Automobile financial transaction data transmission method, system and equipment based on artificial intelligence

Abstract

The invention discloses an automobile financial transaction data transmission method based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: obtaining automobile financial transaction data and a transmission path, obtaining automobile financial transaction time and a data volume value, and obtaining an initial transmission interval; generating a data transmission sequence queue; generating a basic bit value according to the bit number and the highest bit of the data volume value; calculating a transmission resource proportion according to the basic bit value, and generating an actual transmission interval according to the transmission resource proportion; completing data transmission according to the plurality of actual transmission intervals and the data transmission sequence queue, and obtaining a transmission arrival time interval; and obtaining a time difference according to the actual transmission interval and the transmission arrival time interval, obtaining a path correction factor according to the time difference, and generating a new initial transmission interval according to the path correction factor and the initial transmission interval. The method has the advantages of sequential transmission, high transmission efficiency, good stability and dynamic correction and adjustment.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method, system, and device for transmitting data in automotive financial transactions based on artificial intelligence. Background Technology

[0002] With the rapid development of the automotive industry and the deep integration of financial technology, the auto finance market is experiencing unprecedented growth. In this process, the transmission of auto finance transaction data plays a crucial role. However, as the volume of transaction data continues to increase, the challenges of data transmission are becoming increasingly severe.

[0003] In existing data transmission methods, transmitting large amounts of transaction data often takes longer. If the transmission interval is not set properly, it can easily lead to network congestion, thus affecting the real-time performance and reliability of data transmission. This is especially true during peak periods of auto finance transactions, when a large amount of data floods the transmission network simultaneously, placing extremely high demands on the network's capacity. At the same time, processing large amounts of data also brings a huge computational burden. In traditional data transmission methods, complex calculations and verifications are often required to ensure the accuracy and integrity of the data. This not only consumes a lot of computing resources but also increases the latency of data transmission. More importantly, the capacity of the same transmission path can vary significantly at different times, under different hardware performance conditions, or under different load conditions. Simply applying the logic that the data transmission interval should be the same for the same transmission path is difficult to adapt to the needs of the actual transmission environment and can easily lead to network congestion or other abnormal situations. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method, system, and device for transmitting data in automotive finance transactions based on artificial intelligence.

[0005] An AI-based method for transmitting data in automotive finance transactions includes: acquiring multiple automotive finance transaction data and transmission paths; obtaining corresponding automotive finance transaction times and data volume values ​​based on the automotive finance transaction data; obtaining an initial transmission interval corresponding to the transmission path; sequentially arranging multiple automotive finance transaction times and generating a data transmission sequence queue; generating base bit values ​​based on the number of bits and the highest bit of the data volume value, thereby obtaining multiple base bit values; calculating the transmission resource ratio of each automotive finance transaction data based on the multiple base bit values, and generating an actual transmission interval corresponding to the automotive finance transaction data based on the transmission resource ratio and the initial transmission interval; sequentially completing data transmission according to the multiple actual transmission intervals and the data transmission sequence queue, obtaining the transmission arrival time interval of multiple automotive finance transaction data; obtaining a time difference based on the actual transmission interval and the corresponding transmission arrival time interval, obtaining a path correction factor based on the multiple time differences, and generating a new initial transmission interval based on the path correction factor and the initial transmission interval, wherein the new initial transmission interval is applied to the transmission process of multiple automotive finance transaction data in a new round on the transmission path.

[0006] Preferably, generating the base value based on the number of digits and the highest digit of the data value includes: generating a first string n1 based on the number of digits of the data value; generating a second string n2 based on the highest digit of the data value; concatenating the first string and the second string to generate the base value B, where B = n1‖n2.

[0007] Preferably, obtaining the path correction factor based on multiple time differences includes: obtaining a stability index based on multiple time differences; obtaining multiple time differences that are less than the stability index and treating them as multiple differences to be processed; averaging the multiple differences to be processed and generating a path correction factor based on the average value.

[0008] Preferably, the stability index obtained based on multiple time differences is expressed as follows: Where W is the stability index, Δt max The maximum value among multiple time differences, where α is a preset reduction factor.

[0009] Preferably, the path correction factor generated by averaging multiple unprocessed differences is expressed as follows: Where M is the path correction factor, Δt i Let be the i-th difference to be processed, m be the number of differences to be processed, and β be the transmission environment delay coefficient.

[0010] Preferably, the proportion of transmission resources for each auto finance transaction data is calculated based on multiple basic bit values ​​and expressed as follows: Among them, R j B represents the proportion of transmission resources for the j-th auto finance transaction data. jLet B be the base bit value of the j-th auto finance transaction data. k Let be the base value of the k-th auto finance transaction data, and n be the number of auto finance transaction data.

[0011] Preferably, the actual transmission interval corresponding to the auto finance transaction data is generated based on the proportion of transmission resources and the initial transmission interval, and is expressed as: T j =R j ·t c Among them, T j t represents the actual transmission interval between the j-th and j+1-th auto finance transaction data. c This is the initial transmission interval.

[0012] A data transmission system for auto finance transactions based on artificial intelligence is also provided. This system implements a data transmission method for auto finance transactions based on artificial intelligence. The system includes: a data and path acquisition module, used to acquire multiple auto finance transaction data and transmission paths, and to obtain the corresponding auto finance transaction time and data volume value based on the auto finance transaction data, and to obtain the initial transmission interval corresponding to the transmission path; a queue generation module, used to arrange multiple auto finance transaction times in chronological order and generate a data transmission sequence queue; a data processing module, used to generate basic bit values ​​based on the number of bits and the highest bit of the data volume value, thereby obtaining multiple basic bit values; and a calculation module, used to calculate various... The system allocates transmission resources for each auto finance transaction data point and generates an actual transmission interval corresponding to the auto finance transaction data based on the transmission resource allocation and the initial transmission interval. A transmission module is used to sequentially transmit data according to multiple actual transmission intervals and a data transmission order queue, and to obtain the transmission arrival time interval for multiple auto finance transaction data points. A transmission feedback and update module is used to obtain the time difference based on the actual transmission interval and the corresponding transmission arrival time interval, obtain a path correction factor based on multiple time differences, and generate a new initial transmission interval based on the path correction factor and the initial transmission interval. This new initial transmission interval is applied to the transmission process of multiple auto finance transaction data points in a new round on the transmission path.

[0013] An electronic device is also provided, comprising: a memory storing a computer program thereon; and a processor for executing the computer program in the memory to implement the above-described artificial intelligence-based automotive financial transaction data transmission method.

[0014] A non-transitory computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the aforementioned AI-based automotive financial transaction data transmission method.

[0015] The beneficial effects of this invention are reflected in:

[0016] The entire AI-based auto finance transaction data transmission method first establishes an orderly data transmission system by accurately acquiring the transaction time, data volume, and initial transmission interval of each auto finance transaction data point. This ensures accurate data transmission in chronological order, improving data transmission reliability and real-time performance. Furthermore, by introducing the concept of a basic bit value and generating it based on the number of bits and the highest bit of the data volume, the method simplifies the processing of complex data volumes, improves computational efficiency, and reduces the system's computational burden. Further, by calculating the transmission resource allocation based on the basic bit value, the method optimizes the allocation of transmission resources, ensuring that large-volume transaction data receives sufficient transmission bandwidth and time, effectively avoiding network congestion and data transmission delays. Finally, by dynamically adjusting the actual transmission interval, the method considers the changing capacity of the transmission path under different times, hardware performance variations, or load conditions, making data transmission more flexible and adaptable to the needs of the actual transmission environment. Specifically, by calculating the time difference and path correction factor, the method can adjust the initial transmission interval in real time, continuously iterating and optimizing the transmission strategy, further improving transmission efficiency and accuracy. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 This is a schematic diagram illustrating the steps of the artificial intelligence-based automotive finance transaction data transmission method of the present invention;

[0019] Figure 2 This is a schematic diagram of step S3 in the AI-based automotive finance transaction data transmission method of the present invention;

[0020] Figure 3 This is a schematic diagram of part of step S6 in the AI-based automotive finance transaction data transmission method of the present invention;

[0021] Figure 4 This is a block diagram illustrating an electronic device according to an embodiment of the present invention.

[0022] Figure label:

[0023] 700 - Electronic device; 701 - Processor; 702 - Memory; 703 - Multimedia component; 704 - Input / output (I / O) interface; 705 - Communication component. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] like Figure 1 As shown, an artificial intelligence-based method for transmitting data in automotive finance transactions is provided, including:

[0028] S1. Obtain multiple auto finance transaction data and transmission paths, and obtain the corresponding auto finance transaction time and data volume value based on the auto finance transaction data, and obtain the initial transmission interval corresponding to the transmission path;

[0029] S2. Arrange multiple auto finance transaction times in chronological order and generate a data transmission sequence queue;

[0030] S3. Generate basic bit values ​​based on the number of bits and the highest bit of the data value, thereby obtaining multiple basic bit values;

[0031] S4. Calculate the transmission resource ratio of each auto finance transaction data based on multiple basic bit values, and generate the actual transmission interval corresponding to the auto finance transaction data based on the transmission resource ratio and the initial transmission interval.

[0032] S5. Data transmission is completed sequentially according to multiple actual transmission intervals and data transmission order queues to obtain the transmission arrival time interval of multiple auto finance transaction data.

[0033] S6. Obtain the time difference based on the actual transmission interval and the corresponding transmission arrival time interval, obtain the path correction factor based on multiple time differences, and generate a new initial transmission interval based on the path correction factor and the initial transmission interval. The new initial transmission interval is applied to the transmission process of multiple auto finance transaction data in a new round on the transmission path.

[0034] In this embodiment, it should be noted that in S1, multiple automotive finance transaction data to be transmitted are first collected. These data may include different types of transaction records, such as loan applications, insurance purchases, and vehicle sales. Each data item is accompanied by a specific transaction time. For each transaction data, the transaction time and data volume are recorded. The transaction time is used to determine the order of data transmission, while the data volume is used to calculate the transmission resource requirements later. Multiple transaction data will use the same transmission path, and the information of this transmission path needs to be identified and recorded. Finally, the initial transmission interval corresponding to the selected transmission path is obtained. This initial transmission interval is obtained based on the historical transmission information of the transmission path, such as the situation of the previous batch of data transmission (i.e., the new initial transmission interval output by S6 in the previous batch of transmission). If the transmission path has no historical transmission information, it can be manually set based on factors such as the hardware performance, bandwidth limitations, and maximum data throughput of the path. Both the automotive finance transaction data and the transmission path can be intelligently acquired and processed based on artificial intelligence, efficiently acquiring automotive finance transaction data and transmission path information from multiple sources. For example, data can be collected in real time or periodically using web crawling technology or API interfaces.

[0035] In S2, the transaction data is sorted according to the chronological order of the transactions, and a data transmission order queue is generated. Specifically, each transaction is first sorted according to its transaction time, arranged from earliest to latest. The earliest generated transaction data is placed at the front of the queue, and the latest generated transaction data is placed at the back. After sorting, these transaction data are placed sequentially into the data transmission order queue. This queue serves as the basis for data transmission in subsequent steps, ensuring that data is transmitted in the correct order. This ensures that data is transmitted in the correct order and at the correct time intervals, thus avoiding data corruption and transmission errors.

[0036] In S3, a corresponding base bit value is generated based on the data volume of each transaction. These base bit values ​​are used to subsequently calculate the transmission resource ratio and the actual transmission interval. Specifically, first, the number of bits in the data volume of each transaction is determined. Generally, the data volume is a binary or decimal number, which is achieved by calculating its number of bits. For example, a transaction with a data volume of 546439 bits has 6 bits. Next, the highest bit of each data volume is extracted. The highest bit usually represents the size range of the data volume and is important for subsequent calculations of the transmission resource ratio. For example, the highest bit of 546439-bit transaction data is 5, while in a binary number, the highest bit is 1 (assuming the data volume is converted to a sufficiently long binary number). Finally, based on the number of bits and the highest bit of the data volume, a base bit value corresponding to the data volume is generated. This base bit value can be a number concatenated from a string. For example, the base bit value of 546439-bit transaction data is 65. This greatly simplifies the calculation objects, making subsequent calculations and comparisons more intuitive and efficient. By repeating the above steps for all transaction data, the basic bit value corresponding to each transaction data is obtained. These basic bit values ​​will be compiled into a list or array for subsequent calculations and processing.

[0037] In summary, the base bit value is usually closely related to the size of the data. By extracting the highest bit and considering the number of bits, the base bit value can represent the range of the data size. This helps to better allocate transmission resources in subsequent processing, ensuring that transaction data with large data sizes receives sufficient transmission bandwidth and time. Furthermore, by generating base bit values, we can avoid directly using complex data sizes in subsequent calculations, thereby improving computational efficiency and reducing computational burden.

[0038] In S4, the sum of the base bit values ​​of all transaction data is first calculated to obtain a total value. Then, for each transaction data, its base bit value is divided by the total value to obtain the transmission resource share of that transaction data. This share reflects the proportion of the transaction data's demand in the overall transmission resources. Further, based on the transmission resource share of each transaction data and the initial transmission interval, the actual transmission interval corresponding to that transaction data is calculated. This is usually achieved by multiplying the initial transmission interval by the data transmission quantity minus 1, and then multiplying by the transmission resource share. However, the specific calculation method may vary depending on the actual needs.

[0039] In summary, by calculating the sum of the basic bit values ​​of each transaction data to obtain the total value, and further calculating the transmission resource proportion of each transaction data, the optimal allocation of transmission resources is achieved. This ensures that transmission resources are allocated according to the actual needs of the transaction data. Furthermore, based on the transmission resource proportion of each transaction data and the initial transmission interval, the actual transmission interval corresponding to that transaction data is dynamically adjusted. This helps to maintain overall transmission efficiency while minimizing transmission delays and congestion, ensuring that transaction data can be transmitted to the target node in a timely and accurate manner.

[0040] In S5, each auto finance transaction in the data transmission sequence queue corresponds to a previously calculated actual transmission interval. First, each transaction is processed sequentially according to the data transmission sequence queue. For each auto finance transaction in the queue, the start and end times of data transmission are arranged according to its actual transmission interval. After each auto finance transaction data transmission is completed, its arrival time needs to be recorded. By comparing the arrival times of adjacent auto finance transaction data, their arrival time intervals can be calculated. These arrival time intervals are of great significance for analyzing data transmission performance and optimizing resource allocation.

[0041] In S6, the time difference is first calculated based on the discrepancy between the actual transmission interval and the corresponding arrival time interval. This difference reflects the deviation between the actual transmission process and the expected transmission plan, which may be caused by various factors such as network latency and changes in processing speed. Subsequently, based on these time differences, a path correction factor is derived using a specific algorithm or model. This path correction factor is a quantitative representation of potential delays or acceleration effects on the transmission path, helping to more accurately understand and predict time variations in future transmission processes. Finally, the initial transmission interval is adjusted using the path correction factor to generate a new initial transmission interval. This new interval will be applied to the transmission of multiple automotive finance transaction data in a new round on the transmission path, aiming to achieve more efficient and accurate transmission control. In summary, by calculating the time difference and obtaining the path correction factor accordingly, the transmission strategy can be dynamically adjusted to cope with various uncertainties that may occur on the transmission path. This not only helps reduce transmission delays caused by network fluctuations and changes in processing speed but also ensures a smoother and more reliable transmission process. Furthermore, by continuously iterating and optimizing the initial transmission interval, the optimal transmission scheme can be gradually approximated, thereby further improving transmission efficiency and accuracy.

[0042] In summary, the entire AI-based auto finance transaction data transmission method first establishes an orderly data transmission system by accurately acquiring the transaction time, data volume, and initial transmission interval of each auto finance transaction data point. This ensures accurate data transmission in chronological order, improving data transmission reliability and real-time performance. Furthermore, by introducing the concept of a basic bit value and generating it based on the number of bits and the highest bit of the data volume, the method simplifies the processing of complex data volumes, improves computational efficiency, and reduces computational burden. Moreover, the transmission resource allocation based on the basic bit value optimizes resource allocation, ensuring that large-volume transaction data receives sufficient bandwidth and time, effectively avoiding network congestion and data transmission delays. Finally, by dynamically adjusting the actual transmission interval, considering the varying carrying capacity of the transmission path under different times, hardware performance changes, or load conditions, the method makes data transmission more flexible and adaptable to the needs of the actual transmission environment. Specifically, by calculating the time difference and path correction factor, the method can adjust the initial transmission interval in real time, continuously iterating and optimizing the transmission strategy, further improving transmission efficiency and accuracy.

[0043] like Figure 2 As shown, in one embodiment, generating the base bit value based on the number of bits and the most significant bit in S3 includes:

[0044] S31. Generate the first string n1 based on the number of digits in the data value;

[0045] S32. Generate the second string n2 based on the highest bit of the data value;

[0046] S33. Concatenate the first string and the second string to generate the basic bit value B, where B = n1 || n2.

[0047] In this embodiment, it should be noted that in S31, a first string n1 is generated based on the number of bits in the data value. First, the number of bits in the data value of each auto finance transaction is determined. Once the number of bits is determined, this number of bits is used as a string, i.e., the first string n1. For example, if the data value is 546439 bits and its number of bits is 6, then the first string n1 is "6".

[0048] In step S32, the second string n2 is generated based on the most significant bit of the data value. This involves extracting the most significant bit of the data value. In decimal numbers, this typically means finding the largest digit; in binary numbers, the most significant bit is usually 1. The extracted most significant bit is then converted into a string, which is the second string n2. For example, for 546439, the most significant bit is 5, so the second string n2 is "5".

[0049] In S33, n1 and n2 are concatenated to generate the basic bit value B. Specifically, the first string n1 and n2 are concatenated to form a new string. The concatenation method can be a simple string join, such as B = n1‖n t The concatenated string B serves as the base value for the transaction volume data, used for subsequent calculations and processing.

[0050] In summary, by converting the number of bits and the highest bit of the data value into a string and concatenating them into a base bit value, we can avoid directly using complex data values ​​in subsequent calculations. This simplifies the objects of calculation, making subsequent calculations and comparisons more intuitive and efficient, while reducing the computational burden and improving data processing efficiency. Furthermore, the base bit value is usually closely related to the size of the data value, which helps to better allocate transmission resources in subsequent processing. Moreover, this method is applicable to data values ​​of different sizes and types, and has strong versatility and adaptability. Regardless of the size of the data value, this method can generate the corresponding base bit value for subsequent calculations and processing.

[0051] like Figure 3 As shown, in one embodiment, obtaining the path correction factor based on multiple time differences in S6 includes:

[0052] S61. Obtain stability indicators based on multiple time differences;

[0053] S62. Obtain multiple time differences that are less than the stability index and treat them as multiple differences to be processed.

[0054] S63. Take the average of multiple unprocessed differences and generate a path correction factor based on the average value.

[0055] In this embodiment, it should be noted that in S61, a stability index is obtained based on multiple time differences. Specifically, firstly, multiple time differences need to be determined and calculated. These time differences represent the time difference between the actual transmission interval and the real transmission time for different auto finance transaction data. Different real transmission processes are affected by different network delays or response times, so the actual transmission interval and the real transmission time are generally not completely consistent. Furthermore, these time differences are used to calculate a stability index, which can be a measurement threshold calculated based on the time differences.

[0056] In step S62, multiple time differences less than the stability index are obtained and treated as multiple unprocessed differences. Specifically, in this step, based on the previously calculated stability index, all time differences less than the index are filtered out. These time differences are considered relatively stable or have small fluctuations, and therefore are more likely to represent normal path conditions. The filtered time differences will be treated as multiple unprocessed differences for subsequent calculations and processing.

[0057] In step S63, the average value of multiple pending differences is taken, and a path correction factor is generated based on this average value. Specifically, in this step, the average value of all pending differences is taken. This average value represents the central trend of these relatively stable time differences and can therefore serve as a reference point or benchmark value to generate the path correction factor. Finally, the path correction factor is generated based on this average value. The path correction factor is a parameter used to update the initial transmission interval iteratively.

[0058] In summary, steps S61, S62, and S63 accurately identify relatively stable transmission conditions, helping to eliminate abnormal transmission situations caused by unstable factors such as network latency and response time. By selecting time differences smaller than the stability index as the differences to be processed, a more representative set of time difference data can be obtained. This data is more likely to reflect normal path conditions, thus providing an accurate basis for subsequent calculations and processing. Furthermore, the path correction factor can be used to update the initial transmission interval, thereby optimizing the data transmission path, improving transmission efficiency, or reducing transmission delays caused by unstable factors in the next batch of data transmission.

[0059] In one implementation, the stability index obtained in S61 based on multiple time differences is expressed as follows:

[0060] in,

[0061] W is the stability index, Δt max The maximum value among multiple time differences, where α is a preset reduction factor.

[0062] In this embodiment, it should be noted that Δt max This represents the maximum value among multiple time differences, reflecting the maximum deviation within the fluctuation range of the time difference. By using it as the numerator, we can ensure that the stability index W can sensitively capture large fluctuations in the time difference. Furthermore, α is a preset reduction factor, its function being to adjust Δt... maxScaling is performed to obtain a more suitable stability index W. By adjusting the value of α, the sensitivity and threshold range of the stability index can be flexibly controlled. The value of α takes into account multiple conditions such as transmission characteristics, network latency, and response time. Generally speaking, the value of α also needs to consider that S62 can filter out sufficient time difference to meet specific application requirements.

[0063] In summary, by introducing Δt max The addition of α allows for a more accurate assessment of stability. When the time difference fluctuates significantly, the value of W will increase accordingly, triggering subsequent stability handling measures. Furthermore, the introduction of α allows for adjustments based on different application scenarios and requirements. By modifying the value of α, the threshold range of the stability index can be flexibly changed to adapt to different transmission conditions and stability requirements. In practical applications, the value of α can be gradually adjusted according to the actual transmission situation and stability needs to achieve optimal stability and performance.

[0064] In one implementation, the average of multiple unprocessed differences and the generation of a path correction factor in S63 are expressed as follows:

[0065] in,

[0066] M is the path correction factor, Δt i Let be the i-th difference to be processed, m be the number of differences to be processed, and β be the transmission environment delay coefficient.

[0067] In this embodiment, it should be noted that, This indicates that the summation is performed on all m unprocessed differences. This setting ensures that all relevant difference data are considered when calculating the path correction factor, avoiding omissions or biases. Then, by dividing the summation result by m (the number of unprocessed differences), the average value of these differences can be obtained. The average value reflects the central trend of the overall data, helping to eliminate the influence of individual extreme values ​​and making the path correction factor more stable and reliable. Furthermore, β in the formula represents the transmission environment delay coefficient, which considers the impact of the transmission environment on the path correction factor. β is mainly a coefficient set separately due to network latency. Adding β to the average difference comprehensively considers the contribution of the transmission environment and difference data to path correction, making the result more accurate and comprehensive. Finally, in S6, the path correction factor is added to the initial transmission interval, and the sum of the two is obtained, which is the new initial transmission interval.

[0068] In summary, by considering all the differences to be processed and calculating their average, the impact of fluctuations in individual data points on the final result can be reduced, thereby improving the accuracy of path correction. Furthermore, the use of the average helps to eliminate noise and outliers in the data, and contributes to better robustness to small fluctuations and unstable factors in data transmission. In addition, the introduction of the transmission environment delay coefficient β can make the path correction factor more consistent with the actual transmission environment characteristics, which helps to achieve more accurate path correction under different transmission conditions.

[0069] In one implementation, the proportion of transmission resources for each auto finance transaction data calculated based on multiple basic bit values ​​in S4 is expressed as follows:

[0070] in,

[0071] R j B represents the proportion of transmission resources for the j-th auto finance transaction data. j Let B be the base bit value of the j-th auto finance transaction data. k Let be the base value of the k-th auto finance transaction data, and n be the number of auto finance transaction data.

[0072] In this embodiment, it should be noted that R j This represents the proportion of transmission resources required for the j-th auto finance transaction data. It is a value between 0 and 1, reflecting the proportion of this transaction data's demand in the overall transmission resources; B j This represents the base bit value of the j-th auto finance transaction data. It is generated based on the number of bits and the highest bit of the data value, and is used to simplify subsequent calculations and comparisons. This represents the sum of the base bit values ​​of all auto finance transaction data, i.e., the total value, which is used to calculate the proportion of transmission resources for each transaction data.

[0073] In summary, calculating the transmission resource allocation using a base bit value ensures that each transaction receives corresponding transmission resources based on its data size, avoiding uneven resource allocation caused by large differences in data size. Furthermore, the introduction of a base bit value simplifies the processing of complex data sizes; compared to directly using data size values ​​for calculation, the base bit value is simpler and easier to process, improving computational efficiency. Moreover, the calculation of the transmission resource allocation provides a basis for subsequent calculations of the actual transmission interval. By dynamically adjusting the actual transmission interval, it is possible to better adapt to changes in the transmission path's carrying capacity, ensuring smooth data transmission.

[0074] In one implementation, the actual transmission interval corresponding to the auto finance transaction data generated in S4 based on the proportion of transmission resources and the initial transmission interval is expressed as follows:

[0075] T j =R j ·t c ;in,

[0076] T j t represents the actual transmission interval between the j-th and j+1-th auto finance transaction data. c This is the initial transmission interval.

[0077] In this embodiment, it should be noted that the transmission resource ratio R j With the initial transmission interval t c The actual transmission interval T is calculated by multiplying the products. j This ensures that each transaction data receives a corresponding transmission time based on its transmission resource requirements. This proportional allocation method utilizes transmission resources fairly, preventing one transaction data from consuming excessive transmission time and causing delays for other data. Furthermore, the calculation of the actual transmission interval takes into account the specific transmission resource requirements of each transaction data, making the transmission strategy more flexible and dynamic. When the initial transmission interval of the transmission path or the automotive finance transaction data changes, the initial transmission interval t can be adjusted. c Or recalculate the proportion of transmission resources R j To adapt to the new transmission environment.

[0078] An AI-based auto finance transaction data transmission system is also provided. This system implements the AI-based auto finance transaction data transmission method described in any of the above embodiments. The system includes:

[0079] The data and path acquisition module is used to acquire multiple auto finance transaction data and transmission paths, and to obtain the corresponding auto finance transaction time and data volume value based on the auto finance transaction data, and to obtain the initial transmission interval corresponding to the transmission path.

[0080] The queue generation module is used to arrange multiple auto finance transaction times in chronological order and generate a data transmission sequence queue.

[0081] The data processing module is used to generate basic bit values ​​based on the number of bits and the highest bit of the data value, thereby obtaining multiple basic bit values;

[0082] The calculation module is used to calculate the transmission resource ratio of each auto finance transaction data based on multiple basic bit values, and generate the actual transmission interval corresponding to the auto finance transaction data based on the transmission resource ratio and the initial transmission interval.

[0083] The transmission module is used to sequentially complete data transmission according to multiple actual transmission intervals and data transmission order queues, and to obtain the transmission arrival time interval of multiple auto finance transaction data.

[0084] The transmission feedback and update module is used to obtain the time difference based on the actual transmission interval and the corresponding transmission arrival time interval, obtain the path correction factor based on multiple time differences, and generate a new initial transmission interval based on the path correction factor and the initial transmission interval. The new initial transmission interval is applied to the transmission process of multiple auto finance transaction data in a new round on the transmission path.

[0085] In this embodiment, it should be noted that the specific method of performing the operation in the above-mentioned AI-based auto finance transaction data transmission system has been described in detail in the embodiments of the AI-based auto finance transaction data transmission method, and will not be elaborated here.

[0086] Figure 4 This is a block diagram of an electronic device illustrating an artificial intelligence-based method for transmitting data in automotive financial transactions, according to an exemplary embodiment. Figure 4 As shown, the electronic device 700 may include a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0087] The processor 701 controls the overall operation of the electronic device 700 to complete all or part of the steps in the aforementioned AI-based automotive finance transaction data transmission method. The memory 702 stores various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 702 or transmitted via communication component 705. The audio component also includes at least one speaker for outputting audio signals. I / O interface 704 provides an interface between processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0088] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the aforementioned artificial intelligence-based automotive financial transaction data transmission method.

[0089] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the aforementioned AI-based auto finance transaction data transmission method. For example, the computer-readable storage medium may be the aforementioned memory 702 including program instructions, which may be executed by the processor 701 of the electronic device 700 to complete the aforementioned AI-based auto finance transaction data transmission method.

[0090] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a programmable device, the computer program having a code portion for performing the above-described AI-based automotive financial transaction data transmission method when executed by the programmable device.

[0091] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0092] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0093] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. An automobile financial transaction data transmission method based on artificial intelligence, characterized in that: include: Acquire multiple automobile financial transaction data and transmission paths, and acquire corresponding automobile financial transaction time and data volume value according to the automobile financial transaction data, and acquire an initial transmission interval corresponding to the transmission path; Arrange multiple automobile finance transaction times in order and generate a data transmission order queue; Generate a basic place value according to the number of bits and the highest bit of the data value, thereby obtaining multiple basic place values; Calculating the transmission resource ratio of each automobile financial transaction data according to the multiple basic bit values, and generating an actual transmission interval corresponding to the automobile financial transaction data according to the transmission resource ratio and the initial transmission interval; completing data transmission in sequence according to multiple actual transmission intervals and data transmission sequence queues, and obtaining transmission arrival time intervals of multiple automobile financial transaction data; A time difference is obtained according to an actual transmission interval and a corresponding transmission arrival time interval, a path correction factor is obtained according to multiple time differences, and a new initial transmission interval is generated according to the path correction factor and the initial transmission interval, wherein the new initial transmission interval is applied to a new round of transmission process of multiple automobile financial transaction data on the transmission path.

2. The method for transmitting automobile financial transaction data based on artificial intelligence according to claim 1, characterized in that: Generating the basic bit value according to the number of bits and the highest bit of the data value comprises: Generate a first character string n1 according to the number of digits of the data value; Generate a second character string n2 according to the highest bit of the data value; The first character string and the second character string are concatenated to generate a basic bit value B, wherein B=n1||n2.

3. The method for transmitting automobile financial transaction data based on artificial intelligence according to claim 1, characterized in that: The obtaining of the path correction factor according to the multiple time differences comprises: Obtain stability indicators based on multiple time differences; Obtain multiple time differences that are less than the stability index and use them as multiple differences to be processed; An average is taken for a plurality of pending differences and a path correction factor is generated based on the average.

4. The method for transmitting automobile financial transaction data based on artificial intelligence according to claim 3 is characterized in that: The stability index obtained according to multiple time differences is expressed as: in, W is the stability index, Δt max is the maximum value among multiple time differences, and α is the preset reduction coefficient.

5. The method for transmitting automobile financial transaction data based on artificial intelligence according to claim 4 is characterized in that: The method of taking the average value of multiple pending differences and generating a path correction factor is expressed as: in, M is the path correction factor, Δt i is the ith difference to be processed, m is the number of differences to be processed, and β is the transmission environment delay coefficient.

6. The method for transmitting automobile financial transaction data based on artificial intelligence according to claim 1, characterized in that: The calculation of the transmission resource ratio of each automobile financial transaction data according to multiple basic bit values ​​is expressed as: in, R j is the transmission resource ratio of the jth automobile finance transaction data, B j is the basic bit value of the j-th automobile financial transaction data, B k is the basic bit value of the kth automobile financial transaction data, and n is the number of automobile financial transaction data.

7. The method for transmitting automobile financial transaction data based on artificial intelligence according to claim 6, characterized in that: The actual transmission interval corresponding to the automobile financial transaction data generated according to the transmission resource ratio and the initial transmission interval is expressed as: T j =R j ·t c ;in, T j is the actual transmission interval between the jth and j+1th automobile finance transaction data, t c is the initial transmission interval.

8. An automobile financial transaction data transmission system based on artificial intelligence, characterized in that: The system is used to implement the artificial intelligence-based automobile financial transaction data transmission method according to any one of claims 1 to 7, and the system includes: A data and path acquisition module, used to acquire multiple automobile financial transaction data and transmission paths, and acquire corresponding automobile financial transaction time and data volume value according to the automobile financial transaction data, and acquire an initial transmission interval corresponding to the transmission path; A queue generation module, used to arrange multiple automobile financial transaction times in order and generate a data transmission order queue; A data processing module, used for generating a basic bit value according to the number of bits and the highest bit of the data value, so as to obtain multiple basic bit values; A calculation module, used to calculate the transmission resource ratio of each automobile financial transaction data according to a plurality of basic bit values, and generate an actual transmission interval corresponding to the automobile financial transaction data according to the transmission resource ratio and the initial transmission interval; A transmission module, used to complete the data transmission in sequence according to a plurality of actual transmission intervals and a data transmission sequence queue, and obtain a transmission arrival time interval of a plurality of automobile financial transaction data; The transmission feedback and update module is used to obtain the time difference according to the actual transmission interval and the corresponding transmission arrival time interval, and obtain the path correction factor according to multiple time differences, and generate a new initial transmission interval according to the path correction factor and the initial transmission interval, wherein the new initial transmission interval is applied to the transmission process of a new round of multiple automobile financial transaction data on the transmission path.

9. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the artificial intelligence-based automobile financial transaction data transmission method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the artificial intelligence-based automobile financial transaction data transmission method described in any one of claims 1 to 7 is implemented.

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