Oiling machine data security encryption transmission management system

Abstract

The invention discloses a data security encryption transmission management system for an oiling machine, relates to the field of data transmission management, and is used for solving the problems of frequent priority change, reduction of data security and transmission efficiency and waste of computing resources caused by dynamic change of security priority and performance priority. The method comprises the following steps: classifying users through user vehicle fuel tank bearing capacity and user interaction frequency, respectively acquiring an oiling machine transmission data volume mean value, a transmission rate mean value, a data safety transmission probability and similarity between transmission data and original data according to different types of users, and setting an oiling machine simple algorithm and an oiling machine standard algorithm; according to the method, security algorithms matched with different types of users are determined, user allocation prediction analysis is carried out through a time sequence model, standard user allocation is dynamically adjusted, the data transmission efficiency is improved, the data transmission security is enhanced, and peak time delay is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data transmission management, and more specifically, to a fuel dispenser data security encryption transmission management system. Background Art

[0002] With the development of information technology, gas station data management systems are gradually moving towards networking and intelligence, which not only meets the efficient management needs of daily gas station operations, but also provides technical support for improving user experience and optimizing resource allocation. Intelligent gas station data management systems deeply integrate advanced technologies. The Internet of Things technology enables gas pumps, oil tank monitoring equipment and background management systems to be interconnected, so that data can be collected and analyzed in real time, equipment failures can be responded to quickly, refueling processes can be optimized, operating costs can be reduced, and a good guarantee mechanism is provided for data transmission of multi-site gas pumps.

[0003] The prior art has the following deficiencies:

[0004] At present, in the actual process of secure encryption transmission of refueling machine data, the user's security priority and performance priority may change dynamically, resulting in frequent changes in priority. The selection of encryption algorithms may not be able to adapt to real-time needs, reducing data security and transmission efficiency, and causing a waste of computing resources. Therefore, a refueling machine data secure encryption transmission management system is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a fuel dispenser data security encryption transmission management system, which solves the problems raised in the above-mentioned background technology by using different product inspection methods.

[0007] To achieve the above object, the present invention provides the following technical solution: a fuel dispenser data security encryption transmission management system, comprising a data acquisition module, a user classification module, a user matching module and a secondary division module; signal connections between the modules;

[0008] The data collection module is used to collect user behavior feature information, and through data processing, obtain the user's vehicle tank capacity and user interaction frequency, and send it to the user classification module;

[0009] The user classification module is used to receive the fuel tank capacity of the user's vehicle and the user interaction frequency, establish a data analysis model, obtain the user evaluation coefficient, and compare it with the preset user threshold to determine the user classification result. According to the user classification result, the average value of the fuel dispenser transmission data volume, the average value of the transmission rate, the probability of data security transmission, and the similarity between the transmission data and the original data are collected and sent to the user matching module;

[0010] The user matching module is used to receive the average value of the amount of data transmitted by the fuel dispenser, the average value of the transmission rate, the probability of data security transmission, and the similarity between the transmitted data and the original data, set the fuel dispenser simple algorithm and the fuel dispenser standard algorithm, determine the security algorithm matched by different types of users, and send it to the secondary division module;

[0011] The secondary division module is used to receive security algorithms matched by different types of users, obtain the number of standard users and the expected upload time, and perform weighted calculation to obtain the standard user coefficient, which is compared with the preset transmission threshold to determine the delay result. The user allocation prediction analysis is performed through the pre-built time series model to further refine and optimize the standard user allocation plan.

[0012] In a preferred embodiment, the user behavior characteristic information includes the fuel tank capacity of the user's vehicle and the user interaction frequency;

[0013] Count the refueling records associated with the user ID, and add the historical refueling amount to the current fuel volume change to obtain the fuel tank capacity of the user's vehicle;

[0014] The user interaction frequency is calculated by calculating the ratio of the number of interaction events generated by the user in a unit time and the corresponding unit time length.

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

[0016] After obtaining the user evaluation coefficient, compare and analyze the user evaluation coefficient with the continuously iterated user threshold;

[0017] 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;

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

[0019] According to the marking results, classification results of the users are divided, specifically, classification results marked as standard users and simple users.

[0020] In a preferred embodiment, according to the user classification result, the average data volume and transmission rate of the fuel dispenser corresponding to the simple user are collected; and the data security transmission probability and the similarity between the transmitted 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, and the instantaneous data transmission volume is collected multiple times and accumulated to obtain the total data transmission volume per unit time, and the ratio is calculated with the unit time length to obtain the average data transmission volume of the tanker;

[0022] In a unit time, the instantaneous transmission rate is collected regularly through network monitoring, and the ratio is calculated with the unit time length to obtain the average transmission rate;

[0023] By recording the status of each transmission, counting the number of secure transmissions per unit time, and calculating the ratio with the total number of transmissions, the probability of secure data transmission is obtained;

[0024] The content of the transmitted 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 transmitted data and the original data.

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

[0026] Use the Robust Scaling formula to calculate the fuel dispenser transmission coefficient: Where y is the transmission coefficient of the tanker, x is the average amount of data transmitted by the tanker, is the median value of the amount of data transmitted by the tanker, x iqr The difference between the 1 / 4 quantile and the 3 / 4 quantile when the mean value of the amount of data transmitted by the fuel dispenser is arranged from small to large;

[0027] The transmission rate coefficient is calculated using the same Robust Scaling formula, and the simple discrimination coefficient is obtained by summing the fuel dispenser transmission coefficient and the transmission rate coefficient;

[0028] The simple discrimination coefficient is compared and analyzed with the preset simple threshold. If the simple discrimination coefficient is greater than or equal to the simple threshold, the current user's data transmission is transmitted using a fast verification security algorithm. If the simple discrimination coefficient is less than the simple threshold, the current user's data transmission is transmitted using a conventional security algorithm.

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

[0030] Construct a linear combination to calculate the standard discriminant coefficient, and the calculation formula is:

[0031] z=w1·a+w2·b

[0032] In the formula, z is the standard discriminant coefficient, a is the probability of data safe transmission, b is the similarity between the transmitted data and the original data, w1 is the optimal weight of a, and w2 is the optimal weight of b;

[0033] The optimal weights are obtained through the lasso regression formula;

[0034] The standard discrimination coefficient is compared and analyzed with the preset standard threshold. If the standard discrimination coefficient is greater than or equal to the standard threshold, the current user's data transmission is transmitted using the standard security algorithm. If the standard discrimination coefficient is less than the standard threshold, the current user's data transmission is transmitted using the conventional security algorithm.

[0035] In a preferred embodiment, standard users are classified from all current online users, and the number of standard users is obtained by counting the number of users;

[0036] The estimated upload time is calculated by calculating the ratio of the data uploaded by all standard users to the network bandwidth and adding the total value of the encryption and decryption complexity related to the security algorithm.

[0037] The number of standard users and the expected upload time are weighted 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, a delay result is generated.

[0038] In a preferred embodiment, the delay result is obtained, and a time series model is used to perform user allocation prediction analysis. The time series model adopts an ARIMAX model. The specific steps of performing user allocation prediction analysis using 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, verify the effect of the fitting model and check the goodness of fit of the model by residual analysis;

[0043] Step A5: Use the fitted model to predict the future user allocation plan, and use 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 probability of data safe transmission and the similarity between the transmitted data and the original data. The ARIMAX model formula is:

[0045]

[0046] In the formula, y t is the transmission delay per unit time, α is a constant term, is the i-th order autoregressive parameter, p is the order of the autoregressive term, θ j is the jth order moving average parameter, q is the order of the moving average term, ∈ t is a white noise term, representing random error, β k is an exogenous variable X t-k The coefficient of m is the lag order of the exogenous variable, X t-k is an exogenous variable lagged by k periods.

[0047] In a preferred embodiment, in step A3, α, θ1, β1, and β2 are calculated using the maximum likelihood estimation method.

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

[0049] 1. The present invention classifies users according to the fuel tank capacity of their vehicles and the frequency of user interaction, and collects the average amount of data transmitted by the fuel dispenser, the average transmission rate, the probability of data security transmission, and the similarity between the transmitted data and the original data according to different categories of users, sets a simple fuel dispenser algorithm and a standard fuel dispenser algorithm, determines the security algorithms matched by different types of users, improves the efficiency of data transmission, enhances the security of data transmission, reduces computing and resource consumption, and enhances user experience.

[0050] 2. The present invention receives security algorithms matched by different types of users, obtains the number of standard users and the expected upload time, and obtains the standard user coefficient by weighted calculation, compares it with the preset transmission threshold, determines the delay result, and performs user allocation prediction analysis through a time series model. According to the maximum and average values ​​of the prediction results, the standard user allocation is dynamically adjusted to reduce peak delays, set precise delay management, and ensure precise control of transmission time. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention is a schematic diagram of a module of a fuel dispenser data security encryption transmission management system. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Example 1

[0054] The present invention discloses a wireless security system based on the Internet of Things, such as Figure 1 As shown, it includes a data acquisition module, a user classification module, a user matching module and a secondary division module; the modules are connected by signals;

[0055] The data collection module is used to collect user behavior feature information, and through data processing, obtain the user's vehicle tank capacity and user interaction frequency, and send it to the user classification module;

[0056] Among them, user behavior characteristic information includes the fuel tank capacity of the user's vehicle and the user's interaction frequency;

[0057] Data processing refers to the standardization and quantification of the original data of user behavior feature information. Specifically, data normalization and standardization is to map the characteristics of the user's vehicle tank capacity and the user's interaction frequency between 0 and 1, and then adjust the data to a distribution with zero mean and unit standard deviation, etc. The specific standardization and normalization operation steps are existing technologies and will not be described in detail here.

[0058] The fuel tank capacity of a user's vehicle refers to the maximum amount of fuel that the vehicle's fuel tank can hold. The acquisition logic is to obtain the fuel tank capacity of the user's vehicle by counting the refueling records associated with the user ID and adding the historical refueling amount to the current fuel volume change;

[0059] Specifically, the user identification refers to an identification that can identify the user's characteristics, including but not limited to a license plate number, a membership card number, or a refueling account number, which will not be elaborated here;

[0060] The current oil level change refers to the change in the oil level in the user's vehicle tank. The oil level in the tank is monitored in real time by an oil level sensor, usually a float sensor or a capacitive sensor, and the current oil level change is transmitted to the fuel dispenser.

[0061] User interaction frequency refers to the frequency of user interaction with the gas station system in a unit time. The acquisition logic is to calculate the user interaction frequency by calculating the ratio of the number of interaction events generated by the user in a unit time and the corresponding unit time length.

[0062] The interactive operations include refueling operations, QR code interactions, and other service requests. The interactive operations are limited to the above operations and will not be described in detail 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 experimenter according to the specific fuel dispenser operation time and the fuel replenishment interval, and is not limited here;

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

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

[0066] Obtain the fuel tank capacity of the user's vehicle and the user's interaction frequency, and obtain the user evaluation coefficient through weighted calculation;

[0067] The logic of obtaining the user threshold is to collect historical user classification sets, divide the data set into training sets and test sets, set evaluation indicators and clustering algorithms, train the model on the training set and evaluate the model performance on the test set in each round of cross-validation, and then adjust the user threshold according to the performance of the validation set. Therefore, the user threshold is constantly updated;

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

[0069] After obtaining the user evaluation coefficient, compare and analyze the user evaluation coefficient with the continuously iterated user threshold;

[0070] 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;

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

[0072] Specifically, users marked as standard users need a computational security algorithm with higher security performance, whereas users marked as simple users need a computational security algorithm with simple and convenient performance;

[0073] The selection of a specific security algorithm can be determined based on the experimental results of the experimenter or the output results of the specific algorithm experiment, which will not be elaborated here;

[0074] According to the marking results, the classification results of the users are divided, specifically, the classification results marked as standard users and simple users;

[0075] According to the user classification results, the average data volume and transmission rate of the fuel dispenser corresponding to the simple user will be collected; and the data security transmission probability and the similarity between the transmitted data and the original data corresponding to the standard user will be collected;

[0076] The average data volume of the tanker transmission refers to the amount of data transmitted by the tanker through the network per unit time. Its acquisition logic is to record the data transmission volume in real time through the transmission interface, collect multiple instantaneous transmission volumes and accumulate the total transmission volume per unit time and calculate the ratio of the unit time length to obtain the average data volume of the tanker transmission.

[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 upload, download and interact with the fuel dispenser data. Specifically, the transmission interface defines the format, encoding 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 experimenter according to the specific data characteristics, and will not be elaborated here;

[0078] The logic for obtaining the average transmission rate is to collect the instantaneous transmission rate through network monitoring in a unit time, and calculate the ratio with the unit time length to obtain the average transmission rate;

[0079] The probability of data secure transmission refers to the probability that data transmission is successfully encrypted and not destroyed or tampered with in a unit time. The acquisition logic is to record the status of each transmission, count the number of secure transmissions in a unit time, and calculate the ratio with the total number of transmissions to obtain the probability of data secure transmission;

[0080] Specifically, the definition of secure transmission is clear, which means that the data is not intercepted or tampered with during the transmission process, the encryption algorithm works normally, the decrypted data is consistent with the original data, and the data is not attacked by the network during the transmission process.

[0081] The similarity between the transmitted 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, so as to determine the integrity, accuracy and fidelity of the data during the transmission process. Its acquisition logic is based on the similarity of the content of the transmitted data. The content of the transmitted data and the content of the original data are converted into vectors, which are substituted into the cosine similarity calculation to obtain the similarity between the transmitted data and the original data.

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

[0083]

[0084] In the formula, CS(A,B) is the similarity between the transmitted data and the original data, A and B represent the vector of the original data and the vector of the transmitted data respectively, A i and B i They represent the value of the i-th element in the original data and the transmitted data vectors, respectively, 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 value of the amount of data transmitted by the fuel dispenser, the average value of the transmission rate, the probability of data safe transmission, and the similarity between the transmitted data and the original data are all average values, probabilities, and similarities obtained during a set of data transmission. During the transmission process, multiple sets of data are usually included. The number of specific data sets is not limited, but is obtained by different data sets set by the experimenter first, which will not be elaborated here.

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

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

[0088] Use the Robust Scaling formula to calculate the fuel dispenser transmission coefficient: Where y is the transmission coefficient of the tanker, x is the average amount of data transmitted by the tanker, is the median value of the amount of data transmitted by the tanker, x iqr The difference between the 1 / 4 quantile and the 3 / 4 quantile when the mean value of the amount of data transmitted by the fuel dispenser is arranged from small to large;

[0089] The simple discrimination coefficient is obtained by summing the fuel dispenser transmission coefficient and the transmission rate 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. In this example, by compressing the amount of data transmitted by the fuel dispenser and calculating the simple discrimination coefficient, the transmission mode and security strategy of the fuel dispenser can be dynamically adjusted to adapt to different user needs and network environments;

[0091] The simple discrimination coefficient is compared and analyzed with the preset simple threshold. If the simple discrimination coefficient is greater than or equal to the simple threshold, the data transmission of the current user is transmitted using the fast verification security algorithm. If the simple discrimination coefficient is less than the simple threshold, the data transmission of the current user is transmitted using the conventional security algorithm.

[0092] For the standard user comprehensive data security transmission probability and the similarity between the transmitted data and the original data, the standard algorithm of the refueling machine is used to obtain the standard discrimination coefficient. The specific steps are as follows:

[0093] Construct a linear combination to calculate the standard discriminant coefficient, and the calculation formula is:

[0094] z=w1·a+w2·b

[0095] In the formula, z is the standard discrimination coefficient, a is the probability of data safe transmission, b is the similarity between the transmitted data and the original data, w1 is the optimal weight of a, and w2 is the optimal weight of b. The probability of safe transmission of each group of data and the similarity between the transmitted data and the original data in one transmission process are summed and calculated. The calculation formula is: Z c =a c +b c , where Z c is the standard discriminant coefficient of group c, a c is the data security transmission probability of group c, b c The similarity between the cth group of transmitted data and the original data is obtained, and the standard discrimination coefficient of each group of data is obtained;

[0096] Furthermore, the lasso regression formula is used to calculate two optimal weights, namely the optimal weights w1 and w2. The specific formula is expressed as:

[0097]

[0098] In the formula, λ is the regularization parameter, m is the number of data groups in one transmission process, and when w1 and w2 use the lasso regression formula to minimize the calculation results, then w1 and w2 are the optimal weights of a and b respectively;

[0099] Compare and analyze the standard discrimination coefficient with the preset standard threshold. If it is greater than or equal to the standard threshold, the data transmission of the current user is transmitted using the standard security algorithm. If it is less than the standard threshold, 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 to ensure that the data is not leaked during transmission, and uses the hash algorithm SHA-256 to generate the hash value of the data, and attaches the hash value to the data transmission to verify the security of the transmission. The security protocols SSL and TLS are used to encrypt the data transmission channel, and multiple authentication is set. At the same time, the experimenters regularly set risk assessments to prevent the data from being stolen or tampered with during transmission;

[0101] For example, conventional security algorithms use symmetric encryption AES to encrypt data, use lightweight hash algorithm MD5 to verify the possibility of data tampering, and use lightweight encryption protocol HTTPS to encrypt data transmission channels;

[0102] For example: the fast verification 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 verification speed;

[0103] It should be noted that the settings of the standard security algorithm, the conventional security algorithm, and the fast verification security algorithm are set by the experimenters according to the experimental results of data transmission and are not limited here;

[0104] The present invention classifies users according to the fuel tank capacity of their vehicles and the frequency of user interaction, and collects the average value of the amount of data transmitted by the fuel dispenser, the average value of the transmission rate, the probability of data security transmission, and the similarity between the transmitted data and the original data according to different categories of users, sets a simple algorithm for fuel dispensers and a standard algorithm for fuel dispensers, determines the security algorithms matched by different types of users, improves the efficiency of data transmission, enhances the security of data transmission, reduces computing and resource consumption, and enhances user experience.

[0105] Example 2

[0106] In the first embodiment of the present invention, an example is given to classify users by the fuel tank capacity of their vehicles and the frequency of user interaction, and the average value of the amount of data transmitted by the fuel dispenser, the average value of the transmission rate, the probability of data security transmission, and the similarity between the transmission data and the original data are collected respectively according to different categories of users, and a simple fuel dispenser algorithm and a standard fuel dispenser algorithm are set to determine the operation strategy of the security algorithm matched by different types of users; however, in the first embodiment, different fuel dispenser algorithms are only used for different categories of users, and the standard fuel dispenser algorithm is selected by default for standard users, which further increases the operating cost of the system and reduces the user experience when there are many standard users; in view of the above problems, the second embodiment of the present invention is further refined;

[0107] The secondary division module is used to receive the security algorithms matched by different types of users, obtain the number of standard users and the expected upload time, and perform weighted calculation to obtain the standard user coefficient, compare it with the preset transmission threshold, determine the delay result, and perform user allocation prediction analysis through the pre-built time series model to further refine and optimize the standard user allocation plan;

[0108] The logic for obtaining the number of standard users is to classify the current online users into standard users and count the number of users to obtain the number of standard users;

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

[0110] The logic for obtaining the estimated upload time is to add the ratio of the uploaded data volume of 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 estimated upload time;

[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 described in detail here;

[0112] The number of standard users and the expected upload time are weighted to obtain the standard user coefficient, which is 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, otherwise no change is made;

[0113] The delay result expresses that the current standard user group has too many users, which leads to transmission delay;

[0114] It should be noted that the time series model of this embodiment adopts the ARIMAX model. Refining and optimizing the standard user allocation plan means that when the delay of the current standard user allocation plan is determined, further analysis and adjustment are performed to make the standard user allocation plan more scientific and reasonable, thereby achieving control of the transmission duration.

[0115] Furthermore, the specific steps of user allocation prediction analysis through 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, verify the effect of the fitting model and check the goodness of fit of the model by residual analysis;

[0120] Step A5, using the fitted model to predict the future allocation plan, 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 similarity between the transmitted data and the original data, and the transmission delay data; wherein, the data security transmission probability corresponding to each unit time and the similarity between the transmitted data and the original data have been exemplified in Example 1 and will not be described again here;

[0122] Transmission delay data refers to historical transmission delay data, whose data is used as the main variable of the time series;

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

[0124]

[0125] In the formula, y t is the transmission delay per unit time, α is a constant term, is the i-th order autoregressive parameter, p is the order of the autoregressive term, θ j is the jth order moving average parameter, q is the order of the moving average term, ∈ t is a white noise term, representing random error, β k is an exogenous variable X t-k The coefficient of m is the lag order of the exogenous variable, X t-k is an exogenous variable lagged by k periods;

[0126] It should be noted that the exogenous variable part can integrate the impact of other relevant variables (such as the probability of data safe transmission and the similarity between the transmitted data and the original data) on the transmission delay;

[0127] It should be noted that in step A3, α, θ1, β1, and β2 are calculated by the maximum likelihood estimation (MLE) method. The specific steps are as follows:

[0128] Error term ∈ t Subordinate to the normal distribution N(0,σ 2 (, then the likelihood function is:

[0129]

[0130] Taking the logarithm of the likelihood function gives the log-likelihood function:

[0131]

[0132] By maximizing the log-likelihood function, we can obtain the parameter estimates μ, θ1, β1, β2;

[0133] Dynamically adjust the standard user allocation strategy by predicting the maximum and average values ​​of the results;

[0134] This embodiment exemplifies as follows:

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

[0136] According to the average of the predicted results Formulate a balance period allocation strategy to maintain the current number of standard users and their user structure during the period when the transmission delay is close to the average value;

[0137] The peak period and the equilibrium period are set by the experimenters, and the boundaries of the division are not limited.

[0138] The present invention receives security algorithms matched by different types of users, obtains the number of standard users and the expected upload duration, and obtains the standard user coefficient by weighted calculation, compares it with the preset transmission threshold, determines the delay result, and performs user allocation prediction analysis through a time series model. According to the maximum and average values ​​of the prediction results, the standard user allocation is dynamically adjusted to reduce the peak delay, set accurate delay management, and ensure accurate control of the transmission duration.

[0139] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0140] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0141] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal 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 will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0143] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0144] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0147] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0148] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A fuel dispenser data security encryption transmission management system, characterized by: It includes data acquisition module, user classification module, user matching module and secondary division module; signal connection between each module; The data collection module is used to collect user behavior feature information, and through data processing, obtain the user's vehicle tank capacity and user interaction frequency, and send it to the user classification module; The user classification module is used to receive the fuel tank capacity of the user's vehicle and the user interaction frequency, establish a data analysis model, obtain the user evaluation coefficient, and compare it with the preset user threshold to determine the user classification result. According to the user classification result, the average value of the fuel dispenser transmission data volume, the average value of the transmission rate, the probability of data security transmission, and the similarity between the transmission data and the original data are collected and sent to the user matching module; The user matching module is used to receive the average value of the amount of data transmitted by the fuel dispenser, the average value of the transmission rate, the probability of data security transmission, and the similarity between the transmitted data and the original data, set the fuel dispenser simple algorithm and the fuel dispenser standard algorithm, determine the security algorithm matched by different types of users, and send it to the secondary division module; The secondary division module is used to receive security algorithms matched by different types of users, obtain the number of standard users and the expected upload time, and perform weighted calculation to obtain the standard user coefficient, which is compared with the preset transmission threshold to determine the delay result. The user allocation prediction analysis is performed through the pre-built time series model to further refine and optimize the standard user allocation plan.

2. A fuel dispenser data security encryption transmission management system according to claim 1, characterized in that: User behavior characteristic information includes the fuel tank capacity of the user's vehicle and the user's interaction frequency; Count the refueling records associated with the user ID, and add the historical refueling amount to the current fuel volume change to obtain the fuel tank capacity of the user's vehicle; The user interaction frequency is calculated by calculating the ratio of the number of interaction events generated by the user within a unit time and the corresponding unit time length.

3. A fuel dispenser data security encryption transmission management system according to claim 2, characterized in that: Obtain the fuel tank capacity of the user's vehicle and the user's interaction frequency, and obtain the user evaluation coefficient through weighted calculation; After obtaining the user evaluation coefficient, compare and analyze the user evaluation coefficient 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 simple user and a rate signal is generated; According to the marking results, classification results of the users are divided, specifically, classification results marked as standard users and simple users.

4. A fuel dispenser data security encryption transmission management system according to claim 3, characterized in that: According to the user classification results, the average data volume and transmission rate of the fuel dispenser corresponding to the simple user will be collected; And collect the data security transmission probability corresponding to the standard user and the similarity between the transmitted data and the original data; The data transmission volume is recorded in real time through the transmission interface, and the instantaneous data transmission volume is collected multiple times and accumulated to obtain the total data transmission volume per unit time, and the ratio is calculated with the unit time length to obtain the average data transmission volume of the tanker; In a unit time, the instantaneous transmission rate is collected regularly through network monitoring, and the ratio is calculated with the unit time length to obtain the average transmission rate; By recording the status of each transmission, counting the number of secure transmissions per unit time, and calculating the ratio with the total number of transmissions, the probability of secure data transmission is obtained; The content of the transmitted 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 transmitted data and the original data.

5. A fuel dispenser data security encryption transmission management system according to claim 4, characterized in that: For the simple user comprehensive fuel dispenser transmission data volume mean and transmission rate mean, use the fuel dispenser simple algorithm to obtain the simple discrimination coefficient. The specific steps are as follows: Use the Robust Scaling formula to calculate the fuel dispenser transmission coefficient: Where y is the transmission coefficient of the tanker, x is the average amount of data transmitted by the tanker, is the median value of the amount of data transmitted by the tanker, x iqr The difference between the 1 / 4 quantile and the 3 / 4 quantile when the mean value of the amount of data transmitted by the fuel dispenser is arranged from small to large; The transmission rate coefficient is calculated using the same Robust Scaling formula, and the simple discrimination coefficient is obtained by summing the fuel dispenser transmission coefficient and the transmission rate coefficient; The simple discrimination coefficient is compared and analyzed with the preset simple threshold. If the simple discrimination coefficient is greater than or equal to the simple threshold, the current user's data transmission is transmitted using a fast verification security algorithm. If the simple discrimination coefficient is less than the simple threshold, the current user's data transmission is transmitted using a conventional security algorithm.

6. A fuel dispenser data security encryption transmission management system according to claim 4, characterized in that: For the standard user comprehensive data security transmission probability and the similarity between the transmitted data and the original data, the standard algorithm of the refueling machine is used to obtain the standard discrimination coefficient. The specific steps are as follows: Construct a linear combination to calculate the standard discriminant coefficient, and the calculation formula is: z=w1·a+w2·b In the formula, z is the standard discriminant coefficient, a is the probability of data safe transmission, b is the similarity between the transmitted data and the original data, w1 is the optimal weight of a, and w2 is the optimal weight of b; The optimal weights are obtained through the lasso regression formula; The standard discrimination coefficient is compared and analyzed with the preset standard threshold. If the standard discrimination coefficient is greater than or equal to the standard threshold, the current user's data transmission is transmitted using the standard security algorithm. If the standard discrimination coefficient is less than the standard threshold, the current user's data transmission is transmitted using the conventional security algorithm.

7. A fuel dispenser data security encryption transmission management system according to claim 6, characterized in that: Classify standard users from all current online users and count their number to get the number of standard users; The estimated upload time is calculated by calculating the ratio of the data uploaded by all standard users to the network bandwidth and adding the total value of the encryption and decryption complexity related to the security algorithm. The number of standard users and the expected upload time are weighted 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, a delay result is generated.

8. A fuel dispenser data security encryption transmission management system according to claim 7, characterized in that: Obtain the delay results and use the time series model to perform user allocation prediction analysis. The time series model uses the ARIMAX model. The specific steps for performing user allocation prediction analysis using the time series model are as follows: Step A1, obtaining prediction data; Step A2, establishing an ARIMAX model; Step A3, using the maximum likelihood estimation (MLE) method to estimate the ARIMAX model parameters; Step A4, verify the effect of the fitting model and check the goodness of fit of the model by residual analysis; Step A5: Use the fitted model to predict the future user allocation plan, and use the maximum value of the prediction result as the maximum value of the transmission delay in the future unit time.

9. A fuel dispenser data security encryption transmission management system according to claim 8, characterized in that: The exogenous variables in the ARIMAX model include the probability of data security transmission and the similarity between the transmitted data and the original data. The ARIMAX model formula is: In the formula, y t is the transmission delay per unit time, α is a constant term, is the i-th order autoregressive parameter, p is the order of the autoregressive term, θ j is the jth order moving average parameter, q is the order of the moving average term, ∈ t is a white noise term, representing random error, β k is an exogenous variable X t-k The coefficient of m is the lag order of the exogenous variable, X t-k is an exogenous variable lagged by k periods.

10. A fuel dispenser data security encryption transmission management system according to claim 9, characterized in that: In step A3, α, θ1, β1, and β2 are calculated using the maximum likelihood estimation method.

Images