Dynamic caching method and device for supply chain financial factoring order, and medium

Abstract

The invention relates to the technical field of data caching, and discloses a dynamic caching method and device for supply chain financial factoring orders and a medium, and the method comprises the steps: extracting queried order data of historical query records in a factoring repayment page; utilizing a preset time sequence model to count the inquired probability distribution of the inquired order data at different time points; when the highest probability value in the probability distribution corresponding to each piece of inquired order data is smaller than a preset probability threshold value, gathering the inquired order data corresponding to the highest probability value into first factoring order data, and storing the first factoring order data to a physical medium layer in a hierarchical cache; and when the highest probability value corresponding to each piece of inquired order data is greater than or equal to a preset probability threshold value, gathering the inquired order data corresponding to the highest probability value into second factoring order data, and dynamically caching the second factoring order data according to the distribution change of the highest probability value in the probability distribution. According to the invention, the data query efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data caching technology, and in particular to a dynamic caching method, device and medium for supply chain financial factoring orders. Background Art

[0002] As time goes by and business needs change, the access pattern and importance of data may change. When the amount of data to be queried is large and the amount of data corresponding to the query request is large, the data query will be slow. However, the slow query response is likely caused by unreasonable data storage. Therefore, the data storage method needs to be optimized.

[0003] Existing data caching technology refers to storing data in memory. In practical applications, storing a large amount of data in memory may be limited by capacity and cannot store all the data. When the amount of data increases, the storage capacity of the system cannot be expanded simply by increasing the memory, resulting in a slow query speed when querying data, which leads to low efficiency when querying data. Summary of the invention

[0004] The present invention provides a dynamic caching method, device and medium for supply chain financial factoring orders, the main purpose of which is to solve the problem of low efficiency when performing data query.

[0005] To achieve the above-mentioned purpose, the present invention provides a dynamic caching method for supply chain finance factoring orders, comprising:

[0006] Extract the queried order data from the historical query records in the factoring repayment page;

[0007] Using a preset time series model to calculate the probability distribution of the queried order data being queried at different time points;

[0008] When the highest probability value in the probability distribution corresponding to each queried order data is less than a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as first factoring order data, and the first factoring order data is stored in the physical medium layer in the hierarchical cache;

[0009] When the highest probability value corresponding to each queried order data is greater than or equal to a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as second factoring order data, and the second factoring order data are dynamically cached according to the distribution change of the highest probability value in the probability distribution.

[0010] Optionally, the extracting of the queried order data of the historical query record in the factoring repayment page includes:

[0011] Time classification of the historical query records according to preset time intervals;

[0012] Classify different types of query data in the historical query records after time classification;

[0013] The query data of different types after classification are aggregated into the queried order data.

[0014] Optionally, the using a preset time series model to count the probability distribution of the queried order data being queried at different time points includes:

[0015] Counting a first probability distribution of the queried order data being queried at a historical time point within a preset time interval;

[0016] Generate time series data of the queried order data according to the query time point corresponding to the queried order data;

[0017] Counting the error terms in the time series data according to a preset lag order;

[0018] Calculate the query count observation value of the queried order data at the target time point using the preset time series model and the error term;

[0019] Counting a second probability distribution of the queried order data at a target time point according to the query frequency observation value;

[0020] A first curve corresponding to the first probability distribution and a second curve corresponding to the second probability distribution are fitted as probability distributions of the queried order data being queried at different time points.

[0021] Optionally, the calculating the query count observation value of the queried order data at the target time point by using the preset time series model and the error term includes:

[0022] Using the time series data to fit a preset time series model, and determining a model constant term and a lag order of the fitted time series model;

[0023] The query times observation value of the queried order data at the target time point is calculated according to the model constant term, the lag order and the error term, wherein the calculation formula of the query times observation value is:

[0024]

[0025] Among them, X t is the number of query observations at the target time point t, c is the model constant term, θ i is the i-th fitting model parameter, ε t-i is the ti-th error term, and q is the lag order.

[0026] Optionally, the step of aggregating the queried order data corresponding to the highest probability value as first factoring order data includes:

[0027] Count the number of queried order data corresponding to the highest probability value in each probability distribution;

[0028] When the data quantity is greater than a preset quantity threshold, any queried order data corresponding to the highest probability value is selected as the target queried order data, and the target queried order data is aggregated as the first factoring order data;

[0029] When the data quantity is equal to a preset quantity threshold, the queried order data corresponding to the highest probability value is used as the target queried order data, and the target queried order data is aggregated into the first factoring order data.

[0030] Optionally, before storing the first factoring order data in the physical medium layer in the hierarchical cache, the method further includes:

[0031] It is divided into high-level cache structure and low-level cache structure in order from top to bottom;

[0032] Using the high-level cache structure as a cache layer, and using the low-level cache structure as a physical medium layer;

[0033] The cache layer and the physical medium layer are constructed as a hierarchical cache in a top-down order.

[0034] Optionally, storing the first factoring order data in a physical medium layer in a hierarchical cache includes:

[0035] Using the first factoring order data as query cold data, and obtaining a storage address of the query cold data;

[0036] Allocating the query cold data to a physical medium layer in a hierarchical cache according to the storage address;

[0037] Calculate the real-time probability distribution of the queried order data according to a preset time change;

[0038] The first factoring order data is updated according to the real-time probability distribution, and the process returns to the step of using the first factoring order data as query cold data until a preset deadline is reached.

[0039] Optionally, dynamically caching the second factoring order data according to the distribution change of the highest probability value in the probability distribution includes:

[0040] Determine the cache priority of each queried order data according to the highest probability value in the probability distribution;

[0041] The queried order data with the highest cache priority is stored in a high-speed cache layer in the hierarchical cache;

[0042] Extract the probability change of the highest probability value corresponding to each queried order data in the distribution change;

[0043] updating the highest probability value in the probability distribution according to the probability change;

[0044] The cache priority corresponding to each queried order data is updated according to the updated highest probability value, and the step of storing the queried order data with the highest cache priority in the cache layer in the hierarchical cache is returned until all the queried order data are stored.

[0045] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0046] at least one processor; and,

[0047] a memory communicatively connected to the at least one processor; wherein,

[0048] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the dynamic caching method for supply chain financial factoring orders described above.

[0049] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the dynamic caching method of supply chain financial factoring orders described above.

[0050] The embodiment of the present invention performs big data analysis based on the historical query records of the factoring repayment page, combines the time series model to confirm the probability distribution of different types of data being queried at different times, and then stores the factoring data in layers according to the probability distribution, stores the infrequently queried data at the bottom layer, and dynamically caches the frequently queried data according to its distribution time series according to the different time series distributions, so as to improve the overall query efficiency of the data. Therefore, the dynamic caching method, device and medium of the supply chain finance factoring order proposed by the present invention can solve the problem of low efficiency when performing data query. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1A schematic diagram of a flow chart of a dynamic caching method for supply chain finance factoring orders provided by an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of a flow chart of calculating probability distribution provided by an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of a process for collecting first factoring order data provided by an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of the structure of an electronic device for implementing the dynamic caching method for supply chain financial factoring orders provided in one embodiment of the present invention.

[0055] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0057] The embodiment of the present application provides a dynamic caching method for supply chain finance factoring orders. The execution subject of the dynamic caching method for supply chain finance factoring orders includes but is not limited to at least one of the electronic devices such as the server, the terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the dynamic caching method for supply chain finance factoring orders can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0058] Reference Figure 1 FIG. 1 is a flow chart of a method for dynamically caching supply chain finance factoring orders provided by an embodiment of the present invention. In this embodiment, the method for dynamically caching supply chain finance factoring orders includes:

[0059] S1. Extract the queried order data from the historical query records in the factoring repayment page.

[0060] In an embodiment of the present invention, the queried order data refers to the data that has been queried in the factoring repayment page, that is, the historical record data queried in the factoring repayment page, including but not limited to the user's query records of repayment status, bill status and other information.

[0061] In the embodiment of the present invention, the extracting the queried order data of the historical query record in the factoring repayment page includes:

[0062] Time classification of the historical query records according to preset time intervals;

[0063] Classify different types of query data in the historical query records after time classification;

[0064] The query data of different types after classification are aggregated into the queried order data.

[0065] In detail, the data of historical query records are obtained from the factoring repayment page, and then the historical query records are time-classified according to the preset time interval, that is, the query records within the specified time range are filtered out. For example, if the time interval is set from one week ago to the current time, then the records within this week need to be filtered out from the historical query records, and then the query records after time classification are type-classified, that is, the specific query records are classified according to their types. For example, the query records can be divided into different types such as bill query, repayment status query, interest rate query, etc.; finally, the query data of different types after classification are gathered together to form a summary of the queried order data, and the classified data is stored in a data structure, such as a dictionary or list, for subsequent use or display.

[0066] Furthermore, since the amount of data to be queried is large, when the amount of data corresponding to the query request is large, the data query will be slow. It is necessary to analyze the query frequency of the queried order data to efficiently optimize the data storage method.

[0067] S2. Utilize a preset time series model to calculate the probability distribution of the queried order data being queried at different time points.

[0068] In one of the actual application scenarios of the present invention, users of the supply chain finance platform need to frequently query the status and details of factoring orders. During the peak period of order inquiries, a large number of users simultaneously querying order information may cause system performance degradation and response delays. By statistically analyzing the probability distribution of the queried order data at different time points, popular order data can be cached in memory, reducing direct access to the database and reducing the load pressure on the database.

[0069] In the embodiment of the present invention, the probability distribution refers to the probability of the queried order data being queried at different times, and the query probability corresponding to each queried order data will be counted within a specified time.

[0070] In the embodiment of the present invention, refer to Figure 2 As shown, the method of using a preset time series model to count the probability distribution of the queried order data being queried at different time points includes:

[0071] S21, counting the first probability distribution of the queried order data being queried at historical time points within a preset time interval;

[0072] S22, generating time series data of the queried order data according to the query time point corresponding to the queried order data;

[0073] S23, counting the error terms in the time series data according to a preset lag order;

[0074] S24, using a preset time series model and the error term to calculate the query count observation value of the queried order data at the target time point;

[0075] S25, counting the second probability distribution of the queried order data at the target time point according to the query frequency observation value;

[0076] S26. Fitting a first curve corresponding to the first probability distribution and a second curve corresponding to the second probability distribution into probability distributions of the queried order data being queried at different time points.

[0077] In detail, based on the historical query data, they are sorted in ascending order of time points. For each time point, the number of queries for the queried order data is calculated and normalized to probability, that is, the number of queries for each queried order data is divided by the total number of queries to obtain its relative frequency, that is, probability. To ensure that the sum of all probabilities is 1, each probability can be divided by the total sum of probabilities to obtain the first probability distribution of the queried order data at different time points, wherein the first probability distribution refers to the probability distribution generated by counting the number of queries for the historical queried order data based on the historical query data; and according to the query time points corresponding to the queried order data, time series data is constructed, then the time series data is a simple time series, and each time point corresponds to the number of queries for the queried order data; in addition, based on the pre-selected lag order, the time series data is fitted through a time series model (such as an ARIMA model) to obtain the residual (error term), and then the time series model and the extracted error term are used to predict the number of queries at the target time point.

[0078] Specifically, the query number observation value refers to the actual query number observed at a specific time point or time period, that is, the number of occurrences of a certain event or behavior at different time points.

[0079] In the embodiment of the present invention, the method of calculating the query count observation value of the queried order data at the target time point by using the preset time series model and the error term includes:

[0080] Using the time series data to fit a preset time series model, and determining a model constant term and a lag order of the fitted time series model;

[0081] The query times observation value of the queried order data at the target time point is calculated according to the model constant term, the lag order and the error term, wherein the calculation formula of the query times observation value is:

[0082]

[0083] Among them, X t is the number of query observations at the target time point t, c is the model constant term, θ i is the i-th fitting model parameter, ε t-i is the ti-th error term, and q is the lag order.

[0084] In detail, the time series data is used to fit the preset time series model (autoregressive moving average model), where the autoregressive moving average model is a model used to describe the autocorrelation and lag effect of time series data. It combines autoregression (AR) and moving average (MA). The regression part (AR) uses past observations to predict the current value, where p represents the order of the autoregressive term, and the moving average part (MA) uses the past white noise error to predict the current value, where q represents the order of the moving average term, that is, based on the time points in the time series data and the query number observations corresponding to the time points, the time series model is fitted by the least squares method. The time series model includes the model constant term c and the fitting model parameter θ. i The fitted time series model will provide the constant term and lag order of the model, and the constant term and lag order will be used for subsequent calculations. The observed value of the number of queries at the target time point can be calculated using the fitted model constant term, lag order and error term.

[0085] Specifically, based on the predicted query number observation value, the second probability distribution of the queried order data at the target time point is calculated, wherein the second probability distribution refers to the probability distribution obtained by counting the number of queried order data generated in real time at the real-time time point; and then using a fitting method (such as the least squares method), the first probability distribution and the second probability distribution are fit into a curve to obtain the probability distribution of the queried order data at different time points, that is, the normal distribution function is selected as the fitting function, and the first probability distribution is curve fitted, and the second probability distribution is curve fitted, and then the fitted first probability distribution curve and the second probability distribution curve are fit to obtain the distribution curve corresponding to the probability distribution of the queried order data at different time points.

[0086] Furthermore, the access frequency of each queried order data can be analyzed according to the probability distribution corresponding to each queried order data, and the storage of the queried order data can be optimized based on the access frequency.

[0087] S3. When the highest probability value in the probability distribution corresponding to each queried order data is less than a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as first factoring order data, and the first factoring order data is stored in the physical medium layer in the hierarchical cache.

[0088] In an embodiment of the present invention, the highest probability value in the probability distribution corresponding to each queried order data is counted. For example, the highest probability value in the probability distribution corresponding to the queried order data A is 0.4, and the highest probability value in the probability distribution corresponding to the queried order data B is 0.5, and the preset probability threshold is 0.6. The highest probability values ​​of the queried order data A and the queried order data B are both less than the probability threshold. The queried order data A and the queried order data B are counted as the queried order data in the first factoring order data set, wherein the first factoring order data refers to the data whose highest probability value corresponding to the queried order data is less than the preset probability threshold as the first factoring order data. The queried order data corresponds to the query data involving the debt repayment by the debtor in the factoring business.

[0089] In the embodiment of the present invention, refer to Figure 3 As shown, the step of aggregating the queried order data corresponding to the highest probability value into first factoring order data includes:

[0090] S31, counting the number of queried order data corresponding to the highest probability value in each probability distribution;

[0091] S32: when the number of data is greater than a preset number threshold, any queried order data corresponding to the highest probability value is selected as target queried order data, and the target queried order data is aggregated as first factoring order data;

[0092] S33. When the data quantity is equal to a preset quantity threshold, the queried order data corresponding to the highest probability value is used as the target queried order data, and the target queried order data is aggregated into the first factoring order data.

[0093] In detail, first, it is necessary to traverse each probability distribution, find the data corresponding to the highest probability value, and count its number. According to the preset quantity threshold, the number of data corresponding to the highest probability value is determined. If the number is greater than the threshold, any one of them is selected as the target queried order data; if the number is equal to the threshold, the data is directly selected as the target queried order data, and the selected target queried order data is added to the first factoring order data set, which ensures that the appropriate data is selected as the first factoring order data in each probability distribution.

[0094] For example, there are three probability distributions, each corresponding to different queried order data, and the preset quantity threshold is 1. The probability distribution corresponding to data 1 is [0.1, 0.6, 0.6], the probability distribution corresponding to data 1 is [0.2, 0.3, 0.5], and the probability distribution corresponding to data 3 is [0.3, 0.3, 0.4]. It is necessary to count the number of queried order data corresponding to the highest probability value in each probability distribution. For data 1, the highest probability value is 0.6, and the corresponding quantity threshold of the queried order data is 2; for data 2, the highest probability value is 0.5, and the corresponding The quantity threshold of the queried order data is 1; for data 2, the highest probability value is 0.4, and the corresponding quantity threshold of the queried order data is 1; if there are two time points in data 1 that have the same maximum probability value, then you only need to select any data corresponding to the two time points, where the data corresponding to the two time points are both data 1; and when only one time point has the maximum probability value, directly select data 2 as the target queried order data, then the first factoring order data is data 1, data 2 and data 3, which respectively correspond to the queried order data corresponding to the highest probability values ​​in different probability distributions.

[0095] In one of the practical application scenarios of the present invention, due to the large amount of data to be queried, when the amount of data corresponding to the query request is large, the data query will be slow, but the slow query response is likely to be caused by unreasonable data storage. Therefore, it is necessary to optimize the data storage method.

[0096] In the embodiment of the present invention, the hierarchical cache refers to improving the performance and efficiency of the system by combining storage media of different speeds and capacities. Generally, a hierarchical cache system is composed of multiple levels, and each level uses storage media of different speeds and capacities.

[0097] In the embodiment of the present invention, before storing the first factoring order data in the physical medium layer in the hierarchical cache, the method further includes:

[0098] It is divided into high-level cache structure and low-level cache structure in order from top to bottom;

[0099] Using the high-level cache structure as a cache layer, and using the low-level cache structure as a physical medium layer;

[0100] The cache layer and the physical medium layer are constructed as a hierarchical cache in a top-down order.

[0101] In detail, the high-level cache structure is a faster cache layer used to store frequently accessed data to improve access speed, and usually uses faster storage media such as memory or solid-state drives; the low-level cache structure is a cache layer with larger capacity but slower access speed, used to store less frequently accessed data, and usually uses relatively slower but larger capacity storage media such as mechanical hard drives or network storage; the high-level cache structure is then used as a cache layer for fast access to data, and the low-level cache structure is used as a physical medium layer for persistent data storage. The cache layer and the physical medium layer are constructed into a hierarchical cache in order from top to bottom, indicating that the cache layer is at the top and the physical medium layer is at the bottom to form a hierarchical structure. The hierarchical cache structure can take into account the requirements of access speed and storage capacity. The cache layer provides fast access, while the physical medium layer provides larger storage capacity and persistent data storage capabilities.

[0102] Furthermore, the first factoring order data refers to data with less frequent accesses, and needs to be stored in the lowest level of the hierarchical cache, which can release high-level cache resources so that the cache layer can be used to store more frequently accessed data, thereby improving the overall query performance.

[0103] In the embodiment of the present invention, the physical medium layer refers to a cache layer with a larger capacity but a slower access speed, which is used to store data that is not accessed so frequently, such as a hard disk.

[0104] In the embodiment of the present invention, the step of storing the first factoring order data in a physical medium layer in a hierarchical cache includes:

[0105] Using the first factoring order data as query cold data, and obtaining a storage address of the query cold data;

[0106] Allocating the query cold data to a physical medium layer in a hierarchical cache according to the storage address;

[0107] Calculate the real-time probability distribution of the queried order data according to a preset time change;

[0108] The first factoring order data is updated according to the real-time probability distribution, and the process returns to the step of using the first factoring order data as query cold data until a preset deadline is reached.

[0109] In detail, the first factoring order data refers to data with fewer access times, and the first factoring order data is used as query cold data. The query cold data refers to data with fewer access times, and a corresponding storage address is assigned to each query cold data, and then according to the obtained storage address, the query cold data is stored in the corresponding storage address in the physical medium layer in the hierarchical cache.

[0110] Specifically, monitor the access to cold data and calculate the real-time probability distribution of the queried order data based on historical access patterns and other factors. That is, use the time series model to continuously calculate the real-time probability distribution of the queried order data at different time points according to the changes in time points. According to the real-time probability distribution, update the first factoring order data regularly or as needed to ensure the timeliness and accuracy of the data, and redistribute the updated data to the physical medium layer, determine the preset deadline, and ensure that the data update and management are completed within the specified time range.

[0111] Exemplarily, the maximum probability of the queried order data A at time point t1 is 0.3. As time goes by, the maximum probability of the queried order data A at time point t2 is 0.7, and the probability threshold is 0.6. At this time, the maximum probability value of the queried order data A is greater than the probability threshold. In this case, the queried order data A needs to be screened out from the first factoring order data and no longer used as the first factoring order data. In this case, the first factoring order data needs to be updated and the updated data needs to be reallocated to the physical medium layer.

[0112] Furthermore, after analyzing the situation when the highest probability value corresponding to each queried order data is greater than or equal to the preset probability threshold, it is also necessary to analyze the situation when the highest probability value corresponding to each queried order data is greater than or equal to the preset probability threshold to ensure that all data is cached.

[0113] S4. When the highest probability value corresponding to each queried order data is greater than or equal to a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as second factoring order data, and the second factoring order data are dynamically cached according to the distribution change of the highest probability value in the probability distribution.

[0114] In an embodiment of the present invention, the highest probability value in the probability distribution corresponding to each queried order data is counted. For example, the highest probability value in the probability distribution corresponding to the queried order data A is 0.7, and the highest probability value in the probability distribution corresponding to the queried order data B is 0.8, and the preset probability threshold is 0.6, then the highest probability values ​​of the queried order data A and the queried order data B are both greater than the probability threshold, then the queried order data A and the queried order data B are counted as the queried order data in the second factoring order data set, wherein the second factoring order data refers to the data whose highest probability value corresponding to the queried order data is greater than or equal to the preset probability threshold as the second factoring order data.

[0115] In detail, the step of aggregating the queried order data corresponding to the highest probability value into the second factoring order data is consistent with the step of aggregating the queried order data corresponding to the highest probability value into the first factoring order data in S3, and will not be repeated here.

[0116] Furthermore, the cache strategy of the first factoring order data is adjusted in real time according to the change of probability distribution, and the data that is more likely to be accessed is placed in the cache, thereby reducing cache misses, reducing the number of accesses to the underlying storage, and improving the efficiency of data query.

[0117] In one of the practical application scenarios of the present invention, suppliers in the supply chain usually trade with multiple buyers, and each buyer may adopt a different factoring scheme. Through dynamic caching, the data caching strategy can be dynamically adjusted according to the access mode of the factoring orders corresponding to different buyers, so as to improve the access efficiency of the supplier's factoring orders, ensure that the supply chain manager can obtain the latest factoring orders more quickly, and conduct real-time data analysis to support decision-making and business optimization, and the supply chain manager can respond to customer inquiries and needs more quickly, improve the efficiency and satisfaction of customer service, and customers can obtain the required factoring orders more quickly, strengthen communication and cooperation with supply chain managers, optimize supply chain management and operational efficiency, and on the supply chain financial platform, dynamic caching can be used to display order data and monitor order status. For example, financial institutions can monitor the flow of funds and transaction progress in real time through the order information stored in the cache, so as to facilitate timely decision-making and management.

[0118] In the embodiment of the present invention, the step of dynamically caching the second factoring order data according to the distribution change of the highest probability value in the probability distribution includes:

[0119] Determine the cache priority of each queried order data according to the highest probability value in the probability distribution;

[0120] The queried order data with the highest cache priority is stored in a high-speed cache layer in the hierarchical cache;

[0121] Extract the probability change of the highest probability value corresponding to each queried order data in the distribution change;

[0122] updating the highest probability value in the probability distribution according to the probability change;

[0123] The cache priority corresponding to each queried order data is updated according to the updated highest probability value, and the step of storing the queried order data with the highest cache priority in the cache layer in the hierarchical cache is returned until all the queried order data are stored.

[0124] In detail, the cache priority of each queried order data is determined according to the highest probability value in the probability distribution. The data with a higher probability value has a higher cache priority. The queried order data with the highest cache priority is stored in a cache layer in the hierarchical cache. The data in the cache layer is determined according to the highest probability value in the probability distribution and is the data that is most likely to be frequently accessed.

[0125] Specifically, the probability change of the highest probability value corresponding to each queried order data is extracted from the probability distribution change updated in real time. The probability change reflects the change in the data access pattern, which can affect the cache priority of the data. The highest probability value in the probability distribution is updated according to the probability change, which can reflect the latest data access pattern so as to adjust the cache strategy; according to the updated highest probability value, the cache priority corresponding to each queried order data is updated, and the storage location of the data in the cache is adjusted to ensure that the most frequently accessed data is still located in the cache layer. Each queried order data is executed in a loop until all the queried order data are stored. The cache strategy can be dynamically adjusted according to the changes in the data access pattern to improve data query efficiency.

[0126] Furthermore, through dynamic caching, the data in the cache can be dynamically adjusted according to the real-time query mode and data access situation. Placing the most frequently accessed data in the cache and placing the less frequently accessed data in the physical media layer can make more efficient use of cache resources.

[0127] The embodiment of the present invention performs big data analysis based on the historical query records of the factoring repayment page, combines the time series model to confirm the probability distribution of different types of data being queried at different times, and then stores the factoring data in layers according to the probability distribution, stores the infrequently queried data at the bottom layer, and dynamically caches the frequently queried data according to its distribution time series according to the different time series distributions, so as to improve the overall query efficiency of the data. Therefore, the dynamic caching method, device and medium of the supply chain finance factoring order proposed by the present invention can solve the problem of low efficiency when performing data query.

[0128] like Figure 4 , which is a schematic diagram of the structure of an electronic device for implementing a dynamic caching method for supply chain finance factoring orders provided by an embodiment of the present invention.

[0129] The electronic device may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a dynamic cache program for supply chain finance factoring orders.

[0130] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory 11 (for example, a dynamic cache program for executing supply chain finance factoring orders, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device and process data.

[0131] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 can also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the dynamic cache program of the supply chain financial factoring order, but also can be used to temporarily store data that has been output or is to be output.

[0132] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0133] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0134] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0135] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0136] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0137] The dynamic cache program of the supply chain finance factoring order stored in the memory 11 in the electronic device is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0138] Extract the queried order data from the historical query records in the factoring repayment page;

[0139] Using a preset time series model to calculate the probability distribution of the queried order data being queried at different time points;

[0140] When the highest probability value in the probability distribution corresponding to each queried order data is less than a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as first factoring order data, and the first factoring order data is stored in the physical medium layer in the hierarchical cache;

[0141] When the highest probability value corresponding to each queried order data is greater than or equal to a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as second factoring order data, and the second factoring order data are dynamically cached according to the distribution change of the highest probability value in the probability distribution.

[0142] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0143] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0144] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0145] Extract the queried order data from the historical query records in the factoring repayment page;

[0146] Using a preset time series model to calculate the probability distribution of the queried order data being queried at different time points;

[0147] When the highest probability value in the probability distribution corresponding to each queried order data is less than a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as first factoring order data, and the first factoring order data is stored in the physical medium layer in the hierarchical cache;

[0148] When the highest probability value corresponding to each queried order data is greater than or equal to a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as second factoring order data, and the second factoring order data are dynamically cached according to the distribution change of the highest probability value in the probability distribution.

[0149] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, media and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0150] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0152] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0153] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited only according to the above description, and it is intended that all changes within the meaning and scope of equivalent elements within the scope of protection are included in the present invention.

[0154] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0155] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A dynamic caching method for supply chain finance factoring orders, characterized in that: The method comprises: Extract the queried order data from the historical query records in the factoring repayment page; Using a preset time series model to calculate the probability distribution of the queried order data being queried at different time points; When the highest probability value in the probability distribution corresponding to each queried order data is less than a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as first factoring order data, and the first factoring order data is stored in the physical medium layer in the hierarchical cache; When the highest probability value corresponding to each queried order data is greater than or equal to a preset probability threshold, the queried order data corresponding to the highest probability value are aggregated as second factoring order data, and the second factoring order data are dynamically cached according to the distribution change of the highest probability value in the probability distribution.

2. The dynamic caching method for supply chain finance factoring orders according to claim 1, characterized in that: The queried order data of the historical query record in the extraction factoring repayment page includes: Time classification of the historical query records according to preset time intervals; Classify different types of query data in the historical query records after time classification; The query data of different types after classification are aggregated into the queried order data.

3. The dynamic caching method for supply chain finance factoring orders according to claim 1, characterized in that: The using a preset time series model to count the probability distribution of the queried order data being queried at different time points includes: Counting a first probability distribution of the queried order data being queried at a historical time point within a preset time interval; Generate time series data of the queried order data according to the query time point corresponding to the queried order data; Counting the error terms in the time series data according to a preset lag order; Calculate the query count observation value of the queried order data at the target time point using the preset time series model and the error term; Counting a second probability distribution of the queried order data at a target time point according to the query frequency observation value; A first curve corresponding to the first probability distribution and a second curve corresponding to the second probability distribution are fitted as probability distributions of the queried order data being queried at different time points.

4. The dynamic caching method for supply chain finance factoring orders according to claim 3, characterized in that: The method of calculating the query count observation value of the queried order data at the target time point by using the preset time series model and the error term includes: Using the time series data to fit a preset time series model, and determining a model constant term and a lag order of the fitted time series model; The query times observation value of the queried order data at the target time point is calculated according to the model constant term, the lag order and the error term, wherein the calculation formula of the query times observation value is: Among them, X t is the number of query observations at the target time point t, c is the model constant term, θ i is the i-th fitting model parameter, ε t-i is the ti-th error term, and q is the lag order.

5. The dynamic caching method for supply chain finance factoring orders according to claim 1, characterized in that: The step of aggregating the queried order data corresponding to the highest probability value into first factoring order data includes: Count the number of queried order data corresponding to the highest probability value in each probability distribution; When the data quantity is greater than a preset quantity threshold, any queried order data corresponding to the highest probability value is selected as the target queried order data, and the target queried order data is aggregated as the first factoring order data; When the data quantity is equal to a preset quantity threshold, the queried order data corresponding to the highest probability value is used as the target queried order data, and the target queried order data is aggregated into the first factoring order data.

6. The dynamic caching method for supply chain finance factoring orders according to claim 1, characterized in that: Before storing the first factoring order data in the physical medium layer in the hierarchical cache, the method further includes: It is divided into high-level cache structure and low-level cache structure in order from top to bottom; Using the high-level cache structure as a cache layer, and using the low-level cache structure as a physical medium layer; The cache layer and the physical medium layer are constructed as a hierarchical cache in a top-down order.

7. The dynamic caching method for supply chain finance factoring orders according to claim 6, characterized in that: The storing of the first factoring order data into the physical medium layer in the hierarchical cache includes: Using the first factoring order data as query cold data, and obtaining a storage address of the query cold data; Allocating the query cold data to a physical medium layer in a hierarchical cache according to the storage address; Calculate the real-time probability distribution of the queried order data according to a preset time change; The first factoring order data is updated according to the real-time probability distribution, and the process returns to the step of using the first factoring order data as query cold data until a preset deadline is reached.

8. The dynamic caching method for supply chain finance factoring orders according to claim 1, characterized in that: The dynamically caching the second factoring order data according to the distribution change of the highest probability value in the probability distribution includes: Determine the cache priority of each queried order data according to the highest probability value in the probability distribution; The queried order data with the highest cache priority is stored in a high-speed cache layer in the hierarchical cache; Extract the probability change of the highest probability value corresponding to each queried order data in the distribution change; updating the highest probability value in the probability distribution according to the probability change; The cache priority corresponding to each queried order data is updated according to the updated highest probability value, and the step of storing the queried order data with the highest cache priority in the cache layer in the hierarchical cache is returned until all the queried order data are stored.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the dynamic caching method for supply chain financial factoring orders as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the dynamic caching method for supply chain financial factoring orders as described in any one of claims 1 to 8 is implemented.

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