information corresponding to the POS machine and the cloud platform is integrated to build a POS machine collaborative processing report through data visualization technology. The POS machine collaborative processing report is provided to users through human-computer interaction devices, such as high-definition display screens, so that users can understand the collaborative working effect of different POS machines.

[0065] Through this solution, the POS machine working status data set is analyzed to determine the timing status evaluation information used to characterize the working status of each POS machine during the timing change process. On this basis, combined with the real-time business transaction data set, a local encryption strategy set is obtained that can achieve a good balance between data security and real-time performance in the current POS machine working state. Then, a cloud decryption update strategy set suitable for different types of transaction data availability requirements is analyzed and obtained. According to the local encryption strategy set and the cloud decryption update strategy set, the transaction data is transmitted and updated, and the POS machine collaborative processing report is determined and output to ensure a good balance between data security and data real-time performance, reduce the mutual impact between data security and data real-time performance, and effectively support the availability of transaction data.

[0066] In some embodiments, the performance indicator set, network indicator set and load indicator set are analyzed to determine the total number of indicators corresponding to the POS machine working status data set; the indicator type corresponding to each indicator is extracted according to the performance indicator set, network indicator set and load indicator set; the minimum indicator boundary value and the maximum indicator boundary value corresponding to each indicator are determined according to the indicator type; based on the performance indicator set, network indicator set and load indicator set, the real-time status index corresponding to each POS machine is determined according to the minimum indicator boundary value and the maximum indicator boundary value; based on the load indicator set, the load change amplitude corresponding to each POS machine is determined, and based on the load change amplitude, the predicted time span for each POS machine is determined; based on the performance indicator set, network indicator set and load indicator set, the predicted status index of each POS machine is determined according to the predicted time span; and based on the real-time status index and the predicted status index, the time series status evaluation information is constructed.

[0067] The POS machine working status data set includes a performance indicator set, a network indicator set and a load indicator set.

[0068] The performance indicator set may be an indicator set that reflects the changes in the computing performance of the POS machine during operation, such as processor occupancy, etc. The network indicator set may be an indicator set that reflects the changes in the network status of the POS machine during operation, such as network delay, packet loss rate, etc. The load indicator set may be an indicator set that reflects the changes in the workload of the POS machine during operation, such as system occupancy, response delay, etc.

[0069] The total number of indicators can be the total number of indicator parameters in the performance indicator set, network indicator set, and load indicator set.

[0070] The indicator type may be the specific type of data corresponding to the indicator parameters in the performance indicator set, network indicator set, and load indicator set.

[0071] The minimum indicator boundary value may be the minimum boundary value in the measurement scale corresponding to the current indicator.

[0072] The maximum indicator boundary value may be the maximum boundary value in the measurement scale corresponding to the current indicator.

[0073] The real-time status index may be a quantitative index used to characterize the working status load condition of the POS machine at the current time node.

[0074] The load variation range may be the load variation range of the POS machine within a specified analysis time period.

[0075] The prediction time span may be the time span corresponding to the prediction of the future load state of the POS machine.

[0076] The predicted state index may be a quantitative index used to characterize the working state load condition of the POS terminal within the future predicted time span.

[0077] Specifically, the working status of the POS machine is mainly evaluated from three aspects: performance, network and load. The performance indicator set reflects the hardware performance usage during the working process of the POS machine. If the hardware occupancy is large when the data volume is not large, it means that the current POS machine has a hardware performance bottleneck; the network indicator set reflects the network connectivity performance between the POS machine and the cloud platform. If the network connectivity performance is poor, it will significantly affect the data transmission delay and data integrity; the load indicator set directly reflects the current workload faced by the POS machine and affects the data throughput demand for the current POS machine; through the comprehensive analysis of the performance indicator set, the network indicator set and the load indicator set, the real-time status index used to reflect the current workload of each POS machine is quantified by using mathematical analysis methods, and on this basis, according to the load change amplitude corresponding to each POS machine, the real-time status index for each POS machine is determined. For the prediction time span of the OS machine, the larger the load change amplitude, the greater the workload fluctuation faced by the POS machine. The corresponding prediction time span should be reduced to accurately capture the frequently changing workload. On the contrary, the smaller the load change amplitude, the corresponding prediction time span should be increased to reduce the adjustment frequency and improve data stability. Through further mathematical analysis, based on the performance indicator set, network indicator set and load indicator set, the prediction state index of each POS machine is determined according to the prediction time span. The prediction state index can significantly improve the real-time performance of a series of subsequent adjustments made to the POS machine and reduce the data delay caused by adjustment lag. According to the real-time state index and the prediction state index, the time series state evaluation information is constructed so that the time series state evaluation information can reflect the current working state of each POS machine while characterizing its working change state within a specified time period in the future.

[0078] Through this solution, the performance indicator set, network indicator set and load indicator set are comprehensively analyzed to obtain a real-time status index for reflecting the current working status of each POS machine and a predicted status index for reflecting the changing working status of each POS machine within a specified time period in the future. Based on the real-time status index and the predicted status index, time series status evaluation information is constructed to serve as the basic data for subsequent dynamic adjustment of the POS machines. At the same time, through the dual effects of the real-time status index and the predicted status index, data delays caused by adjustment lags can be significantly reduced.

[0079] In some embodiments, based on the performance indicator set, the network indicator set and the load indicator set, according to the minimum indicator boundary value and the maximum indicator boundary value, the real-time status index corresponding to each POS terminal is determined, specifically, as follows:

[0080] (1)

[0081] in, is the real-time status index, is the total number of indicators, For the The preset indicator influence coefficient corresponding to each indicator is: For the The indicator value corresponding to the indicator is For the The minimum indicator boundary value corresponding to the indicator, For the The maximum indicator boundary value corresponding to the indicator.

[0082] The preset indicator influence coefficient may be a quantitative value used to characterize the degree of influence of the current type of indicator on the real-time status index.

[0083] Specifically, through formula (1) Each indicator value is normalized uniformly to eliminate the impact of different indicators on the dimensions. The values ​​corresponding to different indicators are mapped to the interval [0,1]. Then, the normalized indicator values ​​are transformed uniformly and nonlinearly through the logarithmic function in formula (1) to reflect the nonlinear impact of different indicators on the real-time status index. Then, the weighted impact of different indicators on the real-time status index is quantified by introducing the preset indicator impact coefficient, and finally the real-time status index is obtained.

[0084] Through this scheme, mathematical analysis is used to quantify the real-time status index reflecting the current working status of the POS machine based on the performance indicator set, network indicator set and load indicator set according to the minimum indicator boundary value and the maximum indicator boundary value, so as to achieve a scientific evaluation of the real-time working status of the POS machine and improve the accuracy of subsequent adjustments to the POS machine.

[0085] In some embodiments, based on the performance indicator set, the network indicator set and the load indicator set, the predicted state index of each POS terminal is determined according to the predicted time span, specifically, the following formula (2):

[0086] (2)

[0087] in, is the predicted state index, To predict the time span, For the current time point The next The indicator value corresponding to the indicator is is the intercept term, For the The regression coefficient corresponding to the indicator is is the regression random error term.

[0088] The intercept term may be a parameter used to characterize a basic prediction value for the future value of an indicator, and the intercept term may be set by summarizing experimental data.

[0089] The regression coefficient can be a numerical value used to characterize the degree of influence of the current value corresponding to the indicator on its future value. The regression coefficient can be obtained by fitting the experimental data.

[0090] The regression random error term can be a parameter term used to characterize the random error that exists in the process of predicting the future value of the indicator. The regression random error term can be obtained by calculating the residual between the actual value and the predicted value in the historical data or experimental data using the least squares method.

[0091] Specifically, through formula (2) Describes the future forecast value of the indicator, through the multiple linear regression model , quantify the future prediction values ​​corresponding to different indicators, and then Clearly predict the direction of the state index to the future workload state of the POS machine. hour, , indicating that the future workload of POS machines will be on the rise. On the contrary, hour, , indicating that the future workload of POS machines will show a downward trend. , it means that the future workload status of the POS machine will remain stable.

[0092] Through this solution, mathematical analysis methods are used to quantify the predicted state index of each POS machine based on the performance indicator set, network indicator set and load indicator set according to the predicted time span, so as to reflect the workload performance of each POS machine within the future predicted time span, improve the real-time performance of the subsequent dynamic adjustment of the POS machine according to the working state of the POS machine, and reduce the data delay caused by adjustment lag.

[0093] In some embodiments, the real-time business transaction data set is analyzed to extract the real-time transaction data set corresponding to each POS machine and the total amount of real-time transaction data; based on the time series status evaluation information, the real-time occupied resources of each POS machine at different time points are determined; based on the real-time business transaction data set, the business sensitivity level of each POS machine and the data sensitivity index of each corresponding transaction data are determined; based on the data sensitivity index, the real-time transaction data set is analyzed, and the corresponding type of encryption method is assigned to each transaction data, and the encryption method mapping value and encryption status value corresponding to each transaction data are generated, and the total number of data packets is determined accordingly; the encryption method includes single data encryption and data packet encryption; the real-time transaction data set is analyzed to determine the data size of each transaction data, and the data size of each transaction data is analyzed accordingly The encryption method corresponding to each transaction data determines the single data encryption time cost corresponding to each transaction data and the data packet encryption time cost corresponding to each transaction data packet; based on the data sensitivity index, according to the encryption method mapping value, encryption status value, the single data encryption time cost and the data packet encryption time cost shown, respectively determine the single data encryption total utility, data packet encryption total utility, single data encryption total cost and data packet encryption total cost; based on the real-time occupied resources and business sensitivity level, according to the single data encryption total utility, data packet encryption total utility, single data encryption total cost and data packet encryption total cost, maximize the total encryption utility; according to the encryption method mapping value of each transaction data corresponding to the maximized total encryption utility, construct a local encryption strategy set.

[0094] The real-time transaction data set may be a collection of transaction data that each POS needs to process at present.

[0095] The total amount of real-time transaction data may be the total amount of transaction data in the real-time transaction data set.

[0096] The real-time occupied resources may be the platform resources occupied by the POS machine in the current state.

[0097] The business sensitivity level may be the data security sensitivity level of the business that the current POS terminal is responsible for.

[0098] The data sensitivity index may be a quantitative index used to characterize the data security sensitivity corresponding to different transaction data.

[0099] The encryption method mapping value may be a numerical value used to characterize the encryption method type corresponding to the current transaction data.

[0100] The encryption status value may be a numerical value used to indicate whether the current transaction data has been encrypted.

[0101] Single data encryption can be a method of encrypting a single transaction data.

[0102] Data packet encryption may be a method of encrypting a data packet consisting of multiple transaction data.

[0103] The total number of data packets may be the total number of data packets that need to be encrypted.

[0104] The data scale may be the amount of data corresponding to the transaction data.

[0105] The single data encryption time cost can be the time cost required to encrypt a single transaction data.

[0106] The data packet encryption time cost may be the time cost required for encrypting the data packet.

[0107] The total utility of single data encryption may be the total utility generated by encrypting a single piece of transaction data.

[0108] The total utility of data packet encryption may be the total utility generated by encrypting the data packet.

[0109] The total cost of single data encryption may be the total resource cost required to encrypt a single piece of transaction data.

[0110] The total data packet encryption cost may be a total resource cost required to encrypt the data packet.

[0111] The total encryption utility can be the total utility generated by encrypting all single transaction data and data packets.

[0112] Specifically, it can be seen from the analysis of the above embodiments that if the POS machine adopts a method of encrypting each transaction data one by one, when the amount of transaction data is too large, a significant data transmission delay will occur, which will make it difficult to ensure the real-time performance of the transaction data. It is necessary to perform targeted screening and batch encryption processing on different types of transaction data to reduce the data transmission delay caused by data encryption. For transaction data with high data security sensitivity requirements, a targeted single data encryption method is adopted. For short-term, large-scale concurrent transaction data without high data security sensitivity requirements, several transaction data of this type can be integrated into a data packet, and then the data packet is uniformly encrypted. The size limit of the data packet can be set according to the data processing performance of the POS machine. Through the cooperation of the two encryption methods of single data encryption and data packet encryption, while ensuring the data security of key transaction data, the number of encryptions is reduced and the encryption overhead is reduced to reduce the delay in transaction data transmission. Since the real-time transaction data set contains a large number of different types of transaction data, the total encryption utility is maximized by mathematical analysis to obtain a local encryption strategy set that uses the cooperation of the two encryption methods of single data encryption and data packet encryption to achieve a good balance between data security and data real-time performance under the current real-time transaction data set.

[0113] Through this solution, based on the characteristics of different transaction data in the real-time business transaction data set and combined with the working status of each POS machine, the encryption methods of different transaction data are targetedly allocated through the cooperation of single data encryption and data packet encryption. With the goal of maximizing the total encryption utility, a local encryption strategy set is constructed according to the encryption method mapping value of each transaction data corresponding to the maximized total encryption utility, so as to achieve a good balance between data security and data real-time performance.

[0114] In some embodiments, based on the real-time occupied resources and the service sensitivity level, the total encryption utility is maximized according to the total encryption utility of single data, the total encryption utility of data packets, the total encryption cost of single data, and the total encryption cost of data packets, specifically, the following formula (3):

[0115] (3)

[0116] in, is the total encryption utility, is the total utility of single data encryption, The total utility of encrypting the data packet, is the total cost of single data encryption, The total encryption cost for the data packet, is the preset balance adjustment coefficient, For resource budget, To occupy resources in real time, To preset the data sensitivity impact coefficient, The business sensitivity level.

[0117] The preset balance adjustment coefficient may be a coefficient used to adjust the proportion of utility and cost impact.

[0118] The preset data sensitivity impact coefficient may be a coefficient used to characterize the degree of impact of data sensitivity on resource budget.

[0119] Specifically, in the process of maximizing the total encryption utility, the total single data encryption utility and the total data packet encryption utility represent the utility contribution of the encryption process to data security and integrity. Maximizing this part can significantly improve the total encryption utility, while the total single data encryption cost and the total data packet encryption cost represent the cost of the encryption process. Minimizing this part can have a positive effect on the total encryption utility. Describe the optimization relationship between utility and cost, and To constrain costs, use Resource budgets are controlled to maximize the total utility of encryption at a reasonable cost.

[0120] Through this solution, mathematical analysis is used to maximize the total encryption utility based on real-time occupied resources and business sensitivity levels, according to the total utility of single data encryption, the total utility of data packet encryption, the total cost of single data encryption and the total cost of data packet encryption, thereby improving the local encryption efficiency of transaction data and ensuring the real-time nature of data.

[0121] In some embodiments, based on the data sensitivity index, according to the encryption mode mapping value, the encryption state value, the single data encryption time cost and the data packet encryption time cost, the single data encryption total utility, the data packet encryption total utility, the single data encryption total cost and the data packet encryption total cost are determined respectively, specifically as follows: Formula (4):

[0122] (4)

[0123] in, is the total utility of single data encryption, The total utility of encrypting the data packet, is the total cost of single data encryption, The total encryption cost for the data packet, is the total amount of transaction data, is the single data encryption cost, For the The single data encryption utility value of each transaction data, Map value for encryption mode, For the The encrypted status value of the transaction data, To preset the data sensitivity impact coefficient, For the The data sensitivity index corresponding to each transaction data, is the total number of packets, For the The packet encryption utility value of the transaction packet, For the The single data encryption time cost required for each transaction data, For the The data packet encryption time cost of each transaction data packet.

[0124] The preset data sensitivity impact coefficient may be a coefficient used to characterize the degree of impact of data sensitivity on encryption utility.

[0125] Specifically, when the encryption status value in formula (4) is 0, it means that the current transaction data has been encrypted. When the encryption status value is 1, it means that the current transaction is not encrypted, so as to exclude the encrypted transaction data and avoid repeated encryption. Through the simultaneous expressions in formula (4), based on the single data encryption utility value, data packet encryption utility value, single data encryption time cost and data packet encryption time cost, the single data encryption total utility, data packet encryption total utility, single data encryption total cost and data packet encryption total cost are accurately quantified respectively.

[0126] Through this scheme, mathematical analysis methods are used based on the data sensitivity index, according to the encryption method mapping value, encryption status value, the shown single data encryption time cost and data packet encryption time cost, to quantify the total utility of single data encryption, the total utility of data packet encryption, the total cost of single data encryption and the total cost of data packet encryption respectively, to achieve automated batch analysis of massive transaction data, improve the accuracy of utility and cost analysis, and at the same time improve data analysis efficiency.

[0127] In some embodiments, based on a local encryption policy set, according to the encryption method mapping value corresponding to each transaction data in the local encryption policy set, the real-time transaction data set is analyzed to extract a single data encryption time series data set and a data packet encryption time series data set; the single data encryption time series data set is analyzed, and a sensitive data update queue is constructed according to the transaction data in the single data encryption time series data set and its corresponding transaction time; the data packet encryption time series data set is analyzed, and a high-frequency data cache is constructed according to the transaction data packets in the data packet encryption time series data set and its corresponding transaction time; the overall write time span is determined according to the data scale corresponding to all transaction data and transaction data packets in the sensitive data update queue and the high-frequency data cache; based on the overall write time span, the cloud decryption update timeline is determined according to the transaction time corresponding to each transaction data and transaction data packet; a cloud decryption update policy set is constructed according to the sensitive data update queue, the high-frequency data cache and the cloud decryption update timeline.

[0128] The single data encryption time series data set may be a collection of transaction data with transaction time stamps that need to be single data encrypted.

[0129] The data packet encryption time series data set may be a collection of transaction data with transaction time stamps that need to be encrypted.

[0130] The sensitive data update queue may be a number of transaction data that needs to be updated first and takes a queue as a data structure.

[0131] The high-frequency data cache may be a cache with a key-value pair as a data structure for temporarily storing high-frequency trading data.

[0132] The overall write time span may be the event span required for the cloud platform to write all current transaction data.

[0133] The cloud decryption update timeline may include timeline information for decryption update time points of different transaction data.

[0134] Specifically, in the process of decrypting and writing the encrypted data provided by the POS machine on the cloud platform, the overall decryption and writing are carried out in sequence according to the transaction time of the transaction data. That is, the earlier the transaction time, the higher the priority of the decryption and writing. However, due to data delay considerations in the encryption stage, different types of transaction data are encrypted in a differentiated manner, resulting in differences in the data scale of the encrypted objects. If the decryption and writing are simply carried out in chronological order, it is easy to cause the writing time of highly sensitive single data to be constrained by the decryption of the data packet, resulting in low overall writing efficiency and increased uncertainty in the availability of key data. Therefore, it is necessary to make targeted adjustments to the cloud decryption update policy set according to the local encryption policy set, and construct a sensitive data update queue based on the transaction time corresponding to the single data encryption time series data set and its internal single data. This queue strictly follows the advanced The first-in-first-out principle is used to ensure the real-time writing of key data. For data packets with relatively low data sensitivity, a high-frequency data cache is built based on the transaction data packets in the data packet encryption time series data set and their corresponding transaction times. The cache can be used to achieve targeted access to data packets, and then the overall writing time span is determined based on the data scale corresponding to all transaction data and transaction data packets in the sensitive data update queue and the high-frequency data cache, providing a constraint on the overall time span for the allocation of decryption and writing time points for each transaction data. Then, through mathematical analysis, the cloud decryption update timeline is determined based on the transaction time corresponding to each transaction data and transaction data packet. The timeline records the time point when the cloud platform is expected to decrypt and write each transaction data and transaction data packet. In this way, the cloud decryption update strategy set is formed in combination with the sensitive data update queue and the high-frequency data cache.

[0135] Through this solution, based on the local encryption policy set, single data encryption time series data sets and data packet encryption time series data sets with time tags are extracted, and a sensitive data update queue is constructed according to the transaction data in the single data encryption time series data set and its corresponding transaction time. At the same time, a high-frequency data cache is constructed according to the transaction data packets in the data packet encryption time series data set and its corresponding transaction time. Through the cooperation of the sensitive data update queue and the high-frequency data cache, targeted decryption and writing of single data and data packets are realized, and based on the overall writing time span and according to the transaction time corresponding to each transaction data and transaction data packet, a cloud-based decryption update timeline containing the time point for decryption and writing of each transaction data and transaction data packet is constructed. According to the sensitive data update queue, the high-frequency data cache and the cloud-based decryption update timeline, a cloud-based decryption update policy set is constructed to improve the overall data decryption and writing efficiency and ensure the high availability of key data.

[0136] In some embodiments, the overall write time span is analyzed to determine the start time point of the write span; according to the transaction time corresponding to each transaction data and transaction data packet, the earliest transaction time point, the latest transaction time point and the representative timestamp corresponding to each transaction data and transaction data packet are determined; based on the overall write time span, according to the write span start time point, the earliest transaction time point and the latest transaction time point, the write time point corresponding to each transaction data and transaction data packet is determined, which is specifically the following formula (5):

[0137] (5)

[0138] in, Transaction data / transaction data package The corresponding writing time point, To write the span start time point, Transaction data / transaction data package The corresponding representative timestamp, The earliest trading time point, The latest trading time point, is the overall write time span, Transaction data / transaction data package The corresponding write thread number, is the total number of parallel threads, Update queues for sensitive data, Cache high-frequency data; build a cloud-based decryption update timeline based on the write time point corresponding to each transaction data and transaction data packet.

[0139] The write span start time point may be the start write time point in the overall write time span.

[0140] The earliest transaction time point may be the earliest transaction time point corresponding to the transaction data included in the data packet.

[0141] The latest transaction time point may be the latest transaction time point corresponding to the transaction data included in the data packet.

[0142] The representative timestamp may be a representative transaction timestamp among several transaction data in the data packet, and the representative timestamp may be the median transaction time node in the data packet.

[0143] The write thread number may be the number of each thread responsible for writing data in the cloud platform.

[0144] The total number of parallel threads may be the total number of threads in the cloud platform responsible for writing data in parallel.

[0145] Specifically, through formula (5) Describe transaction data / transaction data package The distribution of decryption write time in the overall time span is used to reflect the dynamic adjustment of different write times, thereby introducing , in order to reflect the impact of the time allocation delay of multi-threaded parallel writing on the writing time, so as to determine the writing time point of each transaction data / transaction data packet based on the starting time point of the writing span, and reasonably plan the cloud decryption update timeline.

[0146] Through this solution, mathematical analysis methods are used to analyze the writing time point corresponding to each transaction data and transaction data packet based on the overall writing time span and the transaction time corresponding to each transaction data and transaction data packet, and the cloud decryption update timeline is constructed to improve the decryption and writing efficiency of the cloud platform for transaction data and improve the availability of transaction data.

[0147] Figure 3 A schematic diagram of a cloud computing-based intelligent device management system is provided in accordance with an embodiment of the present application. Figure 3 As shown, a cloud computing-based smart device management system 300 of this embodiment includes: a state evaluation module 301 , an encryption strategy analysis module 302 , a decryption strategy analysis module 303 and a writing module 304 .

[0148] The state evaluation module 301 is used to obtain a POS machine working state data set, analyze the POS machine working state data set, and determine time series state evaluation information;

[0149] The encryption policy analysis module 302 is used to obtain a real-time business transaction data set, and determine a local encryption policy set based on the real-time business transaction data set and the timing state evaluation information;

[0150] A decryption policy analysis module 303, configured to analyze the local encryption policy set based on the real-time business transaction data set to determine a cloud decryption update policy set;

[0151] The writing module 304 is used to transmit and update the transaction data according to the local encryption policy set and the cloud decryption update policy set, and determine and output the POS machine collaborative processing report.

[0152] Optionally, the status evaluation module 301 is specifically used to: analyze the performance indicator set, the network indicator set and the load indicator set to determine the total number of indicators corresponding to the POS machine working status data set; extract the indicator type corresponding to each indicator according to the performance indicator set, the network indicator set and the load indicator set; determine the minimum indicator boundary value and the maximum indicator boundary value corresponding to each indicator according to the indicator type; based on the performance indicator set, the network indicator set and the load indicator set, determine the real-time status index corresponding to each POS machine according to the minimum indicator boundary value and the maximum indicator boundary value; determine the load change amplitude corresponding to each POS machine according to the load indicator set, and determine the predicted time span for each POS machine according to the load change amplitude; determine the predicted status index of each POS machine according to the predicted time span based on the performance indicator set, the network indicator set and the load indicator set; construct the time series status evaluation information according to the real-time status index and the predicted status index.

[0153] Optionally, the state evaluation module 301 determines the real-time state index corresponding to each POS terminal based on the performance indicator set, the network indicator set and the load indicator set according to the minimum indicator boundary value and the maximum indicator boundary value, specifically using the following formula: ;in, is the real-time status index, is the total number of indicators, For the The preset indicator influence coefficient corresponding to each indicator is: For the The indicator value corresponding to the indicator is For the The minimum indicator boundary value corresponding to the indicator, For the The maximum indicator boundary value corresponding to the indicator.

[0154] Optionally, when the state evaluation module 301 determines the predicted state index of each POS terminal based on the performance indicator set, the network indicator set and the load indicator set according to the predicted time span, it is specifically the following formula: ;in, is the predicted state index, To predict the time span, For the current time point The next The indicator value corresponding to the indicator is is the intercept term, For the The regression coefficient corresponding to the indicator is is the regression random error term.

[0155] Optionally, the encryption strategy analysis module 302 is specifically used to: analyze the real-time business transaction data set, extract the real-time transaction data set corresponding to each POS machine and the total amount of real-time transaction data; determine the real-time occupied resources of each POS machine at different time points according to the timing state evaluation information; determine the business sensitivity level of each POS machine and the data sensitivity index of each corresponding transaction data according to the real-time business transaction data set; analyze the real-time transaction data set according to the data sensitivity index, assign a corresponding type of encryption method to each transaction data, generate an encryption method mapping value and an encryption status value corresponding to each transaction data, and determine the total number of data packets accordingly; the encryption method includes single data encryption and data packet encryption; analyze the real-time transaction data set to determine the data size of each transaction data, and divide it into Analyze the encryption method corresponding to each transaction data, determine the single data encryption time cost corresponding to each transaction data and the data packet encryption time cost corresponding to each transaction data packet; based on the data sensitivity index, according to the encryption method mapping value, the encryption status value, the single data encryption time cost and the data packet encryption time cost, respectively determine the single data encryption total utility, data packet encryption total utility, single data encryption total cost and data packet encryption total cost; based on the real-time occupied resources and the business sensitivity level, according to the single data encryption total utility, the data packet encryption total utility, the single data encryption total cost and the data packet encryption total cost, maximize the total encryption utility; according to the encryption method mapping value of each transaction data corresponding to the maximized total encryption utility, construct the local encryption policy set.

[0156] Optionally, when the encryption strategy analysis module 302 maximizes the total encryption utility based on the real-time occupied resources and the service sensitivity level, according to the total single data encryption utility, the total data packet encryption utility, the single data encryption total cost, and the total data packet encryption cost, the formula is as follows: ;in, is the total encrypted utility, The total utility of the single data encryption, encrypting a total utility for said data packet, is the total cost of encrypting the single data, the total encryption cost for the data packet, is the preset balance adjustment coefficient, For resource budget, For the real-time occupation of resources, To preset the data sensitivity impact coefficient, is the business sensitivity level.

[0157] Optionally, when the encryption strategy analysis module 302 determines the total utility of single data encryption, the total utility of data packet encryption, the total cost of single data encryption, and the total cost of data packet encryption respectively based on the data sensitivity index, according to the encryption mode mapping value, the encryption state value, the single data encryption time cost, and the data packet encryption time cost, the specific formulas are as follows:

[0158] ;

[0159] in, For the single data encryption utility, Encryption utility for said data packet, is the total cost of encrypting the single data, the total encryption cost for the data packet, is the total amount of transaction data, is the single data encryption cost, For the The single data encryption utility value of each transaction data, Mapping value for the encryption method, For the The encrypted state value of the transaction data, To preset the data sensitivity impact coefficient, For the The data sensitivity index corresponding to the transaction data, is the total number of packets, For the The packet encryption utility value of the transaction packet, For the The single data encryption time cost required for each transaction data, For the The data packet encryption time cost of each transaction data packet.

[0160] Optionally, the decryption strategy analysis module 303 is specifically used to: based on the local encryption strategy set, analyze the real-time transaction data set according to the encryption method mapping value corresponding to each transaction data in the local encryption strategy set, and extract the single data encryption time series data set and the data packet encryption time series data set; analyze the single data encryption time series data set, and construct a sensitive data update queue according to the transaction data in the single data encryption time series data set and the transaction time corresponding to it; analyze the data packet encryption time series data set, and construct a high-frequency data cache according to the transaction data packets in the data packet encryption time series data set and the transaction time corresponding to it; determine the overall write time span according to the data scale corresponding to all transaction data and transaction data packets in the sensitive data update queue and the high-frequency data cache; determine the cloud decryption update timeline based on the overall write time span and the transaction time corresponding to each transaction data and transaction data packet; construct the cloud decryption update strategy set according to the sensitive data update queue, the high-frequency data cache and the cloud decryption update timeline.

[0161] Optionally, when determining the cloud decryption update timeline based on the overall write time span and the transaction time corresponding to each transaction data and transaction data packet, the decryption strategy analysis module 303 is specifically used to: analyze the overall write time span to determine the write span start time point; determine the earliest transaction time point, the latest transaction time point and the representative timestamp corresponding to each transaction data and transaction data packet according to the transaction time corresponding to each transaction data and transaction data packet; based on the overall write time span, determine the write time point corresponding to each transaction data and transaction data packet according to the write span start time point, the earliest transaction time point and the latest transaction time point, specifically the following formula: ;in, Transaction data / transaction data package The corresponding writing time point, is the start time point of the write span, Transaction data / transaction data package The corresponding representative timestamp, is the earliest transaction time point, is the latest transaction time point, for the overall write time span, Transaction data / transaction data package The corresponding write thread number, is the total number of parallel threads, updating a queue for said sensitive data, Cache the high-frequency data; and construct the cloud decryption update timeline according to the writing time point corresponding to each transaction data and transaction data packet.

[0162] The system of this embodiment can be used to execute the method of any of the above embodiments. The implementation principles and technical effects are similar and will not be described in detail here.

Claims

1. A cloud computing-based intelligent device management method, characterized in that: include: Acquire a POS machine working status data set, analyze the POS machine working status data set, and determine time series status evaluation information; Acquire a real-time business transaction data set, and determine a local encryption policy set based on the real-time business transaction data set and the timing state evaluation information; Based on the real-time business transaction data set, analyzing the local encryption policy set, and determining a cloud decryption update policy set; According to the local encryption strategy set and the cloud decryption update strategy set, the transaction data is transmitted and updated, and a POS machine collaborative processing report is determined and output; The determining of a local encryption strategy set based on the real-time business transaction data set and according to the time series state evaluation information includes: Analyze the real-time business transaction data set, extract the real-time transaction data set corresponding to each POS machine and the total amount of real-time transaction data; Determine the real-time occupied resources of each POS terminal at different time points according to the time sequence status evaluation information; Determine the business sensitivity level of each POS terminal and the data sensitivity index of each corresponding transaction data according to the real-time business transaction data set; Analyze the real-time transaction data set according to the data sensitivity index, assign a corresponding type of encryption method to each transaction data, generate an encryption method mapping value and an encryption status value corresponding to each transaction data, and determine the total number of data packets according to the encryption method mapping value and the encryption status value corresponding to each transaction data; The encryption method includes single data encryption and data packet encryption; Analyze the real-time transaction data set to determine the data size of each transaction data, and analyze the encryption method corresponding to each transaction data according to the data size of each transaction data to determine the single data encryption time cost corresponding to each transaction data and the data packet encryption time cost corresponding to each transaction data packet; Based on the data sensitivity index, according to the encryption mode mapping value, the encryption state value, the single data encryption time cost and the data packet encryption time cost, respectively determine the single data encryption total utility, the data packet encryption total utility, the single data encryption total cost and the data packet encryption total cost; Based on the real-time occupied resources and the service sensitivity level, according to the single data encryption total utility, the data packet encryption total utility, the single data encryption total cost and the data packet encryption total cost, maximizing the total encryption utility; The local encryption strategy set is constructed according to the encryption method mapping value corresponding to each transaction data under the maximized total encryption utility.

2. The method according to claim 1, characterized in that The POS machine working state data set includes a performance indicator set, a network indicator set and a load indicator set, and the analyzing the POS machine working state data set to determine the timing state evaluation information includes: Analyze the performance indicator set, the network indicator set and the load indicator set to determine the total number of corresponding indicators in the POS machine working status data set; Extracting the indicator type corresponding to each indicator according to the performance indicator set, the network indicator set and the load indicator set; According to the indicator type, determine the minimum indicator boundary value and the maximum indicator boundary value corresponding to each indicator; Based on the performance indicator set, the network indicator set and the load indicator set, and according to the minimum indicator boundary value and the maximum indicator boundary value, determine the real-time status index corresponding to each POS terminal; Determine the load variation range corresponding to each POS terminal according to the load indicator set, and determine the predicted time span for each POS terminal according to the load variation range; Based on the performance indicator set, the network indicator set and the load indicator set, and according to the predicted time span, determining the predicted state index of each POS terminal; The time series state evaluation information is constructed according to the real-time state index and the predicted state index.

3. The method according to claim 2, characterized in that The real-time status index corresponding to each POS terminal is determined based on the performance indicator set, the network indicator set and the load indicator set according to the minimum indicator boundary value and the maximum indicator boundary value, specifically as the following formula: ； in, is the real-time status index, is the total number of indicators, For the The preset indicator influence coefficient corresponding to each indicator is: For the The indicator value corresponding to the indicator is For the The minimum indicator boundary value corresponding to the indicator, For the The maximum indicator boundary value corresponding to the indicator.

4. The method according to claim 2, characterized in that: The prediction state index of each POS terminal is determined based on the performance indicator set, the network indicator set and the load indicator set according to the prediction time span, specifically as follows: ； in, is the predicted state index, To predict the time span, For the current time point The next The indicator value corresponding to the indicator is is the intercept term, is the total number of indicators, For the The regression coefficient corresponding to the indicator is is the regression random error term.

5. The method according to claim 1, characterized in that Based on the real-time occupied resources and the service sensitivity level, according to the total encryption utility of single data, the total encryption utility of data packet, the total encryption cost of single data and the total encryption cost of data packet, the total encryption utility is maximized, specifically as follows: ； in, is the total encrypted utility, The total utility of the single data encryption, encrypting a total utility for said data packet, is the total cost of encrypting the single data, the total encryption cost for the data packet, is the preset balance adjustment coefficient, For resource budget, For the real-time occupation of resources, To preset the data sensitivity impact coefficient, is the business sensitivity level.

6. The method according to claim 5, characterized in that Based on the data sensitivity index, according to the encryption mode mapping value, the encryption state value, the single data encryption time cost and the data packet encryption time cost, the single data encryption total utility, the data packet encryption total utility, the single data encryption total cost and the data packet encryption total cost are determined respectively, specifically as follows: ； in, The total utility of the single data encryption, encrypting a total utility for said data packet, is the total cost of encrypting the single data, the total encryption cost for the data packet, is the total amount of transaction data, For the The single data encryption utility value of each transaction data, Mapping value for the encryption method, For the The encrypted state value of the transaction data, To preset the data sensitivity impact coefficient, For the The data sensitivity index corresponding to the transaction data, is the total number of packets, For the The packet encryption utility value of the transaction packet, For the The single data encryption time cost required for each transaction data, For the The data packet encryption time cost of each transaction data packet.

7. The method according to claim 6, characterized in that The step of analyzing the local encryption policy set based on the real-time business transaction data set to determine the cloud decryption update policy set includes: Based on the local encryption policy set, according to the encryption mode mapping value corresponding to each transaction data in the local encryption policy set, the real-time transaction data set is analyzed to extract the single data encryption time series data set and the data packet encryption time series data set; Analyze the single data encrypted time series data set, and build a sensitive data update queue according to the transaction data in the single data encrypted time series data set and its corresponding transaction time; Analyze the data packet encryption time series data set, and build a high-frequency data cache according to the transaction data packets in the data packet encryption time series data set and their corresponding transaction times; Determine the overall write time span according to the data scale corresponding to all transaction data and transaction data packets in the sensitive data update queue and the high-frequency data cache; Based on the overall writing time span, and according to the transaction time corresponding to each transaction data and transaction data packet, determine the cloud decryption update timeline; The cloud decryption update strategy set is constructed according to the sensitive data update queue, the high-frequency data cache and the cloud decryption update timeline.

8. The method according to claim 7, characterized in that The determining of the cloud decryption update time axis based on the overall writing time span and according to the transaction time corresponding to each transaction data and transaction data packet includes: Analyze the overall writing time span to determine the starting time point of the writing span; According to the transaction time corresponding to each transaction data and transaction data packet, determine the earliest transaction time point, the latest transaction time point and the representative timestamp corresponding to each transaction data and transaction data packet; Based on the overall writing time span, the writing time point corresponding to each transaction data and transaction data packet is determined according to the writing span starting time point, the earliest transaction time point and the latest transaction time point, wherein the writing time point corresponding to each transaction data and transaction data packet is calculated using the following formula: ； in, Transaction data / transaction data package The corresponding writing time point, is the start time point of the write span, Transaction data / transaction data package The corresponding representative timestamp, is the earliest transaction time point, is the latest transaction time point, for the overall write time span, Transaction data / transaction data package The corresponding write thread number, is the total number of parallel threads, updating a queue for said sensitive data, caching the high-frequency data; The cloud decryption update timeline is constructed according to the writing time point corresponding to each transaction data and transaction data packet.

9. A cloud computing-based intelligent device management system, characterized in that: The method as claimed in any one of claims 1 to 8 comprises: A status evaluation module is used to obtain a POS machine working status data set, analyze the POS machine working status data set, and determine time series status evaluation information; An encryption policy analysis module, used to obtain a real-time business transaction data set, and determine a local encryption policy set based on the real-time business transaction data set and the timing state evaluation information; A decryption policy analysis module, configured to analyze the local encryption policy set based on the real-time business transaction data set to determine a cloud-based decryption update policy set; The writing module is used to transmit and update the transaction data according to the local encryption policy set and the cloud decryption update policy set, and determine and output the POS machine collaborative processing report.

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