Information docking method based on mall applet

Abstract

The invention relates to the technical field of information docking, and particularly discloses a shopping mall applet-based information docking method, which comprises the following steps of: S1, periodically acquiring data traffic sent to a forwarding node by a service node, and setting a traffic interval based on the acquired data traffic; s2, setting a sub-interval based on the traffic interval, and determining a time period in which the data traffic belongs to the sub-interval; acquiring the data traffic consumed by a single type of service in the data traffic, recording the data traffic as sub-traffic, and determining predicted traffic based on the sub-traffic in a time period; and S3, grouping the predicted traffic, the sum of the predicted traffic in the group being less than or equal to 0.95 Q, Q representing the preset maximum data traffic received per second by the forwarding node, and configuring the forwarding node according to the group and performing information docking. According to the invention, information docking interruption caused by overlarge working pressure of a single forwarding node can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of information docking, and particularly relates to an information docking method based on a mall mini-program. Background Art

[0002] A mall mini-program generally refers to an online mall application in the form of a mini-program developed and run on platforms such as WeChat and Alipay. It is a lightweight application form that does not require downloading a separate App and can be directly used within a social communication or payment platform.

[0003] In a Chinese patent with the publication number CN114374739A, an information docking system and method are disclosed. In its solution, it mainly includes that a forwarding node receives service information and forwards it to a terminal node through a customized interface. Through this system architecture, it is avoided that there is no need to set more service nodes for one-to-one connection communication, thereby greatly reducing the complexity of the overall system and further reducing the difficulty of later maintenance.

[0004] However, in the above solution, the forwarding node needs to centrally be responsible for receiving, parsing, and forwarding all service information. As the number of terminal nodes gradually increases, the processing pressure and concurrent requirements borne by the forwarding node will also rapidly climb, and it is very likely to have performance bottlenecks or service failures, resulting in the forwarding node being paralyzed and affecting the overall operation of the system. Summary of the Invention

[0005] The purpose of the present invention is to provide an information docking method based on a mall mini-program to solve the following technical problems: The forwarding node needs to centrally be responsible for receiving, parsing, and forwarding all service information. As the number of terminal nodes gradually increases, the processing pressure and concurrent requirements borne by the forwarding node will also rapidly climb, and it is very likely to have performance bottlenecks or service failures, resulting in the forwarding node being paralyzed and affecting the overall operation of the system.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An information docking method based on a mall mini-program includes the following steps: S1: Periodically obtain the data traffic sent by a service node to a forwarding node, and set a traffic interval based on the collected data traffic; S2: Set sub-intervals based on the traffic interval, and determine the time period when the data traffic belongs to the sub-intervals; obtain the data traffic consumed by a single type of service in the data traffic, denoted as sub-traffic, and determine the predicted traffic based on the sub-traffic within the time period; S3: Group the predicted traffic. The sum of the predicted traffic in each group is less than or equal to 0.95Q, where Q represents the maximum data traffic that the forwarding node is preset to receive per second. Configure the forwarding node according to the grouping and perform information docking.

[0007] As a further solution of the present invention: In step S1, the process of setting the traffic interval specifically includes: Draw the curve f(t) of the data traffic changing with time, where t represents time. Set the maximum data traffic Q that a single forwarding node receives per second, and set the traffic interval [0, n * 0.95Q], where the number of intervals n = ⌈max(f(t)) / 0.95Q⌉, and ⌈max(f(t)) / 0.95Q⌉ represents rounding up max(f(t)) / 0.95Q.

[0008] As a further solution of the present invention: In step S2, the process of obtaining the predicted traffic specifically includes: Starting from zero, set n - 1 sub - intervals at intervals of 0.95Q within the traffic interval. Sort the sub - intervals in descending order according to the average traffic. Obtain the part of the curve f(t) whose function value belongs to the B - th sub - interval in the sorting. Determine the domain of this part, denoted as domain B. Obtain the union of domain B and denote it as domain B'. The average traffic A1 = (A2 + A3) / 2, where A2 and A3 respectively represent the lower and upper limits of the sub - interval; Obtain the data traffic consumed by a single type of service in the data traffic, denoted as sub - traffic. Draw the curve g(t) of the sub - traffic changing with time. Substitute the time i belonging to domain B' into the curve g(t) to obtain the predicted traffic C i , C i，j represents the predicted traffic of service j at time point i.

[0009] As a further solution of the present invention: In step S3, the process of grouping the predicted traffic specifically includes: Step 1: Sort the predicted traffic in descending order to obtain the traffic sorting. Obtain the splitting position h1 in the traffic sorting, and the splitting position h1 satisfies the constraint: ∑h1 ≤ 0.95Q and ∑(h1 + 1) > 0.95Q, where ∑h1 represents the sum of the first h1 predicted traffic in the traffic sorting, and ∑(h1 + 1) represents the sum of the first h1 + 1 predicted traffic in the traffic sorting; Step 2: Obtain the predicted traffic CP at the (h1 + 2) - th position in the traffic sorting h1+2 , and calculate the judgment value PD = ∑h1 + CP h1+2, if the judgment value PD ≤ 0.95Q, execute Step 3; if the judgment value PD > 0.95Q, execute Step 4; Step 3: Take the predicted traffic CP h1+2 as the grouped traffic, take the judgment value as ∑h1, and obtain the predicted traffic CP at the (h1 + 3)-th position in the traffic sorting h1+3 , calculate a new judgment value, and repeat Step 1 until it is judged whether all the predicted traffic is grouped traffic, then execute Step 5; Step 4: Do not take the predicted traffic CP h1+2 as the grouped traffic, obtain the predicted traffic CP at the (h1 + 3)-th position in the traffic sorting h1+3 , calculate a new judgment value, and repeat Step 1 until it is judged whether all the predicted traffic is grouped traffic, then execute Step 5; Step 5: Take the first h1 predicted traffic in the traffic sorting and the grouped traffic as the same group, remove the grouped predicted traffic from the traffic sorting to obtain a new traffic sorting XP1, and execute Step 1, repeat the above steps until all the predicted traffic has been grouped.

[0010] As a further solution of the present invention: In the step S4, the process of configuring the forwarding nodes according to the grouping and performing information docking specifically includes: When the time belongs to the domain B', obtain the total number M of groups, and configure M forwarding nodes. Each forwarding node is used to receive the service information of the service type corresponding to the predicted traffic in the same group.

[0011] As a further solution of the present invention: In the step one, the following steps are further included: If there is a predicted traffic x > 0.95Q, divide the predicted traffic into X = ⌈x / 0.95Q⌉ parts, where ⌈x / 0.95Q⌉ represents rounding up x / 0.95Q. Among them, X - 1 parts of the predicted traffic are 0.95Q, and the remaining one part of the predicted traffic is x - (X - 1) * 0.95Q. The remaining one part of the predicted traffic participates in obtaining the traffic sorting, and X - 1 parts of the predicted traffic are used as x - 1 groups.

[0012] As a further solution of the present invention: The process of drawing the curve of the data traffic changing with time specifically includes: Sort the data traffic in the order of the time axis to obtain the first sorting; Calculate the data traffic difference ΔK i =K i -K i-1 , when the data traffic difference ΔK iWhen ≥ ΔKys, mark the data traffic at the i-th position in the first sorting as the segmentation traffic, K i represents the data traffic at the i-th position in the first sorting, and ΔKys represents the preset data traffic difference threshold; Mark the data traffic at the first and last positions in the first sorting as the segmentation traffic. For the data traffic between two adjacent segmentation traffics in the first sorting, generate coordinate points (ty, K ty ), K ty represents the data traffic at time ty. Fit the coordinate points to obtain the fitting curve Y(t), where t represents time. Plot all the fitting curves Y(t), and connect the starting point of the j-th fitting curve and the ending point of the (j - 1)-th fitting curve with a straight line to obtain the curve f(t) of the data traffic changing with time. The (j - 1)-th fitting curve is adjacent to the j-th fitting curve, and the time point corresponding to the ending point of the (j - 1)-th fitting curve is less than the time point corresponding to the starting point of the j-th fitting curve.

[0013] Advantages of the present invention: In this solution, in step S1, by periodically collecting data traffic, sorting the data by time, calculating the traffic difference, and using the fitting method to plot the curve f(t) of the data traffic changing with time and determining the segmentation traffic, this process not only realizes the accurate capture and description of the actual business data, but also effectively extracts the key features of the data flow change through segmentation and curve fitting, providing a scientific basis for subsequent traffic interval division and refined prediction, so as to ensure that the system can dynamically adjust the processing strategy based on objective data, reduce the misjudgment risk caused by data fluctuations and reduce the possibility of the forwarding node being accidentally overloaded. At the same time, it also builds a reliable data foundation for the whole solution, directly supporting the goal of the overall stable operation of the system. On this basis, step S2 further divides the entire traffic interval into multiple sub-intervals, sorts them according to the average traffic size within the sub-intervals, and completes the prediction of the sub-traffic within each time period. In this way, not only the traffic prediction within each segmented time period is more accurate, but also the traffic of different time periods and different service types can be effectively identified and separated, providing detailed and reliable data support for subsequent grouping operations, enabling the system to more accurately estimate the load situation in actual operation, adjust and allocate resources in advance, and effectively relieve the concurrent processing pressure that may occur when the number of terminal nodes surges. Step S3 sorts the predicted traffic in descending order, accurately determines the segmentation position, and uses the judgment value PD for grouping. It not only realizes the reasonable splitting of the predicted traffic into multiple small groups not exceeding 0.95Q of the processing upper limit of the forwarding node, but also further refines the traffic magnitude through segmentation processing when a single predicted traffic exceeds the standard, so as to ensure that the traffic within each group is within the tolerable range of the forwarding node. This grouping strategy can effectively avoid performance bottlenecks or service failures of a single node due to processing too large data, thus ensuring the stability and efficient operation of the overall system, and also laying a foundation for the system to achieve flexible resource scheduling and dynamic expansion. Finally, step S4, according to the foregoing grouping results, when the time is within the domain B', counts the total number of groups and dynamically configures the corresponding number of forwarding nodes, so that each node specifically processes the corresponding service information within the same group, thus not only realizing load balancing, reducing the risk of single-node overload, but also effectively simplifying the connection management and later maintenance work between nodes. This process ensures that the system can flexibly adjust the number and distribution of forwarding nodes according to the change of actual business traffic, thereby ensuring the efficiency of the information docking process and the reliability of the overall system operation. Through refined segmentation and intelligent prediction grouping, it effectively relieves the problem of the sharp increase in the processing pressure of the forwarding node that may be caused by the increase in the number of terminal nodes, providing an efficient, stable and scalable information docking solution for the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below with reference to the accompanying drawings.

[0015] Figure 1 It is a schematic flow chart of a method for information docking based on a mall mini-program of the present invention. Detailed implementation manners

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0017] Please refer to Figure 1 As shown, the present invention is a method for information docking based on a mall mini-program, including the following steps: S1: Periodically obtain the data traffic sent from the service node to the forwarding node, and set a traffic interval based on the collected data traffic; In a preferred embodiment of the present invention, in the step S1, the process of setting the traffic interval specifically includes: Draw a curve f(t) of the data traffic changing with time, where t represents time, set the maximum data traffic Q received by each forwarding node per second, and set a traffic interval [0, n * 0.95Q], where the number of intervals n = ⌈max(f(t)) / 0.95Q⌉, and ⌈max(f(t)) / 0.95Q⌉ represents rounding up max(f(t)) / 0.95Q; It should be noted that assuming that the data traffic periodically collected from the service nodes by the system within a fixed time window is 200, 450, 800, 1100, 900, 2500, 1500 in sequence (the unit can be the number of data packets per second or other traffic measurement units), a curve f(t) is obtained after plotting these data in chronological order, where the abscissa represents time and the ordinate represents the traffic at that moment. Next, assuming that the maximum data traffic Q received by a single forwarding node per second is set to 1000 units, in order to ensure the stability and safety margin of the system operation, we take 0.95Q as the working upper limit of a single node, that is, 0.95×1000 = 950 units. On the curve f(t), we first find the maximum traffic value within this time period, that is, max(f(t)), which is 2500 units in this example. Then, according to the formula n = ⌈max(f(t)) / 0.95Q⌉, we divide 2500 by 950 to get approximately 2.63, and then round it up to get n = 3, which means that the entire traffic range needs to be divided into 3 intervals under the current collected data. Finally, the traffic interval we set is [0, n×0.95Q], that is, [0, 3×950], which is [0, 2850] units. The purpose of doing this is to divide the possible traffic change range of the system within a time window into several intervals with a fixed width according to the processing capacity of the forwarding nodes; In a preferred case of this embodiment, the process of plotting the curve of the data traffic changing with time specifically includes: Sort the data traffic in the order of the time axis to obtain the first sorting; Calculate the data traffic difference ΔK i =K i -K i-1 When the data traffic difference ΔK i ≥ΔKys, mark the data traffic at the i-th position in the first sorting as the segmentation traffic, K i represents the data traffic at the i-th position in the first sorting, and ΔKys represents the preset data traffic difference threshold; Mark the data traffic at the first and last positions in the first sorting as the segmentation traffic. For the data traffic between two adjacent segmentation traffics in the first sorting, generate coordinate points (ty, K ty ), K tyRepresents the data traffic at time ty. The coordinate points are fitted to obtain a fitted curve Y(t), where t represents time. All the fitted curves Y(t) are plotted, and the starting point of the j-th fitted curve and the ending point of the (j - 1)-th fitted curve are connected by a straight line to obtain the curve f(t) representing the change of the data traffic over time. The (j - 1)-th fitted curve is adjacent to the j-th fitted curve, and the time point corresponding to the ending point of the (j - 1)-th fitted curve is less than the time point corresponding to the starting point of the j-th fitted curve.

[0018] It should be noted that assume the business data traffic records collected within a certain time period are as follows (the recording unit can be the number of data packets or any traffic unit): 8:00 - 200, 8:01 - 220, 8:03 - 500, 8:04 - 520, 8:05 - 510, 8:06 - 300, 8:07 - 320, 8:08 - 310. First, sort the above data in chronological order to obtain the first sorting. Then, for every two adjacent data points, calculate the traffic difference ΔK i =K i -K i-1 , for example, the difference between 8:01 and 8:00 is 220 - 200 = 20, the difference between 8:02 and 8:01 is 230 - 220 = 10, the difference between 8:03 and 8:02 is 500 - 230 = 270, and so on. Assume the preset traffic difference threshold ΔKys is 100, then in this example, only ΔK4 (i.e., the difference 270 at 8:03) is greater than or equal to 100; at the same time, the data at the first (8:00, 200) and the last (8:08, 310) in the first sorting are also marked as split traffic. In this way, the split traffic points are determined as: 8:00 (200), 8:03 (500), and 8:08 (310). Subsequently, for the data points between two adjacent split traffics, corresponding coordinate points (ty, Kty) are generated. Here, the data is divided into two intervals: Interval 1: The data between 8:00 and 8:03, including (8:00, 200), (8:01, 220), (8:02, 230), and (8:03, 500); Interval 2: The data between 8:03 and 8:08, including (8:03, 500), (8:04, 520), (8:05, 510), (8:06, 300), (8:07, 320), and (8:08, 310); For the coordinate points within each interval, a curve fitting method (such as linear regression or polynomial fitting) can be used to obtain a fitting curve Y(t). Suppose the curve Y1(t) is obtained by fitting the first interval, which can describe the change trend of the traffic volume from 8:00 to 8:03; and the curve Y2(t) is obtained by fitting the second interval, which describes the traffic volume change from 8:03 to 8:08. Finally, all the fitting curves are plotted in the same coordinate system, and the end point of the fitting curve Y1(t) of the first interval at 8:03 is connected to the start point of the fitting curve Y2(t) of the second interval at 8:03 with a straight line to ensure the continuity of the two curves in time and the connection conforms to the data trend. Ultimately, all these fitting curves and the connecting straight lines are combined to form the overall f(t) curve, which reflects the change of the data traffic volume during the entire time period; It should be noted that by pre-marking the segmentation points in the data, the overall data is divided into multiple locally relatively stable intervals, and then each interval is fitted separately. In this way, a relatively simple model (such as linear or low-degree polynomial) can be used to describe the change trend of each local data, thereby avoiding the increase in computational complexity and the risk of overfitting caused by fitting the global data with high-degree or complex models. Piecewise fitting not only reduces the complexity of the overall fitting, but also makes the trend of each segment of data more obvious and the fitting effect more stable. Finally, by connecting the fitting results of each segment with a straight line, it not only ensures the continuity of the overall trend but also realizes a more efficient and accurate traffic prediction.

[0019] S2: Set sub-intervals based on the described traffic volume intervals, determine the time period when the data traffic belongs to the sub-intervals; obtain the data traffic consumed by a single type of service in the described data traffic, denoted as sub-traffic, and determine the predicted traffic based on the sub-traffic within the time period; In a preferred case of this embodiment, in the step S2, the process of obtaining the predicted traffic specifically includes: Starting from zero, set n - 1 sub-intervals at an interval of 0.95Q within the described traffic volume interval, sort the sub-intervals in descending order according to the average traffic volume, obtain the part of the curve f(t) whose function value belongs to the B-th sub-interval in the sorting, determine the domain of this part, denoted as domain B, obtain the union of the domain B and denote it as domain B', and the average traffic volume A1 = (A2 + A3) / 2, where A2 and A3 respectively represent the lower and upper limits of the sub-interval; Obtain the data traffic consumed by a single type of service in the described data traffic, denoted as sub-traffic, plot the curve g(t) of the change of the sub-traffic with time, substitute the time i belonging to the domain B' into the curve g(t), and obtain the predicted traffic C at the time point i i , C i，j represents the predicted traffic of service j at the time point i; It is worth noting that, first, the traffic intervals are divided from scratch at intervals of 950. Theoretically, it can be divided into three sub-intervals: [0, 950], [950, 1900], and [1900, 2850]. These three sub-intervals are sorted in descending order according to their respective average traffic sizes. The calculation formula for the average traffic A1 is A1 = (A2 + A3) / 2, where A2 and A3 represent the lower and upper limits of the sub-interval respectively. Therefore, the average traffic of the first sub-interval is (0 + 950) / 2 = 475, the second sub-interval is (950 + 1900) / 2 = 1425, and the third sub-interval is (1900 + 2850) / 2 = 2375. After sorting in descending order, the sub-interval order is the third ([1900, 2850], average 2375), the second ([950, 1900], average 1425), and the first ([0, 950], average 475). Suppose when selecting the B-th sub-interval in the selection sort, B = 2, that is, the second sub-interval [950, 1900] is selected. Next, the part of the function value on the curve f(t) that falls within this interval is extracted. For example, if the f(t) values obtained from continuous sampling are: 400 at t = 1, 1600 at t = 2, 1600 at t = 3, and 2500 at t = 4, then the domain B is [1, 3]. There may be multiple conforming domains, so the union is used as the domain B'. At the same time, in the system, the consumption of a single type of service in the entire data traffic is also extracted, denoted as sub-traffic. For example, for service j, its sub-traffic in the same time period may be 100 at t = 1, 150 at t = 2, 200 at t = 3, 300 at t = 4, 250 at t = 5, and 120 at t = 6. These data points form the curve g(t) of the sub-traffic changing with time. Then, substitute the corresponding time points into g(t) within the domain B' to obtain the corresponding predicted traffic: for example, the predicted traffic C2 at t = 2 is C2 = g(2). S3: Group the predicted traffic. The total sum of the predicted traffic in the group is less than or equal to 0.95Q, where Q represents the maximum data traffic received per second preset by the forwarding node. Configure the forwarding node according to the group and perform information docking. In a preferred case of this embodiment, in step S3, the process of grouping the predicted traffic specifically includes: Step 1: Sort the predicted traffic in descending order to obtain the traffic sorting, and obtain the splitting position h1 in the traffic sorting. The splitting position h1 satisfies the constraint: ∑h1 ≤ 0.95Q and ∑(h1 + 1) > 0.95Q, where ∑h1 represents the total sum of the first h1 predicted traffic in the traffic sorting, and ∑(h1 + 1) represents the total sum of the first h1 + 1 predicted traffic in the traffic sorting. Step 2: Obtain the predicted traffic CP at the (h1 + 2)-th position in the traffic sorting h1+2 , and calculate the judgment value PD = ∑h1 + CP h1+2 . If the judgment value PD ≤ 0.95Q, execute Step 3; if the judgment value PD > 0.95Q, execute Step 4; Step 3: Take the predicted traffic CP h1+2 as the grouped traffic, take the judgment value as ∑h1, and obtain the predicted traffic CP at the (h1 + 3)-th position in the traffic sorting h1+3 , calculate the new judgment value, and repeat Step 1 until it is judged whether all the predicted traffic is grouped traffic, then execute Step 5; Step 4: Do not take the predicted traffic CP h1+2 as the grouped traffic, obtain the predicted traffic CP at the (h1 + 3)-th position in the traffic sorting h1+3 , calculate the new judgment value, and repeat Step 1 until it is judged whether all the predicted traffic is grouped traffic, then execute Step 5; Step 5: Take the first h1 predicted traffic and the grouped traffic in the traffic sorting as the same group, remove the grouped predicted traffic from the traffic sorting to obtain a new traffic sorting XP1, and execute Step 1, repeat the above steps until all the predicted traffic has been grouped.

[0020] In a preferred case of this embodiment, in Step 1, the following steps are further included: If there is a predicted traffic x > 0.95Q, divide the predicted traffic into X = ⌈x / 0.95Q⌉ parts, where ⌈x / 0.95Q⌉ represents rounding up x / 0.95Q. Among them, the predicted traffic of X - 1 parts is 0.95Q, and the predicted traffic of the remaining one part is x - (X - 1) * 0.95Q. The predicted traffic of the remaining one part participates in obtaining the traffic sorting, and the predicted traffic of X - 1 parts is used as x - 1 groups; In a preferred case of this embodiment, in Step S4, the process of configuring forwarding nodes according to the grouping and performing information docking specifically includes: When the time belongs to the domain B', obtain the total number M of groups, and configure M forwarding nodes. Each forwarding node is used to receive the service information of the service type corresponding to the predicted traffic in the same group.

[0021] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. An information docking method based on a mall mini-program, characterized in that, It includes the following steps: S1: Periodically obtain the data traffic sent from the service node to the forwarding node, and set a traffic range based on the collected data traffic; S2: Set sub-ranges based on the traffic range, determine the time period when the data traffic belongs to the sub-range; obtain the data traffic consumed by a single type of service in the data traffic, denoted as sub-traffic, and determine the predicted traffic based on the sub-traffic within the time period; S3: Group the predicted traffic, and the sum of the predicted traffic in the group is less than or equal to 0.95Q, where Q represents the maximum data traffic received by the forwarding node per second preset. Configure the forwarding node according to the group and perform information docking.

2. The information docking method based on a mall mini-program according to claim 1, wherein In step S1, the process of setting the traffic range specifically includes: Draw the curve f(t) of the data traffic changing with time, where t represents time, set the maximum data traffic Q received by a single forwarding node per second, and set the traffic range [0, n*0.95Q], where the number of intervals n = ⌈max(f(t)) / 0.95Q⌉, and ⌈max(f(t)) / 0.95Q⌉ represents rounding up max(f(t)) / 0.95Q.

3. The information docking method based on a mall mini-program according to claim 2, wherein In step S2, the process of obtaining the predicted traffic specifically includes: Starting from zero, set n - 1 sub-ranges at intervals of 0.95Q within the traffic range, sort the sub-ranges in descending order according to the average traffic, obtain the part of the curve f(t) whose function value belongs to the B-th sub-range in the sorting, determine the domain of this part, denoted as domain B, and obtain the union of domain B, denoted as domain B'. The average traffic A1 = (A2 + A3) / 2, where A2 and A3 respectively represent the lower and upper limits of the sub-range; Obtain the data traffic consumed by a single type of service in the said data traffic, denoted as sub-traffic, plot the curve g(t) of the said sub-traffic varying with time, substitute the time i belonging to the domain B' into the curve g(t), and obtain the predicted traffic C at the time point i. i , C i，j represents the predicted traffic of service j at the time point i.

4. The information docking method based on a mall mini-program according to claim 3, wherein In step S3, the process of grouping the predicted traffic specifically includes: Step 1: Sort the predicted traffic in descending order to obtain a traffic sorting, and obtain the splitting position h1 in the traffic sorting. The splitting position h1 satisfies the constraint: ∑h1 ≤ 0.95Q and ∑(h1 + 1) > 0.95Q, where ∑h1 represents the sum of the first h1 predicted traffic in the traffic sorting, and ∑(h1 + 1) represents the sum of the first h1 + 1 predicted traffic in the traffic sorting; Step 2: Obtain the predicted traffic CP at the (h1 + 2)-th position in the traffic ranking h1+2 , and calculate the judgment value PD = ∑h1 + CP h1+2 , if the judgment value PD ≤ 0.95Q, execute Step 3; if the judgment value PD > 0.95Q, execute Step 4; Step 3: Take the predicted traffic CP h1+2 as the grouped traffic, take the judgment value as ∑h1, and obtain the predicted traffic CP at the (h1 + 3)-th position in the traffic sorting h1+3 , calculate a new judgment value, and repeat Step 1 until it is judged whether all the predicted traffic is grouped traffic, and then execute Step 5; Step 4: Do not use the predicted traffic CP h1+2 As the grouped traffic, obtain the predicted traffic CP at the (h1 + 3)-th position in the above traffic sorting h1+3 , calculate a new judgment value, and repeat Step 1 until it is judged whether all the predicted traffic is grouped traffic, and then execute Step 5; Step 5: Group the first h1 predicted traffic and the grouped traffic in the traffic sorting as the same group, remove the grouped predicted traffic from the traffic sorting to obtain a new traffic sorting XP1, and execute step 1. Repeat the above steps until all the predicted traffic has been grouped.

5. The information docking method based on a mall mini-program according to claim 4, wherein In step S4, the process of configuring the forwarding node according to the group and performing information docking specifically includes: When the time belongs to domain B', obtain the total number M of groups, and configure M forwarding nodes. A single forwarding node is used to receive the service information of the service type corresponding to the predicted traffic in the same group.

6. The information docking method based on a mall mini-program according to claim 5, characterized in that, In step 1, the following steps are also included: If there is a predicted traffic volume \(x>0.95Q\), divide the predicted traffic volume into \(X = \lceil x / 0.95Q\rceil\) parts, where \(\lceil x / 0.95Q\rceil\) represents rounding up \(x / 0.95Q\). Among them, the predicted traffic volume of \(X - 1\) parts is \(0.95Q\), and the predicted traffic volume of the remaining one part is \(x-(X - 1)\times0.95Q\). The predicted traffic volume of the remaining one part participates in obtaining the traffic volume ranking, and the predicted traffic volume of \(X - 1\) parts is used as \(x - 1\) groups.

7. A method for information docking based on a mall mini-program according to claim 6, characterized in that The process of drawing the curve of the data traffic volume changing with time specifically includes: Sort the data traffic volume in the order of the time axis to obtain the first sorting; Calculate the data traffic difference ΔK i =K i -K i-1 When the data traffic difference ΔK i ≥ΔKys, mark the data traffic at the i-th position in the first sorting as the segmentation traffic, K i represents the data traffic at the i-th position in the first sorting, and ΔKys represents the preset data traffic difference threshold; Mark the data flows at the beginning and the end in the first sorting as split flows. For the data flows between two adjacent split flows in the first sorting, generate coordinate points (ty, K ty ), where K ty represents the data flow at time ty. Fit the coordinate points to obtain a fitting curve Y(t), where t represents time. Plot all the fitting curves Y(t), and connect the starting point of the j-th fitting curve and the ending point of the (j - 1)-th fitting curve with a straight line to obtain the curve f(t) showing the change of the data flow over time. The (j - 1)-th fitting curve is adjacent to the j-th fitting curve, and the time point corresponding to the ending point of the (j - 1)-th fitting curve is less than the time point corresponding to the starting point of the j-th fitting curve.

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