A fuel dispenser data security encryption transmission management system

Abstract

The application discloses a fuel dispenser data security encryption transmission management system and relates to the field of data transmission management, which is used to solve the problem that the dynamic change of security priority and performance priority leads to frequent priority change, reduces data security and transmission efficiency, and causes waste of computing resources. The system classifies users according to the carrying capacity of the user's vehicle tank and the user interaction frequency, collects the mean value of the fuel dispenser transmission data, the mean value of the transmission rate, the data security transmission probability and the similarity between the transmission data and the original data of different categories of users, sets the fuel dispenser simple algorithm and the fuel dispenser standard algorithm, determines the security algorithm matched by different types of users, and then performs user allocation prediction analysis through a time series model to dynamically adjust the standard user allocation, improve the efficiency of data transmission, enhance the security of data transmission and reduce the delay during the peak period.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data transmission management, more particularly, the present application relates to a kind of fuel dispenser data security encryption transmission management system. BACKGROUND

[0002] With the development of information technology, gas station data management system gradually develops towards network and intelligence, not only meets the efficient management needs of daily operation of gas station, but also provides technical support for improving user experience and optimizing resource allocation, intelligent gas station data management system realizes interconnection of fuel dispenser, tank monitoring equipment and background management system through deep integration of advanced technology, Internet of Things technology, so that real-time data acquisition and analysis can be realized, equipment failure can be quickly responded, fueling process can be optimized, operating cost can be reduced, and good protection mechanism is provided for data transmission of multi-site fuel dispenser.

[0003] The prior art has the following disadvantages:

[0004] At present, in the actual fuel dispenser data security encryption transmission process, the security priority and performance priority of the user can change dynamically, which can cause frequent priority changes, the selection of encryption algorithm can not adapt to real-time requirements, reduce data security and transmission efficiency, and cause waste of computing resources. Therefore, a fuel dispenser data security encryption transmission management system is proposed.

[0005] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, so it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a fuel dispenser data security encryption transmission management system, which uses different product inspection methods to solve the problems raised in the above background technology.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical scheme, a kind of fuel dispenser data security encryption transmission management system, including data acquisition module, user classification module, user matching module and secondary division module;Signal connection between each module;

[0008] The data acquisition module is used to collect user behavior characteristic information, and through data processing, the user vehicle tank load and user interaction frequency are obtained, and sent to the user classification module;

[0009] The user classification module is configured to receive the user vehicle fuel tank carrying capacity and the user interaction frequency, establish a data analysis model, obtain a user evaluation coefficient, compare the user evaluation coefficient with a preset user threshold value, determine a user classification result, and collect the average transmission data amount, the average transmission rate, the data safe transmission probability, and the transmission data similarity with the original data of the fuel dispenser according to the user classification result, and send the same to the user matching module.

[0010] The user matching module is configured to receive the average transmission data amount, the average transmission rate, the data safe transmission probability, and the transmission data similarity with the original data of the fuel dispenser, set a simple algorithm of the fuel dispenser and a standard algorithm of the fuel dispenser, determine a safety algorithm matched by a user of different types, and send the same to the secondary division module.

[0011] The secondary division module is configured to receive the safety algorithm matched by the user of different types, obtain a standard user number and an expected uploading time length, perform a weighted calculation to obtain a standard user coefficient, compare the standard user coefficient with a preset transmission threshold value, determine a delay result, perform user distribution prediction analysis through a time series model constructed in advance, and further refine and optimize a standard user distribution scheme.

[0012] In a preferred embodiment, the user behavior characteristic information includes the user vehicle fuel tank carrying capacity and the user interaction frequency.

[0013] The fueling records associated with the user identification are counted, and the historical fueling amount and the current fuel amount change are added to obtain the user vehicle fuel tank carrying capacity.

[0014] The number of interaction events generated by the user within a unit time is counted, and a unit time length corresponding to the number of interaction events is counted to obtain the user interaction frequency.

[0015] In a preferred embodiment, the user vehicle fuel tank carrying capacity and the user interaction frequency are obtained, and a user evaluation coefficient is obtained through a weighted calculation.

[0016] After the user evaluation coefficient is obtained, the user evaluation coefficient is compared and analyzed with a user threshold value that is iteratively updated.

[0017] If the user evaluation coefficient is greater than or equal to the user threshold value, the current user is marked as a standard user, and a safety signal is generated.

[0018] If the user evaluation coefficient is less than the user threshold value, the current user is marked as a simple user, and a rate signal is generated.

[0019] According to the marking result, a classification result of the user is divided, and the classification result of the standard user and the simple user is specifically divided.

[0020] In a preferred embodiment, according to the user classification result, the average transmission data volume and the average transmission rate of the fuel dispenser corresponding to the simple user are collected; and the data security transmission probability and the similarity between the transmission data and the original data corresponding to the standard user are collected;

[0021] The data transmission volume is recorded in real time through the transmission interface, the instantaneous data transmission volume of the current user is collected multiple times and accumulated to obtain the total data transmission volume per unit time, and the average transmission data volume of the fuel dispenser is obtained by ratio calculation with the unit time length;

[0022] In a unit of time, the instantaneous transmission rate is collected through network monitoring at regular intervals, and the average transmission rate is obtained by ratio calculation with the unit time length;

[0023] By recording the state of each transmission, the number of safe transmissions per unit time is counted, and the data security transmission probability is obtained by ratio calculation with the total number of transmissions;

[0024] The content of the transmission data and the content of the original data are converted into vectors, and the cosine similarity calculation is performed to obtain the similarity between the transmission data and the original data.

[0025] In a preferred embodiment, for the average transmission data volume and the average transmission rate of the fuel dispenser of the simple user, the simple fuel dispenser algorithm is used to obtain the simple discriminant coefficient, and the specific steps are as follows:

[0026] The fuel dispenser transmission coefficient is calculated using the Robust Scaling formula: where y is the fuel dispenser transmission coefficient, x is the average transmission data volume of the fuel dispenser, is the median value of the transmission data volume of the fuel dispenser, is the difference between the 1 / 4 quantile and the 3 / 4 quantile when the average transmission data volume of the fuel dispenser is arranged in ascending order;

[0027] The transmission rate coefficient is calculated using the same Robust Scaling formula, and the sum of the fuel dispenser transmission coefficient and the transmission rate coefficient is obtained to obtain the simple discriminant coefficient;

[0028] The simple discriminant coefficient is compared and analyzed with the preset simple threshold value, if the simple discriminant coefficient is greater than or equal to the simple threshold value, the data transmission of the current user is transmitted using the fast discriminant safety algorithm, if the simple discriminant coefficient is less than the simple threshold value, the data transmission of the current user is transmitted using the conventional safety algorithm.

[0029] In a preferred embodiment, for the data security transmission probability and the similarity between the transmission data and the original data of the standard user, the standard fuel dispenser algorithm is used to obtain the standard discriminant coefficient, and the specific steps are as follows:

[0030] The linear combination of the calculation standard discriminant coefficient is constructed, and the calculation formula is:

[0031]

[0032] In the formula, z is the standard discriminant coefficient, a is the data security transmission probability, b is the similarity of the transmitted data and the original data, is the optimal weight of a, is the optimal weight of b;

[0033] The optimal weight is obtained by the lasso regression formula;

[0034] The standard discriminant coefficient is compared and analyzed with the preset standard threshold value, if the standard discriminant coefficient is greater than or equal to the standard threshold value, the data transmission of the current user is transmitted using the standard security algorithm, if the standard discriminant coefficient is less than the standard threshold value, the data transmission of the current user is transmitted using the conventional security algorithm.

[0035] In a preferred embodiment, the standard users are classified from all the online users at present, and the number of the standard users is counted to obtain the number of the standard users;

[0036] The result of the ratio of the data amount uploaded by all the standard users to the network bandwidth is added to the total value of the encryption and decryption complexity related to the security algorithm to obtain the predicted uploading time length;

[0037] The number of the standard users and the predicted uploading time length are weighted to obtain the standard user coefficient, and the standard user coefficient is compared with the preset transmission threshold value, if the standard user coefficient is greater than or equal to the transmission threshold value, the delay result is generated.

[0038] In a preferred embodiment, the delay result is obtained, and a time series model is used for user allocation prediction analysis, the time series model adopts an ARIMAX model, and the specific steps of the user allocation prediction analysis by the time series model are as follows:

[0039] Step A1, obtaining prediction data;

[0040] Step A2, establishing an ARIMAX model;

[0041] Step A3, using the maximum likelihood estimation (MLE) method to estimate the ARIMAX model parameters;

[0042] Step A4, verifying the effect of the fitted model, and checking the goodness of fit of the model by residual analysis method;

[0043] Step A5, using the fitted model to predict the future user allocation scheme, and taking the maximum value of the prediction result as the maximum value of the transmission delay in the future unit time.

[0044] In a preferred embodiment, the exogenous variables in the ARIMAX model include the data security transmission probability and the transmission data similarity to the original data, and the ARIMAX model formula is as follows:

[0045]

[0046] wherein, is the transmission delay of the current unit time, is a constant term, is the i-th order autoregressive parameter, is the order of the autoregressive term, is the j-th order moving average parameter, is the order of the moving average term, is a white noise term, representing a random error, is the coefficient of the exogenous variable , is the lag order of the exogenous variable, is the exogenous variable at lag k.

[0047] In a preferred embodiment, in step A3, , , , , is obtained by maximum likelihood estimation method.

[0048] Technical effects and advantages of the present application:

[0049] 1. The present application classifies users according to their vehicle fuel tank carrying capacity and user interaction frequency, and collects the average transmission data volume, transmission rate, data security transmission probability and transmission data similarity to the original data of the fuel dispenser for different categories of users, sets up a simple algorithm for the fuel dispenser and a standard algorithm for the fuel dispenser, determines the safety algorithm matched for different types of users, improves the efficiency of data transmission, enhances the safety of data transmission, reduces the calculation and resource consumption, and enhances the user experience.

[0050] 2. The present application receives the safety algorithm matched for different types of users, obtains the number of standard users and the expected upload time, and calculates the standard user coefficient by weighting, compares it with the preset transmission threshold, determines the delay result, performs user allocation prediction analysis through the time series model, dynamically adjusts the standard user allocation according to the maximum value and the average value of the prediction result, reduces the delay during peak period, sets up accurate delay management, and ensures accurate control of transmission time. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1A schematic diagram of a module of the fuel dispenser data security encryption transmission management system of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0053] Embodiment 1

[0054] The present application discloses a wireless security system based on Internet of Things, as shown in the figure, comprising a data acquisition module, a user classification module, a user matching module and a secondary division module; the modules are signal connected; Figure 1

[0055] The data acquisition module is used for acquiring user behavior characteristic information, and obtaining user vehicle tank carrying capacity and user interaction frequency through data processing, and sending to the user classification module;

[0056] The user behavior characteristic information includes user vehicle tank carrying capacity and user interaction frequency.

[0057] The data processing refers to the standardization and quantization processing of the original data of the user behavior characteristic information, and the specific data normalization and standardization is to map the characteristics of the user vehicle tank carrying capacity and the user interaction frequency between the interval 0 to 1, and then adjust the data to the distribution of zero mean and unit standard deviation, etc. The specific standardization and normalization operation steps are prior art, and will not be described here.

[0058] The user vehicle tank carrying capacity refers to the maximum oil capacity that the vehicle tank can hold, and the acquisition logic is to add the historical oiling amount and the current oil amount change to obtain the user vehicle tank carrying capacity by counting the fueling records associated with the user identification;

[0059] Specifically, the user identification refers to an identification that can identify the user characteristics, specifically including but not limited to license plate number, membership card number or fueling account, which will not be described here.

[0060] The current oil amount change refers to the change of the oil amount in the user vehicle tank, which is monitored by an oil amount sensor, usually a float type sensor or a capacitive type sensor, in real time, and transmits the current oil amount change to the fuel dispenser.

[0061] ​The user interaction frequency refers to the frequency of user interaction with the fuel dispenser system per unit time, and the acquisition logic is to calculate the user interaction frequency by taking the ratio of the number of interaction events generated by the user per unit time and the corresponding unit time length;

[0062] The interactive operations include fueling operations, two-dimensional code interactions, and other service requests, and the interactive operations are limited to the above operations, which will not be described here;

[0063] It should be noted that the unit time can be set to "48 hours", "120 hours" and "one month", wherein the specific setting is obtained by the experimenters according to the specific fuel dispenser running time and the oil replenishment interval, which is not limited here;

[0064] The user classification module is used to receive the user vehicle fuel tank carrying capacity and the user interaction frequency, establish a data analysis model, obtain a user evaluation coefficient, and compare it with a preset user threshold value to determine the user classification result, and according to the user classification result, the fuel dispenser transmission data amount average, transmission rate average, data safe transmission probability and transmission data and original data similarity are collected and sent to the user matching module;

[0065] The data analysis model refers to a weighted analysis model, which generates a user evaluation coefficient through weighted calculation;

[0066] The user vehicle fuel tank carrying capacity and the user interaction frequency are obtained, and the user evaluation coefficient is obtained through weighted calculation;

[0067] The acquisition logic of the user threshold value is to collect a historical user classification set, then divide the data set into a training set and a test set, set an evaluation index and a clustering algorithm, train the model on the training set in each iteration of cross-validation, and evaluate the model performance on the test set, then adjust the user threshold value according to the performance of the validation set, therefore, the user threshold value is constantly updated;

[0068] In the present application, the clustering algorithm is a class of unsupervised learning algorithms, which is used to divide the data points in the data set into groups or clusters with similarity; the common one is K-means clustering, which divides the data points in the data set into K clusters, so that the distance between each curve data point and the center point (centroid) of its belonging cluster is minimized, and finally the effect of the adjusted user threshold value is measured by the Euclidean distance, so as to set the user threshold value;

[0069] After obtaining the user evaluation coefficient, the user evaluation coefficient is compared and analyzed with the constantly iterated user threshold value;

[0070] If the user evaluation coefficient is greater than or equal to the user threshold value, the current user is marked as a standard user, and a safety signal is generated;

[0071] If the user evaluation coefficient is less than the user threshold value, the current user is marked as a simple user, and a rate signal is generated;

[0072] Specifically, the user marked as a standard user needs a higher security performance calculation security algorithm, and vice versa, the user marked as a simple user needs a simple and convenient calculation security algorithm; therefore, for the user marked as a standard user, the data security transmission probability and the transmission data similarity with the original data are collected; for the user marked as a simple user, the fuel dispenser transmission data amount average and the transmission rate average are collected;

[0073] The selection of specific security algorithms can be determined according to the experimental results of the experimenters or the output results of specific algorithm experiments, which are not described here;

[0074] According to the marking result, the classification result of the user is divided, specifically, the classification result of the user marked as a standard user and a simple user;

[0075] According to the user classification result, the fuel dispenser transmission data amount average and the transmission rate average corresponding to the simple user are collected; and the data security transmission probability and the transmission data similarity with the original data corresponding to the standard user are collected;

[0076] The fuel dispenser transmission data amount average refers to the data amount transmitted by the fuel dispenser through the network per unit time, and the acquisition logic is to record the data transmission amount in real time through the transmission interface, collect multiple instantaneous transmission amounts, and add the total transmission amount per unit time to obtain the fuel dispenser transmission data amount average by ratio calculation with the unit time length;

[0077] It should be noted that the transmission interface refers to the communication medium and technical means for data exchange between the fuel dispenser and the data management system, which is used to realize the uploading, downloading and interaction of the fuel dispenser data. Specifically, the transmission interface defines the format, coding and transmission method of the data with the protocol interface; the specific transmission protocol and data format protocol are not limited, but are set by the experimenters according to the specific data characteristics, which are not described here;

[0078] The acquisition logic of the transmission rate average is to collect the instantaneous transmission rate through network monitoring at a fixed time per unit time, and to obtain the transmission rate average by ratio calculation with the unit time length;

[0079] The data security transmission probability refers to the probability that the data is successfully encrypted and not damaged or tampered with during the transmission process per unit time, and the acquisition logic is to record the state of each transmission, count the safe transmission per unit time, and calculate the data security transmission probability by ratio with the total transmission times;

[0080] Specifically, the definition of secure transmission refers to the transmission process in which the data is not intercepted and tampered with, the encryption algorithm works normally, the decrypted data is consistent with the original data, and the data is not attacked by network attacks.

[0081] The similarity between the transmission data and the original data refers to the similarity between the data transmitted to the target system and the original data during the data transmission process, thereby determining the integrity, accuracy and fidelity of the data during the transmission process. The logic for obtaining the similarity is based on the similarity of the transmission data content. The content of the transmission data and the content of the original data are converted into vectors and substituted into the cosine similarity calculation to obtain the similarity between the transmission data and the original data.

[0082] Specifically, the content of the transmission data and the original data is represented by vectors to obtain the vectors of the original data and the transmission data, which are substituted into the cosine similarity calculation. The specific formula is as follows:

[0083]

[0084] In the formula, is the similarity between the transmission data and the original data, A and B represent the vectors of the original data and the transmission data, and respectively represent the value of the i-th element in the original data vector and the transmission data vector, and n is the dimension of the vector, i.e. the number of data points.

[0085] It should be noted that the above-mentioned average transmission data volume of the fuel dispenser, average transmission rate, data security transmission probability, and similarity between the transmission data and the original data are obtained by averaging the probability and similarity during the transmission process. In the transmission process, multiple groups of data are usually included, and the number of specific data groups is not limited. Instead, different data groups are obtained by the experimenters' prior setting, which is not described here.

[0086] The user matching module is used to receive the average transmission data volume of the fuel dispenser, the average transmission rate, the data security transmission probability, and the similarity between the transmission data and the original data, set the simple algorithm of the fuel dispenser and the standard algorithm of the fuel dispenser, determine the security algorithm matched by different types of users, and send it to the secondary division module.

[0087] For the average transmission data volume and the average transmission rate of the simple user comprehensive fuel dispenser, the simple algorithm of the fuel dispenser is used to obtain the simple discrimination coefficient. The specific steps are as follows:

[0088] The fuel dispenser transmission coefficient is calculated using the Robust Scaling formula: where y is the fuel dispenser transmission coefficient, x is the average transmission data volume of the fuel dispenser, is the median value of the refueling machine transmission data volume, is the difference between the 1 / 4 quantile and the 3 / 4 quantile when the refueling machine transmission data volume mean value is arranged from small to large,

[0089] The refueling machine transmission coefficient and the transmission rate coefficient are summed to obtain the simple discriminant coefficient; specifically, the transmission rate coefficient is calculated using the same formula;

[0090] It should be noted that the Robust Scaling algorithm is a data standardization algorithm, and in this example, the refueling machine transmission data volume is compressed to calculate the simple discriminant coefficient, which can dynamically adjust the transmission mode and security strategy of the refueling machine to adapt to different user needs and network environments;

[0091] The simple discriminant coefficient is compared and analyzed with the preset simple threshold value, if the simple discriminant coefficient is greater than or equal to the simple threshold value, the current user's data transmission uses the fast verification security algorithm for transmission, if the simple discriminant coefficient is less than the simple threshold value, the current user's data transmission uses the regular security algorithm for transmission;

[0092] For the standard user comprehensive data security transmission probability and the transmission data similarity with the original data, the refueling machine standard algorithm is used to obtain the standard discriminant coefficient, and the specific steps are as follows:

[0093] A linear combination for calculating the standard discriminant coefficient is constructed, and the calculation formula is:

[0094]

[0095] In the formula, z is the standard discriminant coefficient, a is the data security transmission probability, b is the transmission data similarity with the original data, is the optimal weight of a, is the optimal weight of b; the data security transmission probability and the transmission data similarity with the original data in each group of data in the first transmission process are summed and calculated, and the calculation formula is: , wherein, is the standard discriminant coefficient of the cth group, is the data security transmission probability of the cth group, is the transmission data similarity with the original data of the cth group, and the standard discriminant coefficient of each group of data is obtained.

[0096] Further, the lasso regression formula is used to calculate the two optimal weights, i.e., the optimal weights and , and the specific formula expression is:

[0097]

[0098] In the formula, is a regularization parameter, m is the number of data groups in a transmission process, and when 、 The lasso regression formula is used to minimize the calculation result, and then 、 are the optimal weights of a and b, respectively.

[0099] The standard discriminant coefficient is compared with a preset standard threshold value, if greater than or equal to the standard threshold value, the data transmission of the current user is transmitted using the standard security algorithm, if less than the standard threshold value, the data transmission of the current user is transmitted using the conventional security algorithm.

[0100] Specifically, for example: the standard security algorithm uses the asymmetric encryption algorithm RSA to encrypt the data, ensures that the data is not leaked in the transmission process, and uses the hash algorithm SHA-256 to generate the hash value of the data, and the hash value is attached in the data transmission, which is used to verify the security of the transmission, uses the secure protocol SSL and TLS to encrypt the data transmission channel, sets multiple authentication, and the experiment personnel regularly set risk assessment to prevent data from being stolen or tampered with in the transmission process.

[0101] For example: the conventional security algorithm uses the symmetric encryption AES to encrypt the data, uses the lightweight hash algorithm MD5 to verify the possibility of data tampering, and uses the lightweight encryption protocol HTTPS to encrypt the data transmission channel.

[0102] For example: the fast calculation security algorithm uses the stream cipher encryption algorithm RC4, and uses the simplified hash algorithm CRC32 for verification, and uses the compression algorithm to increase the calculation speed.

[0103] It should be noted that the setting of the standard security algorithm, the conventional security algorithm and the fast calculation security algorithm is set by the experiment personnel according to the experimental results of data transmission, which is not limited here.

[0104] The present application classifies users according to the user vehicle fuel tank carrying capacity and user interaction frequency, and collects the average transmission data amount, average transmission rate, data security transmission probability and transmission data similarity of the fuel dispenser for different types of users, sets the fuel dispenser simple algorithm and the fuel dispenser standard algorithm, determines the security algorithm matched by different types of users, improves the efficiency of data transmission, enhances the security of data transmission, reduces the calculation and resource consumption, and enhances the user experience.

[0105] Example 2

[0106] The embodiment 1 of the present application focuses on illustrating that the users are classified by the carrying capacity of the user's vehicle fuel tank and the user interaction frequency, and the mean value of the data transmission amount, the mean value of the transmission rate, the data security transmission probability, and the similarity between the transmission data and the original data of the fuel dispenser are collected according to different categories of users, the simple algorithm of the fuel dispenser and the standard algorithm of the fuel dispenser are set, and the operation strategy of the security algorithm matched by different types of users is determined; but in the embodiment 1, different fuel dispenser algorithms are used for different categories of users, and the selection of the standard user defaults to the standard algorithm of the fuel dispenser, which further increases the running cost of the system and reduces the user experience when there are many standard users; in view of the above problems, the embodiment 2 of the present application is further refined;

[0107] The secondary division module is used for receiving the security algorithm matched by different types of users, obtaining the number of standard users and the predicted upload time length, and performing weighted calculation to obtain the standard user coefficient, comparing with the preset transmission threshold value, determining the delay result, and performing user allocation prediction analysis through the pre-constructed time series model to further refine and optimize the standard user allocation scheme;

[0108] The acquisition logic of the number of standard users is to classify the current online users as standard users and count the number to obtain the number of standard users;

[0109] The specific classification method of the current online users has been described in the embodiment 1, and will not be repeated here;

[0110] The acquisition logic of the predicted upload time length is to add the ratio of the data amount uploaded by all standard users to the network bandwidth and the total value of the encryption and decryption complexity related to the security algorithm to obtain the predicted upload time length;

[0111] Specifically, the encryption and decryption complexity related to the security algorithm is obtained according to the different security algorithms set in the above embodiment 1, and will not be repeated here;

[0112] The number of standard users and the predicted upload time length are weighted calculated to obtain the standard user coefficient, which is compared with the preset transmission threshold value, if the standard user coefficient is greater than or equal to the transmission threshold value, the delay result is generated, otherwise it is not changed;

[0113] The delay result represents that the number of the current standard user group is too large, resulting in transmission delay;

[0114] It should be noted that the time series model used in this embodiment is an ARIMAX model, and the refinement and optimization of the standard user allocation scheme means that under the condition of determining the delay of the current standard user allocation scheme, the standard user allocation scheme is further analyzed and adjusted to be more scientific and reasonable, so as to realize the control of the transmission time length;

[0115] Further, the specific steps of the user allocation prediction analysis by the time series model are as follows:

[0116] Step A1, obtaining prediction data;

[0117] Step A2, establishing an ARIMAX model;

[0118] Step A3, using the maximum likelihood estimation (MLE) method to estimate the ARIMAX model parameters;

[0119] Step A4, verifying the effect of the fitted model, and checking the goodness of fit of the model by residual analysis method;

[0120] Step A5, using the fitted model to predict the future allocation scheme, and taking the maximum value of the prediction result as the maximum value of the transmission delay in the future unit time.

[0121] Specifically, the prediction data includes the data security transmission probability corresponding to each unit time, the transmission data and the original data similarity, and the transmission delay data; wherein the data security transmission probability corresponding to each unit time and the transmission data and the original data similarity have been exemplified in Embodiment 1 and will not be repeated here;

[0122] The transmission delay data refers to the historical transmission delay data, which is taken as the main variable of the time series;

[0123] Further, the basic form of the ARIMAX model is:

[0124]

[0125] In the formula, is the transmission delay of the current unit time, is a constant term, is the i-th order autoregressive parameter, is the order of the autoregressive term, is the j-th order moving average parameter, is the order of the moving average term, is a white noise term representing random error, is the coefficient of the exogenous variable is the lag order of the exogenous variable, is the lag k period of the exogenous variable; It should be noted that the exogenous variable part can integrate the influence of other related variables (such as data security transmission probability and transmission data and original data similarity) on the transmission delay;

[0126] It should be noted that in step A3,

[0127] , , 、 、 、 The acquisition is calculated by the maximum likelihood estimation (MLE) method, and the specific steps are as follows:

[0128] Error term Subject to normal distribution Then the likelihood function is:

[0129]

[0130] Take the logarithm of the likelihood function to get the log-likelihood function:

[0131]

[0132] By maximizing the log-likelihood function, the parameter estimate is obtained 、 、 、 、 ;

[0133] By the maximum value and the average value of the prediction result, the standard user allocation strategy is dynamically adjusted;

[0134] The embodiment is as follows:

[0135] The maximum value of the prediction result represents the highest point of the transmission delay in the future unit time, and the peak allocation strategy is formulated, and in the predicted transmission delay peak period (i.e. the time point close to or equal to ), the number of standard users is reduced by 20% to ensure the smooth operation of the system and avoid transmission delay;

[0136] According to the average value of the prediction result , a balanced allocation strategy is formulated, and the number of standard users and the user structure are maintained in the period when the transmission delay is close to the average value;

[0137] Among them, the setting of the peak period and the balance period is obtained by the specific implementation of the experiment personnel, and the limit of the division is not limited;

[0138] The application receives different types of user matching security algorithms to obtain the number of standard users and the expected upload time and weighted calculation to obtain the standard user coefficient, compares with the preset transmission threshold, determines the delay result, performs user allocation prediction analysis through the time series model, dynamically adjusts the standard user allocation according to the maximum value and the average value of the prediction result, reduces the peak period delay, sets accurate delay management, and guarantees the accurate control of the transmission time.

[0139] The above formulas are all de-dimensioned to calculate the numerical values, the formulas are obtained by collecting a large amount of data to simulate a formula of the most recent real situation, and the preset parameters in the formulas are set by a person skilled in the art according to the actual situation.

[0140] The above embodiments can be implemented wholly or partially by software, hardware, firmware, or any other combination. When implemented by software, the above embodiments can be implemented wholly or partially in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0141] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0142] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0143] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0144] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. The division of the units is merely logical function division. There can be other division manners in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0145] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0146] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0147] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0148] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A data security encryption transmission management system for fuel dispensers, characterized in that: It includes a data acquisition module, a user classification module, a user matching module, and a secondary segmentation module; signal connections between the modules; The data acquisition module is used to collect user behavior feature information, and through data processing, obtain the user's vehicle fuel tank capacity and user interaction frequency, and send them to the user classification module. The user classification module obtains the user's vehicle fuel tank capacity and user interaction frequency, and calculates the user evaluation coefficient through weighted calculation. After obtaining the user evaluation coefficient, the user evaluation coefficient is compared and analyzed with the continuously iterated user threshold. If the user evaluation coefficient is greater than or equal to the user threshold, the current user is marked as a standard user and a safety signal is generated. If the user evaluation coefficient is less than the user threshold, the current user is marked as a simplified user and a rate signal is generated. Based on the labeling results, the users are classified into standard users and simplified users. Based on the user classification results, the average amount of data transmitted by the fuel dispensers corresponding to simplified users and the average transmission rate are collected. It also collects the probability of secure data transmission corresponding to standard users and the similarity between the transmitted data and the original data; and sends the collected data to the user matching module. The user matching module receives the average amount of data transmitted from the fuel dispenser, the average transmission rate, the probability of secure data transmission, and the similarity between the transmitted data and the original data. It sets up a simplified discrimination algorithm for fuel dispensers based on the RobustScaling formula and a standard discrimination algorithm for fuel dispensers based on linear combination and lasso regression to determine the security algorithm matched for different types of users. When the user classification result is a simplified user, the simplified algorithm for fuel dispensers is used. The simplified discrimination coefficient is calculated using the RobustScaling formula and compared with a preset simplified threshold. If the simplified discrimination coefficient is greater than or equal to the simplified threshold, a fast verification security algorithm using the RC4 stream cipher encryption algorithm, CRC32 hash checksum and compression algorithm is matched. If the simplified discrimination coefficient is less than the simplified threshold, a conventional security algorithm using symmetric encryption AES, MD5 hash algorithm and HTTPS encryption protocol is matched. When the user classification result is a standard user, the standard algorithm of the fuel dispenser is adopted. The standard discriminant coefficient is calculated by constructing a linear combination through the lasso regression formula. The standard discriminant coefficient is compared with the preset standard threshold. If the standard discriminant coefficient is greater than or equal to the standard threshold, a standard security algorithm using asymmetric encryption RSA, SHA-256 hash algorithm and SSL / TLS security protocol is matched. If the standard discriminant coefficient is less than the standard threshold, a conventional security algorithm using symmetric encryption AES, MD5 hash algorithm and HTTPS encryption protocol is matched. The matching security algorithm is then sent to the secondary partitioning module; The secondary segmentation module is used to receive the security algorithms matched to different types of users, classify standard users from all current online users, and count the number of standard users; the estimated upload time is obtained by adding the ratio of the amount of data uploaded by all standard users to the network bandwidth and the total encryption and decryption complexity related to the security algorithm. The standard number of users is weighted and calculated with the expected upload time to obtain the standard user coefficient, which is then compared with the preset transmission threshold. If the standard user coefficient is greater than or equal to the transmission threshold, the delay result is generated. Furthermore, user allocation prediction analysis is performed using a pre-built time series model to further refine and optimize the standard user allocation scheme.

2. The data security encryption transmission management system for fuel dispensers according to claim 1, characterized in that: User behavior characteristics include the fuel tank capacity of the user's vehicle and the frequency of user interaction. The system collects refueling records associated with user identifiers and adds the historical refueling amount to the current fuel level change to obtain the fuel tank capacity of the user's vehicle. The user interaction frequency is calculated by comparing the number of user interaction events per unit time with the corresponding unit time length.

3. The data security encryption transmission management system for fuel dispensers according to claim 2, characterized in that: The user matching module uses a simplified user matching security algorithm based on the Robust Scaling formula to identify fuel dispensers. The specific steps are as follows: Calculate the fuel dispenser transmission coefficient using the Robust Scaling formula: Where y is the fuel dispenser transmission coefficient, and x is the average amount of data transmitted by the fuel dispenser. This is the median value of the amount of data transmitted by the fuel dispenser. The difference between the 1 / 4 quartile and the 3 / 4 quartile when the average amount of data transmitted by the fuel dispenser is arranged from smallest to largest. The transmission rate coefficient was calculated using the Robust Scaling formula, and the simple discrimination coefficient was obtained by summing the refueling machine transmission coefficient and the transmission rate coefficient.

4. The data security encryption transmission management system for fuel dispensers according to claim 3, characterized in that: The user matching module uses a standard user matching security algorithm based on linear combination and lasso regression to determine the standard discrimination algorithm for fuel dispensers. The specific steps are as follows: Construct a linear combination for calculating the standard discriminant coefficients, and the formula for its calculation is as follows: In the formula, z is the standard discrimination coefficient, a is the probability of secure data transmission, and b is the similarity between the transmitted data and the original data. The optimal weight for a is... The optimal weight for b; The optimal weights are obtained using the lasso regression formula.

5. The data security encryption transmission management system for fuel dispensers according to claim 4, characterized in that: After obtaining the delayed results, a time series model is used for user allocation prediction analysis. The time series model used is the ARIMAX model. The specific steps for user allocation prediction analysis using the time series model are as follows: Step A1: Obtain the data for prediction; Step A2: Create an ARIMAX model; Step A3: Use the maximum likelihood estimation method to estimate the ARIMAX model parameters; Step A4: Verify the effectiveness of the fitted model by checking the goodness of fit of the model using residual analysis. Step A5: Use the fitted model to predict the future user allocation scheme, and take the maximum value of the prediction result as the maximum value of the transmission delay per unit time in the future.

6. The data security encryption transmission management system for fuel dispensers according to claim 5, characterized in that: The exogenous variables of the ARIMAX model include the probability of secure data transmission and the similarity between the transmitted data and the original data, and the formula of the ARIMAX model is: In the formula, The transmission delay per unit of time. For constant terms, Let be the i-th order autoregressive parameter. It is the order of the autoregressive term. Let be the parameters of the j-th moving average. It is the order of the moving average term. It is a white noise term, representing random error. It is an exogenous variable coefficient, It is the lag order of the exogenous variable. It is an exogenous variable lagged by k periods.

7. The data security encryption transmission management system for fuel dispensers according to claim 6, characterized in that: In step A3, , , , , It is obtained by the maximum likelihood estimation method.

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