Information docking method based on mall applet

Abstract

The application relates to the technical field of information docking, and particularly discloses an information docking method based on a mall applet, S1: periodically acquiring data flow sent by a service node to a forwarding node, and setting a flow interval based on the collected data flow; S2: setting a subinterval based on the flow interval, determining a time period in which the data flow belongs to the subinterval, acquiring data flow consumed by a single type of service in the data flow, denoted as subflow, and determining predicted flow based on the subflow in the time period; S3: grouping the predicted flow, wherein the total sum of the predicted flow in the group is less than or equal to 0.95Q, Q represents the maximum data flow received per second of the forwarding node, and the forwarding node is configured and information docking is performed according to the group. The application can avoid information docking interruption caused by excessive work pressure of a single forwarding node.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information docking, and particularly relates to an information docking method based on a mall applet. BACKGROUND

[0002] The mall applet generally refers to an online mall application developed and run on a WeChat, Alipay or the like platform in a small program version, which is a lightweight application form that can be directly used in a social communication or payment platform without downloading an independent app.

[0003] A Chinese patent with a patent publication number CN114374739A discloses an information docking system and method, which mainly includes a forwarding node receiving service information and forwarding the same to a terminal node through a customized interface. Through this system architecture, more service nodes do not need to be set up 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 scheme, the forwarding node needs to be responsible for receiving, analyzing and forwarding all service information. As the number of terminal nodes gradually increases, the processing pressure and concurrent demand borne by the forwarding node will also rapidly increase, which may cause a performance bottleneck or service failure, leading to the paralysis of the forwarding node and affecting the overall operation of the system. SUMMARY

[0005] The present application aims to provide an information docking method based on a mall applet, which solves the following technical problems:

[0006] The forwarding node needs to be responsible for receiving, analyzing and forwarding all service information. As the number of terminal nodes gradually increases, the processing pressure and concurrent demand borne by the forwarding node will also rapidly increase, which may cause a performance bottleneck or service failure, leading to the paralysis of the forwarding node and affecting the overall operation of the system.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] An information docking method based on a mall applet, comprising the following steps:

[0009] S1: periodically acquiring data traffic sent by a service node to a forwarding node, and setting a traffic interval based on the acquired data traffic;

[0010] S2: setting a sub-interval based on the traffic interval, determining a time period in which the data traffic belongs to the sub-interval, acquiring data traffic consumed by a single type of service in the data traffic, denoted as sub-traffic, and determining a predicted traffic based on the sub-traffic in the time period;

[0011] 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 that the forwarding node is preset to receive per second. Configure the forwarding node according to the group and perform information docking.

[0012] As a further aspect of the present invention: the process of setting the flow range in step S1 specifically includes:

[0013] Plot the curve f(t) of the data flow over time, where t represents time. Set the maximum data flow Q that a single forwarding node can receive per second. Set the flow range [0, n*0.95Q], where the number of intervals n = ⌈max(f(t)) / 0.95Q⌉, where ⌈max(f(t)) / 0.95Q⌉ represents rounding up max(f(t)) / 0.95Q.

[0014] As a further aspect of the present invention: the process of obtaining the predicted traffic in step S2 specifically includes:

[0015] Starting from zero, within the aforementioned flow range, n-1 sub-intervals are set at intervals of 0.95Q. The sub-intervals are sorted in descending order according to the average flow rate. The part of the function value on the curve f(t) that belongs to the Bth sub-interval in the sorting is obtained, and the domain of this part is determined, denoted as domain B. The union of the domains B is denoted as domain B'. The average flow rate A1 = (A2 + A3) / 2, where A2 and A3 represent the lower limit and upper limit of the sub-interval, respectively.

[0016] Obtain the data traffic consumed by a single type of service within the data traffic, denoted as sub-traffic. Plot the curve g(t) of the sub-traffic changing over time. Substitute the time i within the domain B' into the curve g(t) to obtain the predicted traffic C at time point i. i C i，j This represents the predicted traffic for service j at time point i.

[0017] As a further aspect of the present invention: the process of grouping the predicted traffic in step S3 specifically includes:

[0018] Step 1: Sort the predicted flows in descending order of size to obtain the flow sort. Obtain the split position h1 in the flow sort. The split position h1 satisfies the constraints: ∑h1≤0.95Q and ∑(h1+1)>0.95Q. ∑h1 represents the sum of the first h1 predicted flows in the flow sort, and ∑(h1+1) represents the sum of the first h1+1 predicted flows in the flow sort.

[0019] 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, proceed to step three; if the judgment value PD > 0.95Q, proceed to step four.

[0020] Step 3: Calculate the predicted traffic CP h1+2 As grouped traffic, the judgment value is used as ∑h1, and the predicted traffic CP at the (h1+3)th position in the traffic sorting is obtained. h1+3 Calculate the new judgment value and repeat step one until it is determined whether all predicted traffic is grouped traffic, then proceed to step five;

[0021] Step 4: Do not include predicted traffic CP h1+2 As a group of traffic, obtain the predicted traffic CP at the h1+3rd position in the traffic sorting. h1+3 Calculate the new judgment value and repeat step one until it is determined whether all predicted traffic is grouped traffic, then proceed to step five;

[0022] Step 5: Take the first h1 predicted flows in the traffic sort and the grouped flows as the same group, remove the grouped predicted flows from the traffic sort, obtain a new traffic sort XP1, and execute Step 1. Repeat the above steps until all predicted flows have been grouped.

[0023] As a further aspect of the present invention: in step S4, the process of configuring forwarding nodes according to the group and performing information docking specifically includes:

[0024] When the time belongs to the defined domain B', the total number of packets M is obtained, and M forwarding nodes are configured. Each forwarding node is used to receive service information of the service type corresponding to the predicted traffic in the same packet.

[0025] As a further aspect of the present invention: step one further includes the following step:

[0026] If there is a predicted flow x > 0.95Q, the predicted flow is divided into X = ⌈x / 0.95Q⌉ parts, where ⌈x / 0.95Q⌉ represents rounding up x / 0.95Q. The predicted flow of X-1 parts is 0.95Q, and the predicted flow of the remaining part is x - (X-1) * 0.95Q. The remaining part of the predicted flow participates in the flow sorting, and the predicted flow of X-1 parts is used as x-1 groups.

[0027] As a further aspect of the present invention: the process of plotting the curve of data flow changing over time specifically includes:

[0028] The data traffic is sorted according to the timeline to obtain the first sort;

[0029] Calculate the data flow difference ΔK i =K i -K i-1 When the data traffic difference ΔK i When ≥ΔKys, the data flow of the i-th position in the first sort is marked as the segmented flow, K i This represents the data flow of the i-th position in the first sorting, and ΔKys represents the preset data flow difference threshold.

[0030] The first and last data flows in the first sort are marked as segmented flows. For the data flows in the first sort that are between two adjacent segmented flows, coordinate points (ty, K) are generated. ty ), K ty Let ty represent the data flow over time. Fit the coordinate points to obtain the fitted curve Y(t), where t represents time. Plot all the fitted curves Y(t), and connect the starting point of the j-th fitted curve and the ending point of the (j-1)-th fitted curve with a straight line to obtain the curve f(t) representing the data flow 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.

[0031] The beneficial effects of this invention are as follows: In this solution, step S1 involves periodically collecting data traffic, sorting the data by time, calculating the traffic difference, and using a fitting method to plot the curve f(t) of data traffic changing over time and determine the segmented traffic. This process not only achieves accurate capture and characterization of actual business data, but also effectively extracts key features of data flow changes through segmentation and curve fitting, providing a scientific basis for subsequent division of traffic intervals and refined prediction. This ensures that the system can dynamically adjust its processing strategy based on objective data, reducing the risk of misjudgment caused by data fluctuations and lowering the possibility of forwarding nodes being unexpectedly overloaded. It also builds a reliable data foundation for the entire solution, directly supporting the goal of stable overall system operation. Step S2 further subdivides the entire traffic interval into multiple sub-intervals, sorts them according to the average traffic volume within each sub-interval, and predicts the sub-traffic for each time period. This method not only makes traffic prediction more accurate for each subdivided time period but also effectively identifies and separates traffic for different time periods and different business types. This provides detailed and reliable data support for subsequent grouping operations, enabling the system to more accurately predict load conditions during actual operation, adjust and allocate resources in advance, and effectively alleviate the concurrent processing pressure that may occur when the number of terminal nodes surges. Step S3, by arranging the predicted traffic in descending order, accurately determining the segmentation position, and using the judgment value PD for grouping, not only reasonably splits the predicted traffic into multiple small groups not exceeding the processing limit of 0.95Q for forwarding nodes but also further refines the traffic volume through segmentation when a single predicted traffic exceeds the limit. This ensures that the traffic within each group is within the capacity of the forwarding node. This grouping strategy effectively avoids performance bottlenecks or service failures caused by a single node processing too much data, thus ensuring the stability and efficient operation of the overall system. It also lays the foundation for flexible resource scheduling and dynamic expansion of the system. Finally, step S4, based on the aforementioned grouping results, counts the total number of groups and dynamically configures the corresponding number of forwarding nodes when the time falls within the defined domain B'. This allows each node to specialize in processing the corresponding business information within the same group, thereby achieving load balancing, reducing the risk of single-node overload, and effectively simplifying connection management and subsequent maintenance between nodes. This process ensures that the system can flexibly adjust the number and distribution of forwarding nodes according to changes in actual business traffic, thus guaranteeing the efficiency of the information connection process and the reliability of the overall system operation. Through refined segmentation and intelligent prediction of groups, the problem of a surge in processing pressure on forwarding nodes that may occur with the increase in terminal nodes is effectively alleviated, providing the system with an efficient, stable, and scalable information connection solution. Attached Figure Description

[0032] The invention will now be further described with reference to the accompanying drawings.

[0033] Figure 1 This is a flowchart illustrating an information integration method based on an e-commerce mini-program according to the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Please see Figure 1 As shown, this invention is an information integration method based on an e-commerce mini-program, comprising the following steps:

[0036] S1: Periodically acquire the data traffic sent from the service node to the forwarding node, and set the traffic range based on the acquired data traffic;

[0037] In a preferred embodiment of the present invention, step S1, the process of setting the flow range specifically includes:

[0038] Plot the curve f(t) of the data flow over time, where t represents time. Set the maximum data flow Q that a single forwarding node can receive per second. Set the flow range [0, n*0.95Q], where the number of intervals n = ⌈max(f(t)) / 0.95Q⌉, where ⌈max(f(t)) / 0.95Q⌉ represents rounding up max(f(t)) / 0.95Q.

[0039] It should be noted that, assuming the system periodically collects data traffic from the service nodes within a fixed time window, the numbers are 200, 450, 800, 1100, 900, 2500, and 1500 (the unit can be packets per second or other traffic measurement units). Plotting this data in chronological order yields a curve f(t), where the horizontal axis represents time and the vertical axis represents the traffic volume at that moment. Next, assuming the maximum data traffic Q received by a single forwarding node is set to 1000 units per second, to ensure system stability and safety margin, we take 0.95Q as the upper limit of a single node's workload, i.e., 0.95 × 1000 = 950 units. On the curve f(t), we first find the maximum traffic value within this time period, i.e., max(f(t)), which in this example is 2500 units. 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 under the current data collection, the entire traffic range needs to be divided into 3 intervals. Finally, we set the traffic interval to [0, n×0.95Q], which is [0, 3×950], or [0, 2850] units. The purpose of doing this is to divide the range of traffic changes that may occur in the system within a time window into several fixed-width intervals based on the processing capacity of the forwarding nodes;

[0040] In a preferred embodiment, the process of plotting the curve of data flow changing over time specifically includes:

[0041] The data traffic is sorted according to the timeline to obtain the first sort;

[0042] Calculate the data flow difference ΔK i =K i -K i-1 When the data traffic difference ΔK i When ≥ΔKys, the data flow of the i-th position in the first sort is marked as the segmented flow, K i This represents the data flow of the i-th position in the first sorting, and ΔKys represents the preset data flow difference threshold.

[0043] The first and last data flows in the first sort are marked as segmented flows. For the data flows in the first sort that are between two adjacent segmented flows, coordinate points (ty, K) are generated. ty ), K tyLet ty represent the data flow over time. Fit the coordinate points to obtain the fitted curve Y(t), where t represents time. Plot all the fitted curves Y(t), and connect the starting point of the j-th fitted curve and the ending point of the (j-1)-th fitted curve with a straight line to obtain the curve f(t) representing the data flow 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.

[0044] It should be noted that, assuming the business data traffic records collected within a certain time period are as follows (the unit of recording 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, firstly, sort the above data in chronological order to obtain the first sort. Next, for each pair of 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. Assuming the preset flow difference threshold ΔKys is 100, in this example only ΔK4 (i.e., the difference of 270 at 8:03) is greater than or equal to 100; at the same time, the first (8:00, 200) and the last (8:08, 310) data in the first sort are also marked as split flows. Thus, the split flow points are determined as: 8:00 (200), 8:03 (500) and 8:08 (310). Subsequently, for the data points between two adjacent split flows, the corresponding coordinate points (ty, Kty) are generated. Here, the data is divided into two intervals: Interval 1: data from 8:00 to 8:03, including (8:00, 200), (8:01, 220), (8:02, 230), and (8:03, 500); Interval 2: data from 8:03 to 8:08, including (8:03, 500), (8:04, 520), (8:05, 510), (8:06, 300), (8:07, 320), and (8:08, 310).

[0045] For each coordinate point within an interval, a curve fitting method (such as linear regression or polynomial fitting) can be used to obtain a fitted curve Y(t). Suppose that the curve Y1(t) is obtained by fitting the first interval, which can describe the trend of flow change from 8:00 to 8:03; and the curve Y2(t) is obtained by fitting the second interval, which describes the flow change from 8:03 to 8:08. Finally, all the fitted curves are plotted on the same coordinate system, and the endpoint of the fitted curve Y1(t) at 8:03 in the first interval and the starting point of the fitted curve Y2(t) at 8:03 in the second interval are connected by a straight line to ensure that the two curves are continuous in time and the connection point conforms to the data trend. Finally, all these fitted curves and connecting lines are combined to form the overall f(t) curve, which reflects the change of data flow over the entire time period.

[0046] It is worth noting that by pre-labeling the segmentation points in the data, the overall data is divided into multiple relatively stable local intervals. Then, each interval is fitted separately. This allows the use of simpler models (such as linear or low-order polynomials) to describe the changing trends of each local data point, thus avoiding the increased computational complexity and overfitting risk associated with fitting high-order or complex models to the global data. Segmented fitting not only reduces the overall fitting complexity but also makes the trends of each segment more obvious and the fitting effect more stable. Finally, connecting the fitting results of each segment with a straight line ensures the continuity of the overall trend and achieves more efficient and accurate traffic prediction.

[0047] S2: Set sub-intervals based on the traffic intervals, and determine the time period in which the data traffic belongs to the sub-intervals; obtain the data traffic consumed by a single type of service in the data traffic, and record it as sub-traffic; determine the predicted traffic based on the sub-traffic within the time period.

[0048] In a preferred embodiment, step S2, the process of obtaining the predicted traffic flow, specifically includes:

[0049] Starting from zero, within the aforementioned flow range, n-1 sub-intervals are set at intervals of 0.95Q. The sub-intervals are sorted in descending order according to the average flow rate. The part of the function value on the curve f(t) that belongs to the Bth sub-interval in the sorting is obtained, and the domain of this part is determined, denoted as domain B. The union of the domains B is denoted as domain B'. The average flow rate A1 = (A2 + A3) / 2, where A2 and A3 represent the lower limit and upper limit of the sub-interval, respectively.

[0050] Obtain the data traffic consumed by a single type of service within the data traffic, denoted as sub-traffic. Plot the curve g(t) of the sub-traffic changing over time. Substitute the time i within the domain B' into the curve g(t) to obtain the predicted traffic C at time point i. i C i，j This represents the predicted traffic of service j at time point i;

[0051] It's worth noting that, starting from zero, the flow rate is divided into intervals of 950. Theoretically, this can be divided into three sub-intervals: [0, 950], [950, 1900], and [1900, 2850]. These three sub-intervals are then sorted in descending order according to their average flow rate. The formula for calculating the average flow rate A1 is A1 = (A2 + A3) / 2, where A2 and A3 represent the lower and upper limits of the sub-interval, respectively. Therefore, the average flow rate 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 = 1425. 2375; After sorting in descending order, the sub-intervals are the third ([1900, 2850], average 2375), the second ([950, 1900], average 1425), and the first ([0, 950], average 475). Assuming we select the Bth sub-interval in the sorting and let B=2, that is, select the second sub-interval [950, 1900]; Next, extract the part of the function value falling within this interval from the curve f(t). For example, if the f(t) values ​​obtained from continuous sampling are: 400 at t=1, 1600 at t=2, ... If t=3 is 1600 and t=4 is 2500, then the domain B is [1,3]. There may be multiple domains that meet the criteria, so the union is taken as the domain B'. At the same time, the system will also extract the consumption of a single type of business in the entire data traffic and record it as sub-traffic. For example, for business 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 constitute the curve g(t) of sub-traffic changing with time. Then, the corresponding time points are substituted into g(t) in the domain B' to obtain the corresponding predicted traffic: for example, the predicted traffic C2 = g(2) at t=2.

[0052] 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 that the forwarding node can receive per second. Configure the forwarding node and perform information docking according to the group.

[0053] In a preferred embodiment, step S3, the process of grouping the predicted traffic, specifically includes:

[0054] Step 1: Sort the predicted flows in descending order of size to obtain the flow sort. Obtain the split position h1 in the flow sort. The split position h1 satisfies the constraints: ∑h1≤0.95Q and ∑(h1+1)>0.95Q. ∑h1 represents the sum of the first h1 predicted flows in the flow sort, and ∑(h1+1) represents the sum of the first h1+1 predicted flows in the flow sort.

[0055] 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, proceed to step three; if the judgment value PD > 0.95Q, proceed to step four.

[0056] Step 3: Calculate the predicted traffic CP h1+2 As grouped traffic, the judgment value is used as ∑h1, and the predicted traffic CP at the (h1+3)th position in the traffic sorting is obtained. h1+3 Calculate the new judgment value and repeat step one until it is determined whether all predicted traffic is grouped traffic, then proceed to step five;

[0057] Step 4: Do not include predicted traffic CP h1+2 As a group of traffic, obtain the predicted traffic CP at the h1+3rd position in the traffic sorting. h1+3 Calculate the new judgment value and repeat step one until it is determined whether all predicted traffic is grouped traffic, then proceed to step five;

[0058] Step 5: Take the first h1 predicted flows in the traffic sort and the grouped flows as the same group, remove the grouped predicted flows from the traffic sort, obtain a new traffic sort XP1, and execute Step 1. Repeat the above steps until all predicted flows have been grouped.

[0059] In a preferred embodiment, step one further includes the following steps:

[0060] If there is a predicted flow x > 0.95Q, the predicted flow is divided into X = ⌈x / 0.95Q⌉ parts, where ⌈x / 0.95Q⌉ represents rounding up x / 0.95Q. The predicted flow of X-1 parts is 0.95Q, and the predicted flow of the remaining part is x - (X-1) * 0.95Q. The remaining part of the predicted flow participates in the flow sorting, and the predicted flow of X-1 parts is used as x-1 groups.

[0061] In a preferred embodiment, step S4, which involves configuring forwarding nodes according to the group and performing information interconnection, specifically includes:

[0062] When the time belongs to the defined domain B', the total number of packets M is obtained, and M forwarding nodes are configured. Each forwarding node is used to receive service information of the service type corresponding to the predicted traffic in the same packet.

[0063] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for information integration based on an e-commerce mini-program, characterized in that, Includes the following steps: S1: Periodically acquire the data traffic sent from the service node to the forwarding node, and set the traffic range based on the acquired data traffic; S2: Set sub-intervals based on the traffic intervals, and determine the time period in which the data traffic belongs to the sub-intervals; obtain the data traffic consumed by a single type of service in the data traffic, and record it as sub-traffic; 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 that the forwarding node can receive per second. Configure the forwarding node and perform information docking according to the group. The process of setting a traffic range specifically includes: Plot the curve f(t) of the data flow over time, where t represents time. Set the maximum data flow Q that a single forwarding node can receive per second. Set the flow range [0, n*0.95Q], where the number of intervals n = ⌈max(f(t)) / 0.95Q⌉, where ⌈max(f(t)) / 0.95Q⌉ represents rounding up max(f(t)) / 0.95Q. The process of obtaining the predicted traffic specifically includes: Starting from zero, within the aforementioned flow range, n-1 sub-intervals are set at intervals of 0.95Q. The sub-intervals are sorted in descending order according to the average flow rate. The part of the function value on the curve f(t) that belongs to the Bth sub-interval in the sorting is obtained, and the domain of this part is determined, denoted as domain B. The union of the domains B is denoted as domain B'. The average flow rate A1 = (A2 + A3) / 2, where A2 and A3 represent the lower limit and upper limit of the sub-interval, respectively. Obtain the data traffic consumed by a single type of service within the data traffic, denoted as sub-traffic. Plot the curve g(t) of the sub-traffic changing over time. Substitute the time i within the domain B' into the curve g(t) to obtain the predicted traffic C at time point i. i C i，j This represents the predicted traffic of service j at time point i; The process of grouping the predicted traffic specifically includes: Step 1: Sort the predicted flows in descending order of size to obtain the flow sort. Obtain the split position h1 in the flow sort. The split position h1 satisfies the constraints: ∑h1≤0.95Q and ∑(h1+1)>0.95Q. ∑h1 represents the sum of the first h1 predicted flows in the flow sort, and ∑(h1+1) represents the sum of the first h1+1 predicted flows in the flow sort. 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, proceed to step three; if the judgment value PD > 0.95Q, proceed to step four. Step 3: Calculate the predicted traffic CP h1+2 As grouped traffic, the judgment value is used as ∑h1, and the predicted traffic CP at the (h1+3)th position in the traffic sorting is obtained. h1+3 Calculate the new judgment value and repeat step one until it is determined whether all predicted traffic is grouped traffic, then proceed to step five; Step 4: Do not include predicted traffic CP h1+2 As a group of traffic, obtain the predicted traffic CP at the h1+3rd position in the traffic sorting. h1+3 Calculate the new judgment value and repeat step one until it is determined whether all predicted traffic is grouped traffic, then proceed to step five; Step 5: Take the first h1 predicted flows in the traffic sort and the grouped flows as the same group, remove the grouped predicted flows from the traffic sort, obtain a new traffic sort XP1, and execute Step 1. Repeat the above steps until all predicted flows have been grouped.

2. The information integration method based on a mini-program for e-commerce platforms according to claim 1, characterized in that, Step S3, the process of configuring forwarding nodes and performing information interconnection according to the group configuration, specifically includes: When the time belongs to the defined domain B', the total number of packets M is obtained, and M forwarding nodes are configured. Each forwarding node is used to receive service information of the service type corresponding to the predicted traffic in the same packet.

3. The information integration method based on a mini-program for e-commerce platforms according to claim 2, characterized in that, Step one, as described above, also includes the following steps: If there is a predicted flow x > 0.95Q, the predicted flow is divided into X = ⌈x / 0.95Q⌉ parts, where ⌈x / 0.95Q⌉ represents rounding up x / 0.95Q. The predicted flow of X-1 parts is 0.95Q, and the predicted flow of the remaining part is x - (X-1) * 0.95Q. The remaining part of the predicted flow participates in the flow sorting, and the predicted flow of X-1 parts is used as X-1 groups.

4. The information integration method based on a mini-program for e-commerce platforms according to claim 3, characterized in that, The process of plotting the curve of data flow changing over time specifically includes: The data traffic is sorted according to the timeline to obtain the first sort; Calculate the data flow difference ΔK i =K i -K i-1 When the data traffic difference ΔK i When ≥ΔKys, the data flow of the i-th position in the first sort is marked as the segmented flow, K i This represents the data flow of the i-th position in the first sorting, and ΔKys represents the preset data flow difference threshold. The first and last data flows in the first sort are marked as segmented flows. For the data flows in the first sort that are between two adjacent segmented flows, coordinate points (ty, K) are generated. ty ), K ty Let ty represent the data flow over time. Fit the coordinate points to obtain the fitted curve Y(t), where t represents time. Plot all the fitted curves Y(t), and connect the starting point of the j-th fitted curve and the ending point of the (j-1)-th fitted curve with a straight line to obtain the curve f(t) representing the data flow 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.

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