When the operator uses the asymmetric coupling network to transmit data, there will be several groups of array e-commerce platform signal sets arranged according to certain rules in the network. The network transmits the signals of the e-commerce platform to different users in a certain form through software and hardware such as sensors and displays [1]. The receiving end determines the required data content in various forms such as text, video, and images. The positioning module determines the network usage location and other contents according to the sending location, sending time, receiving location, and receiving time of the e-commerce platform signal. From 1736, foreign countries first began to study the signal transmission control theory of e-commerce platforms through abstract mathematical assumptions. In the past two centuries, people have been combining mathematical abstract hypothesis theory to control the synchronous transmission of e-commerce platform signals. Domestic research on this work started relatively late. With various science and technology's continuous deepening and innovation, various synchronous control strategies or methods have further research progress.

Some scholars have proposed a signal control method for e-commerce platforms based on the improved deep reinforcement learning method. This method sets different transmission reward and punishment coefficients according to the dynamic change characteristics of e-commerce platform signals [2]. They combine data timing to determine the control priority set. According to the signal control risk evaluation model of the plane e-commerce platform, some scholars have determined the risk index of the signal transmission control of the e-commerce platform combined with the physical parameters such as network operation speed, operation state, and distance. In this way, the synchronous control of the signals of the array e-commerce platform is realized. Some scholars have used a combination of methods and models to realize the synchronous control of the transmission of e-commerce platform signals. According to the detection results of the signal change amplitude of the e-commerce platform, they use the model prediction to control the shift amount to divide the different forms of the e-commerce platform signal change [3]. The method can realize the synchronous control of the signals of the e-commerce platform. This research combines several advanced control methods' research ideas and application technologies to propose a new control modeling method. This paper provides more reliable technical support for the daily use of asymmetric coupled networks.

Mixed phase transitions exist in asymmetrically coupled networks. We employ a new method to identify the underlying mechanisms of phase diagrams at different network node scales. We analyze the formation of mixed-phase transitions according to the

According to the above calculation formula, the specific value of formula (1) can be obtained. where

Assume that the values of _{I,A}_{I,B}_{II}

We utilize software to virtualize asymmetric coupling networks. The experiment requires that all the array platform signals can control the platform signal transmission mode and state according to a certain order and regularity during the transmission process. We control the phase angle fluctuation by ensuring the smoothness and stability of the network operation. In addition, it is required that the designed synchronous control full-slave scheme eliminate the influence of communication delay. We need to get rid of the limitation of the pulse platform signal further to simplify the way of network communication [7]. We use Matlab software simulation software to set the synchronous control mode. A virtual network scenario is established using the simulation unit of the software. We control different array platform signals by establishing an all-slave mode. When the all-slave synchronous control can adjust the phase angle of the platform signal in different transmission stages, we design the dynamic control command and the given fixed command of the model. The article extracts the response difference index of the array platform signal. At the same time, the phase synchronization control algorithm is used to eliminate the output difference between the synchronization control command and the platform signal. We use the simulation modeling method to simulate the network operation process of a random period. Then the sum of the absolute values of the deviation of the phase angle is:
_{1} represents the synchronization control index of the all-slave scheme. _{2} represents the synchronization control index of the master-slave scheme. Controlling the diversified phase angle difference variation of asymmetric coupling networks under different operating states has been solved [8]. We control the signal transmission interference error of the e-commerce platform to a minimum and can eliminate the channel fluctuation problem caused by network “jitter.” According to the above process, the phase-locked loop control method is used to set the model's all-slave scheme's application algorithm. We adopt the phase-locked loop as the technology that can automatically lock the signal of the network e-commerce platform. This technology is widely used in various network array e-commerce platform signals. Combined with the control characteristics of phase-locked loop technology, the transfer function calculation formula of this method is obtained as follows:
_{1} represents the loop filter gain in the operating state of the asymmetric coupling network. _{2} represents proportional gain. _{3} represents the integral gain. _{4} represents reintegration gain. _{1}, _{2} respectively represent the time constants of different reintegration elements.

We determine the basic structure under the dynamic transmission control of the network according to the synchronous control full-slave scheme [9]. Assuming that there is no packet loss problem in the signal transmission of the arrayed e-commerce platform in the asymmetric coupling network, there is the following formula:
_{g}_{0} represents the initial condition at the time parameter _{∞}. The controller in the synchronous control model is established according to this process [10]. At the same time, to strengthen the control effect of the model on the transmission load and information exchange frequency, we set the event description method of the model. It is known that the network array e-commerce platform signal starts to perform the transmission task when the trigger condition is met. Therefore, it is easy to lose part of the network e-commerce platform signal due to the mixed-phase transition interference of the asymmetric coupling network during the transmission process. These e-commerce platform signals do not affect data transmission if they have actual functionality. If the lost e-commerce platform signal represents critical information, it will directly affect the transmission effect. We adopt an event-triggered mechanism with saturation constraints to build the synchronous control structure layer of the model. We can describe the set constraints using the following inequalities.
_{y}_{1} is an integer. _{y}_{1}_{1+n} represents the next transmission moment. _{1} represents the minimum value of formula (7). We construct the synchronization control model according to the above conditions.

The experiment gives two simulation examples of asymmetric coupling networks. We use experimental cases to verify the reliability of the synchronous control modeling method in this study. We set up two sets of coupled networks of varying complexity. Test environment A is a mildly asymmetrical coupling network, and test environment B is heavily asymmetrical [11]. The signal transmission behavior of the array e-commerce platform of the two networks is shown in Figure 1 below.

We set the signal sampling period of the array e-commerce platform according to the initial conditions of the two groups of networks. We use the proposed and conventional models to build the control model, respectively. We use the test environment shown in Figure 1 as a variable [12]. At the same time, we calculate the trigger time and interval of synchronization control of the model according to the transmission trajectory of the array e-commerce platform signal in the network. This paper compares the differences in the synchronization control of the signal transmission process of the array e-commerce platform between the two groups of models in the asymmetric coupled network.

Compare the coupling network states after synchronous control in the two test environments. The results obtained in test environment A are shown in Figure 2 below.

We observe the signal transmission trajectory of the array e-commerce platform in test environment A and find the following conclusions. There is a high similarity in the synchronization control of the signal transmission trajectory of the array e-commerce platform of the two groups of models in the asymmetric coupling network with low complexity. The signal of the array e-commerce platform only fluctuates in the 5th to 10th s. The transmission is relatively stable most of the time [13]. Therefore, in the face of low-level signal changes of the e-commerce platform, both models can better control the signal transmission of the e-commerce platform. Experiments show that the two models have better synchronization control effects on the coupled network e-commerce platform signals with low complexity and general asymmetry. Figure 3 below shows the comparison results of the network e-commerce platform signal transmission trajectory obtained in test environment B.

Our observation of the signal transmission trace of the array e-commerce platform in test environment B is follows. In the extremely complex asymmetric coupling network, there is a high degree of difference in the synchronization control of the signal transmission trajectory of the array e-commerce platform of the two models. When the 2.7s, 5.3s, and 15s are fluctuating and stable, respectively, the control model in this paper can control the signal transmission of the e-commerce platform well. There is a great control error in the signal transmission trajectory of the array e-commerce platform under the conventional control model. It can be seen that there are differences in the synchronous control of the two groups of models when faced with a highly complex and severely asymmetric coupled network.

When the two groups of control models perform the same e-commerce platform signal transmission control work, they need to control the trigger time and interval synchronously. We compared the two groups' feedback and executive ability of synchronous control models. The control trigger time and interval test results under test environment A are shown in Figures 4 and 5 below.

According to the two groups of test results shown in Fig. 4 and Fig. 5, it can be seen that when facing test environment A, the synchronous control trigger time of the two groups of models is relatively close, and the time interval is relatively uniform. When facing test environment B, due to the high degree of network asymmetry, the signal transmission of the array e-commerce platform has great fluctuations [14]. The control model in this paper can generally guarantee the control effect of the trigger time and interval. The control effect is similar to the real-time change of the e-commerce platform signal. However, the conventional control model does not match the constraint event trigger mechanism due to the all-slave scheme. This results in a large variance between trigger moments and an approximate average trigger interval. This makes it difficult to solve the dynamic change transmission of e-commerce platform signals.

This study combines the advantages of advanced control modeling methods to optimize conventional modeling further. We have obtained relatively excellent research results. The signal of the array e-commerce platform in the asymmetric coupled network under the model control has a more stable transmission effect in daily tasks and meets the standard research requirements. However, there are still some shortcomings in this study. First, the modeling method is complicated and prone to calculation errors in the calculation steps. This affects the corresponding control parameters. Secondly, the control efficiency of the model cannot be demonstrated through this experiment, so the working time of the model cannot be determined. Whether this method is highly efficient remains to be demonstrated. We can design a complete set of control algorithms for the two shortcomings of this research. We use this algorithm to control the control work of the model to ensure efficiency of the model. Second, we can set up a self-detection unit. This allows the model to check the accuracy of each computational step automatically. The model adjusts the error data in time. This provides a more stable and efficient synchronization control method for the signal transmission of the e-commerce platform.

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Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model