Automobile financial asset data monitoring method, system and equipment based on artificial intelligence

Abstract

The invention discloses an automobile financial asset data monitoring method based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: obtaining to-be-uploaded automobile financial asset data based on an artificial intelligence terminal, and obtaining the importance degree of the automobile financial asset data; acquiring an uploading path; uploading the automobile financial asset data according to the uploading path, and monitoring the data real-time transfer time of each adjacent data transfer node; obtaining a monitoring degree based on the monitoring model, the importance degree and the number of the data relay nodes, and obtaining a target number according to the monitoring degree; extracting a target number of data real-time circulation times from the plurality of data real-time circulation times, and judging whether the extracted data real-time circulation times exceed a corresponding time threshold value or not; if not, processing the automobile financial asset data; and if so, uploading the automobile financial asset data again. The method has the advantages that data can be uploaded in time, the monitoring effect is good, and resources are saved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an automobile financial asset data monitoring method, system and device based on artificial intelligence. Background Art

[0002] Auto finance assets not only involve traditional auto loans, but also include various financial businesses such as auto finance leasing, auto insurance, and related derivatives. These businesses generate a large amount of data during operation, including but not limited to borrower information, vehicle information, loan or lease status, repayment records, insurance payment records, etc. These data are crucial for financial institutions' risk control, business decision-making, customer service, and compliance management. As a result, the transmission of auto finance asset data must be monitored to ensure the integrity and confidentiality of the data. Once the data is leaked or tampered with, it will cause serious losses to financial institutions and customers.

[0003] At present, in order to ensure the integrity and security of automobile financial asset data during the upload process, the existing data monitoring methods will verify the data during the transmission process at each data transfer node, and will also perform encryption and decryption verification during the data transmission process. Data caching and preprocessing technologies are also widely used.

[0004] However, data needs to be checked at each data transfer node, which not only increases processing time, but also may cause congestion when data flows between nodes, thereby prolonging data flow time and seriously affecting the real-time performance of data; at the same time, the encryption and decryption process itself consumes a lot of computing resources, further increasing the complexity and delay of data monitoring. In the field of automobile financial assets with huge data volume, this encryption and decryption verification mechanism may become a bottleneck for data transmission, resulting in a significant increase in data flow time; finally, data caching will reduce data accuracy due to the timeliness of cached data, and preprocessing technology will introduce additional errors due to the imperfection of pre-processing logic. These problems may have an adverse effect on the real-time performance and accuracy of data. In summary, the existing data monitoring methods will reduce the upload efficiency of automobile financial asset data and increase the delay of further processing, making it difficult to meet the needs of real-time decision-making. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides an automobile financial asset data monitoring method, system and device based on artificial intelligence, which effectively solves the problems of low efficiency, large delay and poor security in the existing automobile financial asset data uploading process, and provides more efficient, accurate and reliable data support for financial business decision-making.

[0006] A method for monitoring automobile financial asset data based on artificial intelligence comprises: obtaining automobile financial asset data to be uploaded based on an artificial intelligence terminal, and obtaining the importance of the automobile financial asset data; obtaining an upload path, wherein the upload path includes multiple data transfer nodes; uploading the automobile financial asset data according to the upload path, and monitoring the real-time data flow time of each adjacent data transfer node; obtaining a monitoring degree based on a monitoring model, the importance and the number of data transfer nodes, and obtaining a target number according to the monitoring degree; randomly extracting a target number of real-time data flow times from multiple real-time data flow times, and judging whether each extracted real-time data flow time exceeds a corresponding time threshold; if not, processing the automobile financial asset data; if exceeded, re-uploading the automobile financial asset data.

[0007] Preferably, the monitoring model in the monitoring degree is obtained based on the monitoring model, the importance and the number of data transfer nodes as follows: Among them, M is the monitoring degree, α and β are adjustment parameters, I is the importance of automobile financial asset data, N tr is the number of data transfer nodes.

[0008] Preferably, obtaining the target quantity according to the monitoring degree includes: obtaining a monitoring threshold; if the monitoring degree does not exceed the monitoring threshold, the target quantity is 1; if the monitoring degree exceeds the monitoring threshold, obtaining a proportional coefficient according to the monitoring degree and the monitoring threshold, and obtaining the target quantity based on the target model, the proportional coefficient and the number of data transfer nodes.

[0009] Preferably, the proportionality coefficient is obtained according to the monitoring degree and the monitoring threshold and is expressed as: Among them, P is the proportional coefficient, MI is the monitoring degree, and MT is the monitoring threshold.

[0010] Preferably, the target model in the target number is obtained based on the target model, the proportional coefficient and the number of data transfer nodes as follows: N ta =N tr ·P; where N ta is the target number, N tr is the number of data transfer nodes.

[0011] Preferably, determining whether the real-time flow time of each extracted data exceeds the corresponding time threshold includes: obtaining the upstream node and downstream node corresponding to the real-time flow time of the extracted data; obtaining the theoretical flow time of the data based on the upstream node and the downstream node; obtaining the time threshold based on the theoretical flow time of the data, and determining whether the real-time flow time of the data exceeds the time threshold.

[0012] Preferably, obtaining the importance of the automobile financial asset data includes: obtaining the data source and data size of the automobile financial asset data; obtaining a comprehensive score based on the data source and data size; and obtaining the importance of the automobile financial asset data based on the comprehensive score.

[0013] A car financial asset data monitoring system based on artificial intelligence is also provided, the system comprising: a first acquisition module, used to acquire the car financial asset data to be uploaded based on the artificial intelligence terminal, and acquire the importance of the car financial asset data; a second acquisition module, used to acquire the upload path, the upload path including multiple data transfer nodes; a data monitoring module, used to upload the car financial asset data according to the upload path, and monitor the real-time data flow time of each adjacent data transfer node; a model calculation module, used to acquire the monitoring degree based on the monitoring model, the importance and the number of data transfer nodes, and acquire the target number according to the monitoring degree; an extraction judgment module, used to randomly extract the target number of real-time data flow times from multiple real-time data flow times, and judge whether each extracted real-time data flow time exceeds the corresponding time threshold; a first control module, used to process the car financial asset data when the extracted real-time data flow time does not exceed the corresponding time threshold; and a second control module, used to re-upload the car financial asset data when the extracted real-time data flow time exceeds the corresponding time threshold.

[0014] An electronic device is also provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned automobile financial asset data monitoring method based on artificial intelligence.

[0015] A non-temporary computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned automobile financial asset data monitoring method based on artificial intelligence is implemented.

[0016] The beneficial effects of the present invention are embodied in:

[0017] In the entire automobile financial asset data monitoring method based on artificial intelligence, the automobile financial asset data to be uploaded is obtained by introducing an artificial intelligence terminal, and the importance of the data is evaluated, which provides strong support for subsequent monitoring and processing. At the same time, the monitoring strategy is dynamically adjusted in conjunction with the subsequent monitoring and processing, so as to allocate resources more reasonably and ensure that important data can be more strictly monitored and protected; further, by dynamically adjusting the monitoring degree and randomly sampling to check the real-time flow time of data, efficient monitoring of the data transmission process is achieved. Compared with the traditional method of performing data verification and encryption and decryption verification at each transfer node, this monitoring method not only reduces the consumption of processing time and computing resources, but also can more accurately detect anomalies and bottlenecks in the data transmission process. More importantly, it will not affect the real-time nature of the data, ensuring the timely uploading and processing of the data; further, when anomalies are found in the data transmission process, the system will trigger a mechanism for re-uploading data, and try to analyze the possible causes and take corresponding measures to solve the problem. This mechanism can not only ensure the integrity and accuracy of the data, but also improve the fault tolerance and stability of the system, providing more reliable data support for the business decision-making of financial institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. 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 according to the actual scale.

[0019] Figure 1 A schematic diagram of the steps of the automobile financial asset data monitoring method based on artificial intelligence of the present invention;

[0020] Figure 2 It is a schematic diagram of some steps of S4 in the automobile financial asset data monitoring method based on artificial intelligence of the present invention;

[0021] Figure 3 It is a schematic diagram of some steps of S5 in the automobile financial asset data monitoring method based on artificial intelligence of the present invention;

[0022] Figure 4 It is a schematic diagram of some steps of S1 in the automobile financial asset data monitoring method based on artificial intelligence of the present invention;

[0023] Figure 5 The present invention is a block diagram of an electronic device according to an embodiment of the present invention.

[0024] Reference numerals:

[0025] 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - input / output (I / O) interface, 705 - communication component. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0028] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0029] like Figure 1 As shown, a method for monitoring automobile financial asset data based on artificial intelligence is provided, comprising:

[0030] S1. Obtaining automobile financial asset data to be uploaded based on an artificial intelligence terminal, and obtaining the importance of the automobile financial asset data;

[0031] S2. Obtain an upload path, where the upload path includes multiple data transfer nodes;

[0032] S3. Upload the automobile financial asset data according to the upload path, and monitor the real-time data flow time of each adjacent data transfer node;

[0033] S4. Obtaining a monitoring degree based on the monitoring model, the importance, and the number of data transfer nodes, and obtaining a target number according to the monitoring degree;

[0034] S5. randomly extract a target number of data real-time flow times from multiple data real-time flow times, and determine whether each extracted data real-time flow time exceeds a corresponding time threshold;

[0035] S6. If not, the automobile financial asset data shall be processed;

[0036] S7. If exceeded, re-upload the automobile financial asset data.

[0037] In this embodiment, it should be noted that in S1, the automobile financial asset data to be uploaded is obtained by using artificial intelligence terminals. These intelligent terminals can be servers or special equipment equipped with high-performance computing capabilities and artificial intelligence algorithms, which can efficiently obtain a large amount of data. Then, while obtaining the data, the importance of each piece of automobile financial asset data will be obtained according to the preset rules and algorithms. The importance is an indicator to measure the value and priority of data. It can be comprehensively evaluated according to multiple dimensions such as data type, source, content and business needs; for example, for automobile loan business, the importance of data such as the borrower's credit record, repayment history and vehicle information is relatively high, because these data are directly related to the risk control and business decision-making of financial institutions; the importance of customer consultation records, market analysis reports, etc. may be relatively low; each piece of automobile financial asset data has an importance, which provides strong support for subsequent data monitoring.

[0038] In S2, the current network environment is first analyzed, including key parameters such as the location, connection status, bandwidth, and latency of each data transfer node, and a low-latency, high-bandwidth path is selected to ensure that the data can reach the destination faster and more stably. At the same time, the network's redundancy and fault tolerance are also considered to avoid the failure of the entire data transmission process due to the failure of a single node.

[0039] In S3, according to the upload path determined in step S2, the auto finance asset data is uploaded, and the real-time data transfer time between each adjacent data transfer node is monitored in real time. Specifically, the data transfer time of each data transfer node is accurately measured, including the arrival time and sending time of the data at each node. The monitoring results of these data transfer times will be recorded to provide an important basis for subsequent monitoring and processing.

[0040] Assume that automobile financial data needs to be uploaded from location A to a server at location E through three data transfer nodes B, C, and D. During the upload process, the data transfer time between the four adjacent nodes A to B, B to C, C to D, and D to E is monitored respectively.

[0041] In S4, by introducing the monitoring model, following certain rules or algorithms, and dynamically adjusting the monitoring degree according to the importance and the number of data transfer nodes, the setting of the monitoring degree is directly related to the monitoring demand of the data transmission process, and finally the target number is obtained according to the monitoring degree, that is, the number of real-time data flow time samples to be extracted. First, the importance of data has a direct impact on the monitoring degree. The greater the importance, the higher the value of these data for business decision-making and risk control. Therefore, the requirements of the data transmission process are more stringent. In order to ensure that these data can reach the destination safely and accurately, the greater the monitoring degree is, and the greater the number of targets obtained according to the monitoring degree is. Secondly, the number of data transfer nodes is also an important factor affecting the monitoring degree. In the process of data transmission, each data transfer node will increase the risk of data error or loss. Therefore, when the number of data transfer nodes is larger, the probability of problems in the data transmission process is greater. In order to reduce this risk, the greater the monitoring degree is, and the greater the number of targets obtained according to the monitoring degree is.

[0042] Assume that a batch of automobile financial asset data needs to be uploaded to a remote server. The importance of this batch of data is very high because it is directly related to the risk control and business decision-making of financial institutions. At the same time, due to the complex network environment, the data needs to pass through multiple data transfer nodes to reach the destination. In this case, according to the importance of the data and the number of data transfer nodes, a relatively large monitoring degree is obtained, and then the target number that needs to be extracted from multiple data transfer nodes is obtained based on this monitoring degree.

[0043] In S5, according to the determined target quantity, a corresponding number of samples are randomly selected from the multiple real-time data flow times that have been monitored and recorded. This is to retrospectively detect and analyze the upload process of the data after it has been uploaded to the destination to ensure that the monitoring process does not affect the real-time nature of the data. The purpose of this step is to infer the efficiency and stability of the overall data transmission process by checking the flow time of some data, and then evaluate whether the data is at risk of being tampered with. Specifically, a random sampling method will be used to ensure that the probability of each real-time data flow time being selected is equal, so as to avoid deviations or misleading caused by improper sample selection. After extracting the target number of real-time data flow times, check these samples one by one to see if they exceed the corresponding time threshold. The time threshold is a preset standard value after considering the network environment and other influences of the actual data transfer node. It reflects the maximum time allowed for data to flow between each adjacent transfer node.

[0044] Assume that the number of determined targets is 4, and there are 20 real-time data flow times that have been monitored and recorded; in S5, 4 samples will be randomly selected from these 20 real-time data flow times for inspection. If the flow time of one of the samples exceeds the preset time threshold of 500 milliseconds, it is considered that there may be problems in the data flow process, such as network congestion, node failure, etc. In summary, by analyzing the data flow time records after uploading, bottlenecks and anomalies in the data transmission process can be identified more efficiently without the need for multiple checks during the transmission process, thereby reducing the burden and ensuring the real-time nature of the data; further, by random sampling and checking the real-time data flow time, possible problems and risk points in the data transmission process can be efficiently discovered without the need to check each data flow time, thereby greatly improving the efficiency of monitoring.

[0045] In S6, it is determined that the real-time flow time of the randomly selected data does not exceed the corresponding time threshold, that is, it is considered that the entire data transmission process has reached the expected standards in terms of efficiency and stability, and there is no abnormal transmission process. At this time, the subsequent processing flow of the automobile financial asset data that has been uploaded to the destination continues. The subsequent data processing includes but is not limited to data cleaning, data verification, data integration, and data storage operations for subsequent business analysis and decision-making.

[0046] In S7, if the real-time flow time of the randomly selected data exceeds the corresponding time threshold, it is judged that there may be an abnormality in the data transmission process. At this time, the mechanism of re-uploading the automobile financial asset data is triggered to ensure the integrity of the data.

[0047] At the same time, when it is found that the real-time flow time of a certain data exceeds the time threshold, this abnormal event will be recorded and possible causes will be analyzed, such as network congestion, node failure, unreasonable data transmission path, etc. Then, corresponding measures will be taken to solve the problem according to the analysis results. If the problem is caused by network congestion or node failure, wait for a while before uploading the data again, or try to change other available data transmission paths. If the problem is caused by an unreasonable data transmission path, recalculate and select a better path to upload the data.

[0048] In summary, in the entire automobile financial asset data monitoring method based on artificial intelligence, the automobile financial asset data to be uploaded is obtained by introducing an artificial intelligence terminal, and the importance of the data is evaluated, which provides strong support for subsequent monitoring and processing. At the same time, the monitoring strategy is dynamically adjusted in conjunction with the subsequent monitoring and processing, so as to allocate resources more reasonably and ensure that important data can be more strictly monitored and protected; further, by dynamically adjusting the monitoring degree and randomly sampling to check the real-time flow time of data, efficient monitoring of the data transmission process is achieved. Compared with the traditional method of performing data verification and encryption and decryption verification at each transfer node, this monitoring method not only reduces the consumption of processing time and computing resources, but also can more accurately detect anomalies and bottlenecks in the data transmission process. More importantly, it will not affect the real-time nature of the data, ensuring the timely uploading and processing of the data; further, when anomalies are found in the data transmission process, a mechanism for re-uploading data will be triggered, and attempts will be made to analyze possible causes and take corresponding measures to solve the problem. This mechanism can not only ensure the integrity and accuracy of the data, but also improve fault tolerance and stability, providing more reliable data support for the business decisions of financial institutions.

[0049] In one embodiment, the monitoring model in the monitoring degree is obtained based on the monitoring model, the importance and the number of data transfer nodes as follows:

[0050] in,

[0051] M is the monitoring degree, α and β are adjustment parameters, I is the importance of automobile financial asset data, N tr is the number of data transfer nodes.

[0052] In this embodiment, it should be noted that the design of this monitoring model aims to dynamically adjust the monitoring degree according to the importance of the data and the number of data transfer nodes to achieve more accurate and stable data monitoring. tr +β), when the importance I or the number of data transfer nodes N tr When I or N is small, the change of monitoring degree M is very sensitive, and the monitoring degree can be quickly adjusted to adapt to different situations. tr After increasing to a certain extent, the increase in monitoring degree will gradually slow down to avoid waste of resources caused by excessive monitoring; by adjusting the two parameters α and β, ln(αxN tr +β), so as to achieve fine adjustment of the monitoring degree. Different business scenarios have different needs and requirements for data monitoring. Therefore, when obtaining the values ​​of α and β, it is necessary to fully consider the characteristics and needs of the business scenarios. The initial values ​​of α and β can also be set based on historical record information or other professional knowledge and the monitoring effects of historical projects. In the calculation, the cumulative effect of the importance of data in the entire range of x from 0 to I on the calculated monitoring degree is considered. This means that when calculating the monitoring degree, not only the current data importance is considered, but also the cumulative effect of all its previous importance. At the same time, by combining I, N tr By taking multiple factors such as , α and β into consideration and calculating their cumulative effects, the model can reflect the actual situation more accurately. In addition, the smoothness of the monitoring model enables the monitoring degree to transition smoothly as the importance of data changes, thereby improving the continuity and stability of the monitoring degree.

[0053] like Figure 2 As shown, in one embodiment, obtaining the target quantity according to the monitoring degree in S4 includes:

[0054] S41, obtaining a monitoring threshold;

[0055] S42, if the monitoring degree does not exceed the monitoring threshold, the target number is 1;

[0056] S43. If the monitoring degree exceeds the monitoring threshold, a proportional coefficient is obtained according to the monitoring degree and the monitoring threshold, and a target quantity is obtained based on the target model, the proportional coefficient and the number of data transfer nodes.

[0057] In this embodiment, it should be noted that in S41, a monitoring threshold needs to be determined first. This monitoring threshold is a preset limit, which is used to determine the range of the current data monitoring degree, and then determine the subsequent processing measures. The setting of the monitoring threshold is usually based on a comprehensive consideration of multiple factors such as historical data, business requirements, security standards, and system performance. For example, according to past experience, when the number of data transfer nodes reaches a certain level, the probability of data loss and tampering is relatively high. When the importance reaches a certain level, the loss and tampering of data will have a significant impact on the business. At this time, it is necessary to set a corresponding monitoring threshold to trigger additional monitoring measures. Finally, this monitoring threshold can be obtained through a configuration file, a database, or real-time monitoring data, and used as a basis for subsequent judgments.

[0058] S42. If the monitoring degree does not exceed this threshold, it means that the current data transmission status is stable and does not require special attention or additional measures. Therefore, in this case, setting the target number to 1 means that only the simplest monitoring measures need to be maintained without adding additional monitoring resources. This setting helps reduce unnecessary waste of resources while ensuring that the normal operation of the business is not excessively disturbed when the data status is good.

[0059] S43. If the monitoring degree exceeds the preset monitoring threshold, it means that there may be problems with the current data transmission status, and more stringent monitoring measures need to be taken to ensure the security and stability of the data. In this case, a proportionality coefficient is first calculated based on the difference between the monitoring degree and the monitoring threshold. This proportionality coefficient reflects the extent to which the monitoring degree exceeds the threshold and the urgency of increasing monitoring resources. Then, a pre-established target model is used to calculate a reasonable target number in combination with this proportionality coefficient and the number of data transfer nodes. This target number represents the amount of monitoring resources that need to be invested in order to ensure data security and stable transmission under the current circumstances. In this way, the monitoring strategy can be dynamically adjusted to cope with different data transmission states and risk levels.

[0060] In one embodiment, the proportionality coefficient is obtained according to the monitoring degree and the monitoring threshold in S43 and is expressed as:

[0061] in,

[0062] P is the proportional coefficient, MI is the monitoring degree, and MT is the monitoring threshold.

[0063] In this embodiment, it should be noted that P is a value between 0 and 1, which is used to indicate the relative degree to which the monitoring degree exceeds the monitoring threshold. When MI approaches MT, P is small, indicating that the monitoring degree does not require additional monitoring resources; when MI is much greater than MT, P approaches 1, indicating that the monitoring degree far exceeds the threshold and a substantial increase in monitoring resources is required. By calculating the proportional coefficient P, the allocation of monitoring resources can be dynamically adjusted according to the real-time relationship between the monitoring degree and the monitoring threshold, which helps to give priority to data transmission processes with higher monitoring degrees and higher risks when resources are limited; further, it can avoid wasting monitoring resources when the monitoring degree is low, or insufficient monitoring resources when the monitoring degree is high, thereby improving the efficiency and accuracy of monitoring.

[0064] In one embodiment, the target model in the target quantity obtained based on the target model, the proportional coefficient and the number of data transfer nodes in S43 is expressed as:

[0065] N ta =N tr P; where

[0066] N ta is the target number, N tt is the number of data transfer nodes.

[0067] In this embodiment, it should be noted that N tris the total number of transit nodes involved in the data transmission process. The more transit nodes there are, the higher the complexity and risk of data transmission, so more monitoring resources may be required; P is a coefficient calculated based on the monitoring degree and the monitoring threshold, which is used to quantify the degree to which the monitoring degree exceeds the monitoring threshold. The larger the value of P, the higher the degree to which the monitoring degree exceeds the threshold, so more monitoring resources are required. By combining the number of data transit nodes and the proportional coefficient, the target model can dynamically calculate the number of monitoring resources that need to be invested, which ensures that monitoring resources can be reasonably allocated according to the actual situation and risk level of data transmission; further, by accurately calculating the target number, it can avoid the waste of monitoring resources. In the case of low monitoring degree or fewer data transit nodes, the investment in monitoring resources can be reduced accordingly, thereby optimizing the utilization efficiency of resources.

[0068] like Figure 3 As shown, in one embodiment, determining in S5 whether the real-time flow time of each extracted data exceeds the corresponding time threshold includes:

[0069] S51, obtaining the upstream node and the downstream node corresponding to the real-time flow time of the extracted data;

[0070] S52, obtaining theoretical data flow time according to the upstream node and the downstream node;

[0071] S53. Obtain a time threshold according to the theoretical data flow time, and determine whether the real-time data flow time exceeds the time threshold.

[0072] In this embodiment, it should be noted that, in S51, the upstream node and the downstream node related to the extracted data real-time flow time are first identified and obtained. The upstream node refers to the previous node in the data flow process, which is responsible for sending data to the downstream node; and the downstream node refers to the next node in the data flow process, which is responsible for receiving data from the upstream node. By tracing the flow path of the data, the upstream and downstream nodes corresponding to each data real-time flow time can be determined.

[0073] In S52, after determining the upstream nodes and downstream nodes corresponding to the real-time data flow time, the theoretical data flow time will be calculated based on the information of these nodes. The theoretical data flow time refers to the time required for data to be transmitted from the upstream node to the downstream node under ideal conditions. The calculation of this time may take into account multiple factors, such as network bandwidth, node processing power, data transmission protocol, etc. The parameters of these factors may be obtained through historical data, performance testing or real-time monitoring, and the theoretical data flow time may be calculated in combination with the configuration and status of the node. This time is used as a benchmark for subsequent judgments on whether the real-time data flow time is reasonable.

[0074] In S53, after the theoretical data flow time is obtained, a time threshold will be determined based on this time. The time threshold is a tolerance range used to allow for slight delays in the actual data transmission process due to various reasons (such as network congestion, node load, etc.). This time threshold can be set according to business needs, service quality requirements or historical experience. After setting the time threshold, the real-time data flow time is compared with this time threshold to determine whether the data has been transmitted within a reasonable time. If the real-time data flow time exceeds the time threshold, an alarm will be triggered or other measures will be taken to diagnose and solve the problem to ensure timely transmission and processing of data.

[0075] like Figure 4 As shown, in one embodiment, obtaining the importance of automobile financial asset data in S1 includes:

[0076] S11. Obtaining the data source and data size of automobile financial asset data;

[0077] S12. Obtain a comprehensive score based on data source and data size;

[0078] S13. Obtain the importance of automobile financial asset data based on the comprehensive score.

[0079] In this embodiment, it should be noted that in S11, the data source and data size of the automobile financial asset data need to be identified and collected first. The data source may include multiple channels, such as automobile manufacturers, financial institutions, third-party data providers, etc. The data from each source may have different reliability and importance, and the data size reflects the amount of data, including the number of records, the number of fields, and the amount of data in each field. The information on these data sources and data sizes can be obtained based on the artificial intelligence terminal access to facilities such as databases, data warehouses, or real-time data streams that store and transmit data. This information is the basis for the subsequent evaluation of the importance of the data.

[0080] In S12, the comprehensive score is calculated based on the information of data source and data size. This comprehensive score is a quantitative indicator. The data source and data size each correspond to an indicator, reflecting their contribution to the overall evaluation. The comprehensive score is calculated by multiplying these indicators. Assume that the data of automobile financial assets comes from two sources, namely automobile manufacturers and financial institutions. The corresponding indicator of automobile manufacturers is 0.6, and the corresponding indicator of financial institutions is 0.7. At the same time, the data size is a medium-sized data set (100MB-1GB), and its corresponding indicator is 1.2. Then the comprehensive score = 0.7*1.2+0.6*1.2 = 1.56. This comprehensive score will serve as the main basis for judging the importance of data.

[0081] S13. After calculating the comprehensive score, the importance of the automobile financial asset data is determined based on the score. By determining the importance of the data, different strategies and measures can be taken according to the importance level of the data in the subsequent data monitoring, storage and transmission process to ensure the security, efficiency and rational use of the data. The comprehensive score can be directly identified as the importance, or it can be calculated through a series of magnification or reduction.

[0082] Also provided is an automobile financial asset data monitoring system based on artificial intelligence, which is used to implement the automobile financial asset data monitoring method based on artificial intelligence in any one of the above embodiments, and the system includes:

[0083] A first acquisition module is used to acquire automobile financial asset data to be uploaded based on an artificial intelligence terminal, and acquire the importance of the automobile financial asset data;

[0084] A second acquisition module is used to acquire an upload path, where the upload path includes multiple data transfer nodes;

[0085] The data monitoring module is used to upload the automobile financial asset data according to the upload path and monitor the real-time data flow time of each adjacent data transfer node;

[0086] A model calculation module is used to obtain a monitoring degree based on a monitoring model, importance, and the number of data transfer nodes, and obtain a target number according to the monitoring degree;

[0087] An extraction judgment module is used to randomly extract a target number of data real-time flow times from multiple data real-time flow times, and judge whether each extracted data real-time flow time exceeds a corresponding time threshold;

[0088] A first control module, for processing the automobile financial asset data when the real-time flow time of the extracted data does not exceed the corresponding time threshold;

[0089] The second control module is used to re-upload the automobile financial asset data when the real-time flow time of the extracted data exceeds the corresponding time threshold.

[0090] In this embodiment, it should be noted that, regarding the above-mentioned artificial intelligence-based automobile financial asset data monitoring system, the specific method of performing operations therein has been described in detail in the implementation of the artificial intelligence-based automobile financial asset data monitoring method, and will not be elaborated here.

[0091] Figure 5 is a block diagram of an electronic device for an automobile financial asset data monitoring method based on artificial intelligence according to an exemplary embodiment. Figure 5As 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.

[0092] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned automobile financial asset data monitoring method based on artificial intelligence. The memory 702 is used to store various types of data to support the operation of the electronic device 700, and these data may include, for example, instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, 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 signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The 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, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0093] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned artificial intelligence-based automobile financial asset data monitoring method.

[0094] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for monitoring automobile financial asset data based on artificial intelligence are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned method for monitoring automobile financial asset data based on artificial intelligence.

[0095] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned artificial intelligence-based automobile financial asset data monitoring method when executed by the programmable device.

[0096] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0097] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0098] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An automobile financial asset data monitoring method based on artificial intelligence, characterized in that: include: Obtain the automobile financial asset data to be uploaded based on the artificial intelligence terminal, and obtain the importance of the automobile financial asset data; Obtaining an upload path, where the upload path includes multiple data transfer nodes; Upload auto finance asset data according to the upload path and monitor the real-time data flow time of each adjacent data transfer node; Obtain monitoring degree based on monitoring model, importance and number of data transfer nodes, and obtain target quantity according to monitoring degree; Randomly extracting a target number of data real-time flow times from multiple data real-time flow times, and determining whether each extracted data real-time flow time exceeds a corresponding time threshold; If it does not exceed, the automobile financial asset data will be processed; If exceeded, re-upload the automobile financial asset data.

2. The method for monitoring automobile financial asset data based on artificial intelligence according to claim 1, characterized in that: The monitoring model in obtaining the monitoring degree based on the monitoring model, importance and the number of data transfer nodes is expressed as: in, M is the monitoring degree, α and β are adjustment parameters, I is the importance of automobile financial asset data, N tr is the number of data transfer nodes.

3. The method for monitoring automobile financial asset data based on artificial intelligence according to claim 1, characterized in that: The obtaining of the target quantity according to the monitoring degree includes: Get monitoring threshold; If the monitoring degree does not exceed the monitoring threshold, the target number is 1; If the monitoring degree exceeds the monitoring threshold, a proportional coefficient is obtained according to the monitoring degree and the monitoring threshold, and a target quantity is obtained based on the target model, the proportional coefficient, and the number of data transfer nodes.

4. The method for monitoring automobile financial asset data based on artificial intelligence according to claim 3 is characterized in that: The proportional coefficient obtained according to the monitoring degree and the monitoring threshold is expressed as: in, P is the proportional coefficient, MI is the monitoring degree, and MT is the monitoring threshold.

5. The method for monitoring automobile financial asset data based on artificial intelligence according to claim 4 is characterized in that: The target model in the target quantity obtained based on the target model, the proportional coefficient and the number of data transfer nodes is expressed as: N ta =N tr P; where N ta is the target number, N tr is the number of data transfer nodes.

6. The method for monitoring automobile financial asset data based on artificial intelligence according to claim 1, characterized in that: The step of determining whether the real-time flow time of each extracted data exceeds the corresponding time threshold comprises: Get the upstream and downstream nodes corresponding to the real-time flow time of the extracted data; Obtain theoretical data flow time based on upstream and downstream nodes; The time threshold is obtained according to the theoretical data flow time, and it is determined whether the real-time data flow time exceeds the time threshold.

7. The method for monitoring automobile financial asset data based on artificial intelligence according to claim 1, characterized in that: The importance of obtaining automobile financial asset data includes: Obtain the data source and data size of automobile financial asset data; Get a comprehensive score based on data source and data size; Obtain the importance of automobile financial asset data based on the comprehensive score.

8. An automobile financial asset data monitoring system based on artificial intelligence, characterized in that: The system is used to implement the automobile financial asset data monitoring method based on artificial intelligence as described in any one of claims 1 to 7, and the system includes: A first acquisition module is used to acquire automobile financial asset data to be uploaded based on an artificial intelligence terminal, and acquire the importance of the automobile financial asset data; A second acquisition module is used to acquire an upload path, where the upload path includes multiple data transfer nodes; The data monitoring module is used to upload the automobile financial asset data according to the upload path and monitor the real-time data flow time of each adjacent data transfer node; A model calculation module is used to obtain a monitoring degree based on a monitoring model, importance, and the number of data transfer nodes, and obtain a target number according to the monitoring degree; An extraction judgment module is used to randomly extract a target number of data real-time flow times from multiple data real-time flow times, and judge whether each extracted data real-time flow time exceeds a corresponding time threshold; A first control module, for processing the automobile financial asset data when the real-time flow time of the extracted data does not exceed the corresponding time threshold; The second control module is used to re-upload the automobile financial asset data when the real-time flow time of the extracted data exceeds the corresponding time threshold.

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

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