A roadside MEC start-stop control method and system based on fuzzy control

Abstract

This invention discloses a roadside MEC start-stop control method and system based on fuzzy control. The steps are as follows: obtaining roadside traffic flow and speed data, selecting a membership function to fuzzify the data, formulating fuzzy rules to establish a fuzzy control model, using the established fuzzy control model to determine the MEC deployment density that minimizes energy and time consumption, determining the appropriate device layout to be activated based on existing deployment points as the final layout plan, and verifying the rationality of the deployment plan using NS2 communication simulation. The invention can calculate the required MEC deployment density and rationally balance the operating conditions of all devices, reducing energy consumption while ensuring communication, and facilitating maintenance.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent connected transportation systems, and specifically relates to a roadside MEC start-stop control method and system based on fuzzy control. Background Art

[0002] With the continuous development of wireless communications and internet technologies, manually driven vehicles will be replaced by intelligent, connected vehicles. The Internet of Vehicles (IoV) has attracted considerable attention in the field of intelligent transportation and is internationally recognized as the best way to reduce traffic delays, improve operational efficiency, and achieve energy conservation and emission reduction.

[0003] Fuzzy logic control, also known as fuzzy control, is a control method based on the basic ideas and theories of fuzzy mathematics (fuzzy set theory, fuzzy linguistic variables, and fuzzy logic reasoning). The advantages of fuzzy control include: (1) using language methods, it does not require an accurate mathematical model of the process; (2) it is robust and suitable for solving problems such as nonlinearity, strong coupling time-varying, and hysteresis in process control; (3) it has strong fault tolerance. It has the ability to adapt to changes in the dynamic characteristics of the controlled object, changes in environmental characteristics, and changes in operating conditions; (4) operators can easily communicate with the human-machine interface through natural language, and fuzzy conditional statements can be easily added to the control links of the process.

[0004] Existing MEC deployment methods often rely on a completely free-form layout, failing to properly balance the operational performance of all devices and easily leading to resource waste and equipment wear and tear. Furthermore, in research on MEC deployment in highway environments, some researchers have used curve fitting to determine the relationship between transmission distance and connection probability clusters. They then derived the relationship between information transmission distance and latency, analyzing the relationship between the optimal number of MECs and road segment length. Some researchers have developed an MEC deployment algorithm based on partial mobility information and compared it with deployment methods that rely solely on vehicle trajectories and those that deploy MEC in congested locations. Some researchers first obtain a basic road pattern to form a 1-coverage placement, then shift the basic pattern at optimal intervals to minimize the average gross distribution of power (GDOP) in the area. This approach then seeks the optimal K value and MEC location, transforming the problem into an optimization problem. Some researchers have considered both direct and multi-hop communication modes and proposed a capacity maximization strategy (CMP). Some researchers have established vehicle clusters to study MEC deployment on highways, significantly reducing MEC deployment costs while ensuring connectivity. However, the technologies of these studies are relatively complex. The purpose of this study is to find a simple and feasible method that can not only meet the basic requirements of the road environment for MEC deployment, but also simplify the existing MEC deployment algorithm. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the existing research technology is relatively complex, and the existing MEC deployment cannot reasonably balance the operation of all devices, which easily leads to problems such as resource waste and equipment loss.

[0006] To solve the above technical problems, the present invention provides a roadside MEC start-stop control method and system based on fuzzy control. The present invention adopts the following technical solutions:

[0007] A fuzzy control-based roadside MEC start-stop control method performs the following steps on a target highway to obtain a start-stop plan for each roadside MEC within a target time period:

[0008] Step A: For a target highway, obtain the traffic volume merging into the target highway during a target time period and the maximum speed of the merging vehicles;

[0009] Step B: Based on the traffic volume entering the target highway during the target time period and the maximum speed of the merging vehicles, combined with the communication range of the roadside MEC on the target highway, a density control model is used. The model uses the traffic volume entering the target highway during the target time period, the maximum speed of the merging vehicles, and the communication range of the roadside MEC on the target highway as inputs and the activation density of the roadside MEC on the target highway during the target time period as output to obtain the activation density of the roadside MEC on the target highway during the target time period.

[0010] Step C: Based on the activation density of the roadside MECs on the target highway during the target time period, combined with the location and usage of each roadside MEC on the target highway, the start-stop plan of each roadside MEC on the target highway during the target time period is obtained.

[0011] As a preferred technical solution of the present invention, in step C, the start-stop plan of each roadside MEC of the target highway within the target time period is obtained by specifically performing the following steps:

[0012] Step C1: Based on the activation density of the roadside MECs on the target highway during the target time period and in combination with the locations of each roadside MEC on the target highway, obtain the preliminary start-stop plans corresponding to each roadside MEC on the target highway under the activation density;

[0013] Step C2: Based on the preliminary start-stop plans corresponding to the activation density and the usage of each roadside MEC in the target highway at the previous moment in the target time period, the start-stop plans of each roadside MEC in the target highway during the target time period are screened from the preliminary start-stop plans.

[0014] As a preferred technical solution of the present invention, in step C2, the start-stop plan of each roadside MEC in the target highway within the target time period is screened from each preliminary start-stop plan. The screening is based on the objective function shown below. The preliminary start-stop plan corresponding to the maximum objective function is used as the start-stop plan of each roadside MEC in the target highway within the target time period:

[0015]

[0016] Among them, y i represents the i-th preliminary start-stop plan; k is the total number of roadside MECs activated in the start-stop plan; s ij The working status of the jth roadside MEC in the i-th preliminary start-stop scheme at the previous moment in the target time period, where enabled is 1 and disabled is 0.

[0017] As a preferred technical solution of the present invention, in step B, the vehicle flow rate merging into the target highway during the target time period, the maximum speed of the merging vehicles, and the communication range of the roadside MEC of the target highway are used as inputs, and the activation density of the roadside MECs on the target highway during the target time period is used as output. The density control model is specifically as follows:

[0018] Step 1: Based on the input data of the density control model: the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the roadside MEC of the target highway, the fuzzy control model is combined to obtain the activation density of the roadside MECs of the three target highways during the target time period. The preset fuzzy rules of the fuzzy control model are as follows: based on the division of the preset fuzzy sets, the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the roadside MEC of the target highway are used as inputs, and the fuzzy set corresponding to the activation density is used as output.

[0019] Step 2: Based on the activation density of the roadside MECs of the three target highways during the target time period, the activation density with the largest density value is selected as the activation density of the roadside MECs in the target highway during the target time period.

[0020] As a preferred technical solution of the present invention, based on the time period type to which the target time period belongs, the traffic volume merging into the target highway and the maximum speed data of the merging vehicles corresponding to each same time period type in the preset historical time are used, and the maximum value thereof is used as the traffic volume merging into the target highway and the maximum speed of the merging vehicles in the target time period.

[0021] As a preferred technical solution of the present invention, the following steps are performed within the target time period:

[0022] Step S1: within a target time period, based on preset time intervals, real-time collection of traffic volume on a target highway within each time interval;

[0023] Step S2: For each time interval, perform the following steps to update the activation density of the roadside MECs on the target highway in the next time interval:

[0024] Step S2.1: predicting the traffic volume in the target time period based on the traffic volume in the time interval and the traffic volume in each time interval that has occurred in the target time period;

[0025] Step S2.2: Based on the predicted traffic volume during the target time period and the ratio of traffic volume entering the target highway during the target time period obtained in Step A, combined with the activation density of roadside MECs on the target highway during the target time period obtained in Step B, the activation density of roadside MECs on the target highway during the next time interval is updated.

[0026] Step S2.3: Based on the activation density of the roadside MECs on the target highway in the next time interval, combined with the location of each roadside MEC on the target highway and the usage in the time interval, obtain the start-stop plan of each roadside MEC on the target highway in the next time interval.

[0027] As a preferred technical solution of the present invention, the following steps are further performed after step C to obtain a final start-stop plan for each roadside MEC in the target expressway within the target time period, and the final start-stop plan is used as the start-stop plan for each roadside MEC in the target expressway within the target time period:

[0028] Step D: Based on step C, the start-stop plan of each roadside MEC of the target highway within the target time period is obtained. Based on the communication delay of the start-stop plan, the rationality of the start-stop plan is verified. If the communication delay meets the preset communication delay, the start-stop plan is reasonable and the start-stop plan is the final start-stop plan. If the communication delay does not meet the preset communication delay, return to step B to modify the fuzzy set output in the fuzzy control model based on the fuzzy set order, and then continue to execute BD until the communication delay of the start-stop plan meets the preset communication delay to obtain the final start-stop plan.

[0029] As a preferred technical solution of the present invention, the fuzzy set output in the fuzzy control model is modified based on the fuzzy set order, specifically as follows:

[0030] Based on the current fuzzy rules of the fuzzy control model, the output fuzzy sets under each type of rule are modified respectively; the modification rules are: based on the order of fuzzy sets from small to large, one output fuzzy set is modified at a time, and the output fuzzy set is changed to the next fuzzy set based on the order of fuzzy sets from small to large.

[0031] A roadside MEC start-stop control system based on fuzzy control is applied to the roadside MEC start-stop control method based on fuzzy control, which is characterized by comprising a data acquisition module, a density control module, a solution output module,

[0032] The data acquisition module is used to collect the traffic volume entering the target highway during the target time period and the maximum speed of the entering vehicles;

[0033] The density control module obtains the activation density of roadside MECs on the target highway during the target time period based on the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the roadside MECs on the target highway.

[0034] The plan output module obtains the start-stop plan of each roadside MEC on the target highway within the target time period based on the activation density of the roadside MEC on the target highway within the target time period and the location and usage of each roadside MEC on the target highway.

[0035] As a preferred technical solution of the present invention, it also includes a solution verification module, which is used to verify the rationality of the start-stop plan of each roadside MEC in the target expressway output by the solution output module within the target time period. If the plan is reasonable, the start-stop plan is the start-stop plan of each roadside MEC in the target expressway within the target time period; if the plan is unreasonable, it returns to the density control module to modify the fuzzy rules until the plan is reasonable, and the start-stop plan of each roadside MEC in the target expressway within the target time period is obtained.

[0036] The beneficial effects of the present invention are as follows: the present invention provides a roadside MEC start-stop control method and system based on fuzzy control, which obtains data such as roadside traffic flow and speed, selects a membership function to fuzzify the data, formulates fuzzy rules to establish a fuzzy control model, and obtains the MEC layout density with the least energy and time consumption through the established fuzzy control model. The appropriate layout of equipment that needs to be enabled is determined as the final layout plan based on the existing layout points, and the rationality of the layout plan is verified by NS2 communication simulation, so as to achieve the purpose of meeting communication delay requirements, reducing MEC layout costs and ensuring economy. This method adopts a fuzzy control model and does not establish a mathematical model, which ensures the flexibility and simplicity of the model. The present invention can calculate the required MEC layout density through a simple technical solution, and reasonably balance the operating conditions of all equipment, reduce energy consumption while ensuring communication, and facilitate maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Flowchart of a roadside MEC deployment method based on fuzzy control in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present invention will be further described below in conjunction with the accompanying drawings. The following examples can enable those skilled in the art to more fully understand the present invention, but are not intended to limit the present invention in any way.

[0039] like Figure 1 As shown in FIG, a fuzzy control-based roadside MEC start-stop control method performs the following steps on a target highway to obtain a start-stop plan for each roadside MEC within a target time period:

[0040] Step A: For a target highway, obtain the traffic volume merging into the target highway during a target time period and the maximum speed of the merging vehicles;

[0041] In this embodiment, based on the time period type to which the target time period belongs, the maximum value of the traffic volume and maximum speed data of vehicles merging into the target highway corresponding to each time period type within a preset historical period is used as the traffic volume and maximum speed data of vehicles merging into the target highway for the target time period. For example, for the morning rush hour period on a future Wednesday, the maximum value of the traffic volume and maximum speed data of vehicles merging into the target highway corresponding to each morning rush hour period on Wednesdays within the preset historical period is used as the predicted morning rush hour data for the future Wednesday.

[0042] Step B: Based on the traffic volume entering the target highway during the target time period and the maximum speed of the entering vehicles, combined with the communication range of the roadside MEC on the target highway, a density control model is used. The model uses the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the roadside MEC on the target highway as inputs and the activation density of the roadside MEC on the target highway during the target time period as output to obtain the activation density of the roadside MEC on the target highway during the target time period.

[0043] In step B, the density control model is constructed with the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the roadside MEC on the target highway as inputs, and the activation density of the roadside MEC on the target highway during the target time period as output. Specifically, the model is as follows:

[0044] Step 1: For the input data of the density control model: the traffic volume flowing into the target highway during the target time period, the maximum speed of the vehicles flowing into the highway, and the communication range of the roadside MEC of the target highway. By default, the communication range of each MEC is the same. Combined with the fuzzy control model, the activation density of the roadside MECs of the three target highways during the target time period is obtained; the preset fuzzy rules of the fuzzy control model are: based on the division of the preset fuzzy sets, for the traffic volume flowing into the target highway during the target time period, the maximum speed of the vehicles flowing into the highway, and the communication range of the roadside MEC of the target highway, the fuzzy sets corresponding to two non-repeated data are used as input, and the fuzzy set corresponding to one activation density is output as one type of rule, with a total of three types of rules; namely, The input data is divided into three categories of rules.

[0045] In one embodiment, the fuzzy control model is constructed by the following steps:

[0046] Step a: Normalize the input data of the fuzzy control model to a preset interval. Specifically, normalize the data on the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the MEC on the roadside of the target highway to the interval [-6, 6].

[0047] Step b: For the preset interval of the standardized input data of the fuzzy control model, the output quantity is the same preset interval, and the preset interval is divided into a preset number of fuzzy sets; specifically, the [-6,6] interval is divided into fuzzy sets NB, NM, NS, Z, PS, PM, PB, among which NB[-6,-2], NM[-5,1], NS[-4,2], Z[-2,2], PS[-2,4], PM[-1,5], PB[2,6], and the fuzzy sets are sorted from small to large as NB, NM, NS, Z, PS, PM, PB.

[0048] Step c: For a preset number of fuzzy sets, determine the preset membership function to fuzzify the model input data to the corresponding fuzzy set; specifically, for the fuzzy sets NM, NS, Z, PS, and PM, select the triangular membership function, and for the fuzzy sets NB and PB, select the S-type membership function, and fuzzify the fuzzy control model input data to the corresponding fuzzy set.

[0049] Step d: Based on a preset number of fuzzy sets, a preset fuzzy rule is formulated for the fuzzy set to which the fuzzified input data belongs, taking the fuzzy set to which the model input data belongs as input and the fuzzy set to which the activation density of the roadside MECs on the target highway during the target time period belongs as output. Specifically, the preset fuzzy rule includes: for the volume of vehicles entering the target highway during the target time period, the maximum speed of vehicles entering the highway, and the communication range of the roadside MECs on the target highway, any two fuzzy set input variables correspond to an activation density fuzzy set output. The activation density of the roadside MECs on the three target highways is then obtained through the fuzzy control model. In other words, the fuzzy set rules include the following three types of rules: 1. The volume of vehicles entering the target highway during the target time period and the maximum speed of vehicles entering the highway correspond to an activation density fuzzy set output; 2. The maximum speed of vehicles entering the highway and the communication range of the roadside MECs on the target highway correspond to an activation density fuzzy set output; and 3. The volume of vehicles entering the target highway during the target time period and the communication range of the roadside MECs on the target highway correspond to an activation density fuzzy set output.

[0050] The overall rule of fuzzy rules is: the output (required MEC deployment density) is proportional to the traffic volume and vehicle speed, and inversely proportional to the communication range. Specific fuzzy rules are subjectively formulated based on the overall rule and will be adjusted if they are not appropriate later.

[0051] In this embodiment, EC and E represent two input variables. As shown in Table 1, taking vehicle flow (EC) and communication range (E) as an example, the fuzzy rules formulated are shown in Table 1. The first column in the table below corresponds to the fuzzy set of vehicle flow (EC), and the first row corresponds to the fuzzy set of communication range (E):

[0052] Table 1

[0053]

[0054] The corresponding language part is expressed as:

[0055] If the communication range belongs to NB and the traffic flow belongs to NB or NM, then the output belongs to Z; if the communication range belongs to NB and the traffic flow belongs to NS, then the output belongs to PS;

[0056] If the communication range belongs to NB and the traffic flow belongs to Z or PS, then the output belongs to PM; if the communication range belongs to NB and the traffic flow belongs to PM or PB, then the output belongs to PB; if the communication range belongs to NM and the traffic flow belongs to NB or NM, then the output belongs to NS; if the communication range belongs to NM and the traffic flow belongs to NS, then the output belongs to Z;

[0057] If the communication range belongs to NM and the traffic volume belongs to Z, then the output belongs to PS;

[0058] If the communication range belongs to NM and the traffic volume belongs to PS, then the output belongs to PM;

[0059] If the communication range belongs to NM and the traffic flow belongs to PM or PB, then the output belongs to PB; if the communication range belongs to NS and the traffic flow belongs to NB, then the output belongs to NM;

[0060] If the communication range belongs to NS and the traffic volume belongs to NM, then the output belongs to NS;

[0061] If the communication range belongs to NS and the traffic volume belongs to NS or Z, then the output belongs to Z;

[0062] If the communication range belongs to NS and the traffic volume belongs to PS, then the output belongs to PS;

[0063] If the communication range belongs to NS and the traffic volume belongs to PM, then the output belongs to PM;

[0064] If the communication range belongs to NS and the traffic volume belongs to PB, then the output belongs to PB;

[0065] If the communication range belongs to Z and the traffic flow belongs to NB or NM, then the output belongs to NM; if the communication range belongs to Z and the traffic flow belongs to NS or Z, then the output belongs to Z;

[0066] If the communication range belongs to Z and the traffic flow belongs to PS, then the output belongs to PS;

[0067] If the communication range belongs to Z and the traffic volume belongs to PM, then the output belongs to PM;

[0068] If the communication range belongs to Z and the traffic volume belongs to PB, then the output belongs to PB;

[0069] …

[0070] Taking traffic speed (EC) and communication range (E) as an example, the fuzzy rules are shown in Table 2:

[0071] Table 2

[0072]

[0073] Taking traffic speed (EC) and traffic volume (E) as an example, the fuzzy rules are shown in Table 3:

[0074] Table 3

[0075]

[0076] Step e: Determine a defuzzification method for the output of the fuzzy rule; specifically, the defuzzification method is a center of gravity method, that is, obtaining activation density data.

[0077] Based on the above process of constructing the fuzzy control model, the fuzzy tool in Matlab can be used to determine the membership function, parameters, fuzzy rule code, defuzzification method, etc., and the constructed fuzzy control model can be used to input data and output the final MEC activation density value.

[0078] Step 2: Based on the activation density of the roadside MECs of the three target highways during the target time period, the activation density with the largest density value is selected as the activation density of the roadside MECs in the target highway during the target time period.

[0079] Step C: Based on the activation density of the roadside MECs on the target highway during the target time period, combined with the location and usage of each roadside MEC on the target highway, the start-stop plan of each roadside MEC on the target highway during the target time period is obtained.

[0080] In step C, the start and stop plan of each roadside MEC of the target highway within the target time period is obtained specifically through the following steps:

[0081] Step C1: Based on the activation density of the roadside MECs on the target highway during the target time period and in combination with the locations of each roadside MEC on the target highway, obtain the preliminary start-stop plans corresponding to each roadside MEC on the target highway under the activation density;

[0082] Step C2: Based on the preliminary start-stop plans corresponding to the activation density and the usage of each roadside MEC in the target highway at the previous moment in the target time period, the start-stop plans of each roadside MEC in the target highway during the target time period are screened from the preliminary start-stop plans.

[0083] In step C2, the screening is based on the objective function shown below, and the preliminary start-stop plan corresponding to the maximum objective function is used as the start-stop plan for each roadside MEC in the target highway within the target time period:

[0084]

[0085] Among them, y i represents the i-th preliminary start-stop plan; k is the total number of roadside MECs activated in the start-stop plan; s ij =((i)) / (j) is the operating status of the jth roadside MEC at the previous moment in the target time period in the i-th preliminary start-stop scheme, where enabled is 1 and disabled is 0. The objective function is to maximize the number of roadside MECs that need to change their operating status, while balancing the operating time of each MEC device as much as possible to facilitate unified maintenance of the devices.

[0086] like Figure 1 As shown, after step C, the following steps are performed to obtain the final start-stop plan of the MECs on each roadside in the target highway within the target time period, and the final start-stop plan is used as the start-stop plan of the MECs on each roadside in the target highway within the target time period:

[0087] Step D: Based on Step C, obtain a start-stop plan for each roadside MEC on the target highway within the target time period. Based on the communication delay of this start-stop plan, verify its rationality. If the communication delay meets the preset communication delay, the start-stop plan is reasonable and becomes the final start-stop plan. If the communication delay does not meet the preset communication delay, return to Step B and modify the fuzzy set output in the fuzzy control model based on the fuzzy set sequence. Continue executing Steps 2 and 3 until the communication delay of the start-stop plan meets the preset communication delay, thus obtaining the final start-stop plan. NS2 communication simulation can be used to verify the rationality of the deployment plan.

[0088] The fuzzy rules in the fuzzy control model are modified based on the order of fuzzy sets as follows: based on the current fuzzy rules of the fuzzy control model, the output fuzzy sets under each rule are modified. The modification rule is: based on the ascending order of fuzzy sets, one output fuzzy set is modified at a time, and the output fuzzy set is changed to the next fuzzy set based on the ascending order of fuzzy sets. Specifically, the modification rule is: for each rule, first modify any rule where the output result is NB, that is, modify one output fuzzy set at a time, and change the output fuzzy set of that rule to NM. If all the modifications have completed, the output result is NB, then the output result is NM, and so on for NS, Z, PS, PM, and PB. The modifications are made one grid at a time, and the fuzzy sets are moved back one grid at a time, for example, from NB to NM, from NM to NS, and so on. If all the output fuzzy sets are finally modified to the largest fuzzy set PB, and a reasonable solution is still not obtained, the start-stop solution corresponding to the minimum communication delay in each iteration is used as the final start-stop solution. In this embodiment, for the three types of rules in Tables 1 to 3 above, the rules at the minimum output are modified from right to left and from top to bottom. First, the output result is NB. If all modifications are completed, the output result is NM. The same applies to NS, Z, PS, PM, PB, and so on. Modify one grid at a time. One modification changes the fuzzy set and then moves it one grid, for example, from NB to NM, and from NM to NS.

[0089] Verify whether the start-stop plan is reasonable, specifically:

[0090] (1) Set the corresponding parameters based on the above scheme in the NS simulation platform;

[0091] (2) Conduct simulation to obtain the corresponding communication delay of the start-stop scheme;

[0092] (3) Compare the communication delay results obtained from the NS simulation with the requirements for the VIS communication delay. If they are within the VIS communication delay range, they are considered reasonable. If the simulation results show that the communication delay exceeds the specified range, return to modify the fuzzy rules and execute the subsequent steps in sequence until the communication delay of the start-stop plan meets the preset communication delay, and the final start-stop plan is obtained. In this embodiment, 100ms is used as the preset communication delay. Observe the NS simulation output results: if the output is 0 or greater than 100ms, it is considered unqualified.

[0093] In addition, during the target time period, this solution also performs the following steps, steps S1 and S2, to continuously update and revise the MEC start-stop plan for each roadside of the target highway:

[0094] Step S1: Within the target time period, based on the preset time intervals, the traffic volume of the target highway in each time interval is collected in real time; specifically, for example, for the morning rush hour period of the next Wednesday, the traffic volume of the target highway in the time interval is divided into half-hour time intervals. When the time interval occurs, the traffic volume of the target highway in the time interval is collected.

[0095] Step S2: For each time interval, perform the following steps to update the activation density of the roadside MECs on the target highway in the next time interval:

[0096] Step S2.1: Based on the traffic volume in the time interval and the traffic volume in each time interval that has occurred in the target time interval, the traffic volume in the target time interval is predicted; specifically, for example, the morning rush hour period on the next Wednesday is three hours, and based on the half-hour time interval, when the traffic volume in the first time interval is collected, the traffic volume is multiplied by six to predict the traffic volume in the target time interval; when the traffic volume in the first time interval and the second time interval is collected, the sum of the traffic volume in the first time interval and the second time interval is multiplied by three to predict the traffic volume in the target time interval, and so on to predict the traffic volume in the target time period.

[0097] Step S2.2: Based on the proportional relationship between the predicted traffic volume in the target time period and the traffic volume entering the target expressway in the target time period obtained in step A, combined with the activation density of roadside MECs in the target expressway in the target time period obtained in step B, the activation density of roadside MECs in the target expressway in the next time interval is updated; specifically, the proportional relationship between the predicted traffic volume in the target time period and the traffic volume entering the target expressway in the target time period obtained in step A is equal to the proportional relationship between the activation density of roadside MECs in the target expressway in the next time interval and the activation density obtained in step B.

[0098] Step S2.3: Based on the activation density of the roadside MECs on the target highway in the next time interval, combined with the location of each roadside MEC on the target highway and its usage in the time interval, the start-stop plan for each roadside MEC on the target highway in the next time interval is obtained, similar to step C.

[0099] Specifically, in step S2.3, the start-stop plan of each roadside MEC of the target highway in the next time interval is obtained by the following steps:

[0100] First, based on the activation density of the roadside MECs in the target highway in the next time interval and the locations of each roadside MEC on the target highway, the preliminary start-stop plans corresponding to each roadside MEC in the target highway under the activation density are obtained.

[0101] Then, based on the preliminary start-stop plans corresponding to the activation density and the usage of each roadside MEC in the target highway during the time interval, the start-stop plans of each roadside MEC on the target highway in the next time interval are obtained from the preliminary start-stop plans.

[0102] Specifically, the screening is based on the objective function shown below. The preliminary start-stop plan corresponding to the maximum objective function is used as the start-stop plan for each roadside MEC of the target highway in the next time interval:

[0103]

[0104] Among them, x i represents the i-th preliminary start-stop plan; m is the total number of roadside MECs activated in the start-stop plan; o ij It is the working status of the jth roadside MEC in the i-th preliminary start-stop scheme at this time interval, where enabled is 1 and disabled is 0.

[0105] In addition, based on the above method, this solution also designs a roadside MEC start-stop control system based on fuzzy control, which is applied to the above roadside MEC start-stop control method based on fuzzy control, including a data acquisition module, a density control module, and a solution output module. The data acquisition module, the density control module, and the solution output module are connected in sequence, and finally the solution output module outputs the start-stop solution of each roadside MEC in the target highway within the target time period;

[0106] The data acquisition module is used to collect the traffic volume entering the target highway during the target time period and the maximum speed of the entering vehicles;

[0107] The density control module obtains the activation density of roadside MECs on the target highway during the target time period based on the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the roadside MECs on the target highway.

[0108] The plan output module obtains the start-stop plan of each roadside MEC on the target highway within the target time period based on the activation density of the roadside MEC on the target highway within the target time period and the location and usage of each roadside MEC on the target highway.

[0109] In addition, for the target time period, a density correction module is also included. Based on the division of the preset time interval, steps S1-S2 are executed to continuously update and correct the MEC start-stop plan of each road side of the target highway in the next time interval.

[0110] A roadside MEC start-stop control system based on fuzzy control also includes a plan verification module connected to the plan output module. The plan verification module is used to verify the rationality of the start-stop plan of each roadside MEC in the target highway within the target time period output by the plan output module. If the plan is reasonable, the start-stop plan is the start-stop plan of each roadside MEC in the target highway within the target time period; if the plan is unreasonable, it returns to the density control module to modify the fuzzy rules until the plan is reasonable, and the start-stop plan of each roadside MEC in the target highway within the target time period is obtained.

[0111] This solution designs a roadside MEC start-stop control method and system based on fuzzy control. By obtaining data such as roadside traffic volume and speed, the membership function is selected to fuzzify the data, and fuzzy rules are formulated to establish a fuzzy control model. The MEC layout density with the least energy and time consumption is obtained through the established fuzzy control model. The appropriate equipment layout that needs to be enabled is determined as the final layout plan based on the existing layout points, and the rationality of the layout plan is verified using NS2 communication simulation, so as to achieve the purpose of meeting communication delay requirements, reducing MEC layout costs and ensuring economy. This method adopts a fuzzy control model and does not establish a mathematical model, which ensures the flexibility and simplicity of the model. The present invention can calculate the required MEC layout density through a simple technical solution, and reasonably balance the operating conditions of all equipment, reduce energy consumption while ensuring communication, and facilitate maintenance.

[0112] The above are only preferred embodiments of the present invention, but do not limit the scope of the patent of the present invention. Although the present invention has been described in detail with reference to the above embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the above embodiments or to replace some of the technical features therein with equivalents. Any equivalent structure made by using the contents of the present invention specification and drawings, directly or indirectly applied to other related technical fields, is also within the scope of protection of the patent of the present invention.

Claims

1. A roadside MEC start-stop control method based on fuzzy control, characterized by: For the target highway, perform the following steps to obtain the start and stop plan for each roadside MEC within the target time period: Step A: For a target highway, obtain the traffic volume merging into the target highway during a target time period and the maximum speed of the merging vehicles; Step B: Based on the traffic volume entering the target highway during the target time period and the maximum speed of the merging vehicles, combined with the communication range of the roadside MEC on the target highway, a density control model is used. The model uses the traffic volume entering the target highway during the target time period, the maximum speed of the merging vehicles, and the communication range of the roadside MEC on the target highway as inputs and the activation density of the roadside MEC on the target highway during the target time period as output to obtain the activation density of the roadside MEC on the target highway during the target time period. The step B is specifically as follows: Step 1: Based on the input data of the density control model: the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the roadside MEC of the target highway, the fuzzy control model is combined to obtain the activation density of the roadside MECs of the three target highways during the target time period. The preset fuzzy rules of the fuzzy control model are as follows: based on the division of the preset fuzzy sets, the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the roadside MEC of the target highway are used as inputs, and the fuzzy set corresponding to the activation density is used as output. Step 2: Based on the activation density of the roadside MECs on the three target highways during the target time period, select the activation density with the largest density value as the activation density of the roadside MECs on the target highway during the target time period; Step C: Based on the activation density of the roadside MECs on the target highway during the target time period, combined with the location and usage of each roadside MEC on the target highway, a start-stop plan for each roadside MEC on the target highway during the target time period is obtained; Step D: Based on step C, the start-stop plan of each roadside MEC on the target highway within the target time period is obtained. Based on the communication delay of the start-stop plan, the rationality of the start-stop plan is verified. If the communication delay meets the preset communication delay, the start-stop plan is reasonable and the start-stop plan is the final start-stop plan. If the communication delay does not meet the preset communication delay, return to step B to modify the order of the fuzzy sets output in the fuzzy control model, and then continue to execute BD until the communication delay of the start-stop plan meets the preset communication delay, and the final start-stop plan is obtained; The fuzzy set output in the fuzzy control model is modified based on the fuzzy set order, specifically as follows: Based on the current fuzzy rules of the fuzzy control model, the output fuzzy sets under each type of rule are modified respectively; the modification rules are: based on the order of fuzzy sets from small to large, one output fuzzy set is modified at a time, and the output fuzzy set is changed to the next fuzzy set based on the order of fuzzy sets from small to large.

2. The fuzzy control-based roadside MEC start-stop control method according to claim 1, characterized in that: In step C, the start and stop plan of each roadside MEC of the target highway within the target time period is obtained specifically through the following steps: Step C1: Based on the activation density of the roadside MECs on the target highway during the target time period and in combination with the locations of each roadside MEC on the target highway, obtain the preliminary start-stop plans corresponding to each roadside MEC on the target highway under the activation density; Step C2: Based on the preliminary start-stop plans corresponding to the activation density and the usage of each roadside MEC in the target highway at the previous moment in the target time period, the start-stop plans of each roadside MEC in the target highway during the target time period are screened from the preliminary start-stop plans.

3. The fuzzy control-based roadside MEC start-stop control method according to claim 2, characterized in that: In step C2, the start-stop plan for each roadside MEC in the target highway within the target time period is obtained by screening from each preliminary start-stop plan. The screening is based on the objective function shown below. The preliminary start-stop plan corresponding to the maximum objective function is used as the start-stop plan for each roadside MEC in the target highway within the target time period: Among them, y i represents the i-th preliminary start-stop plan; k is the total number of roadside MECs activated in the start-stop plan; s ij The working status of the jth roadside MEC in the i-th preliminary start-stop scheme at the previous moment in the target time period, where enabled is 1 and disabled is 0.

4. The fuzzy control-based roadside MEC start-stop control method according to claim 1, characterized in that: Based on the time period type to which the target time period belongs, the maximum value of the traffic volume merging into the target highway and the maximum speed data of the merging vehicles corresponding to each same time period type in the preset historical time period is used as the traffic volume merging into the target highway and the maximum speed of the merging vehicles in the target time period.

5. The fuzzy control-based roadside MEC start-stop control method according to claim 1, characterized in that: During the target time period, perform the following steps: Step S1: within a target time period, based on preset time intervals, real-time collection of traffic volume on a target highway within each time interval; Step S2: For each time interval, perform the following steps to update the activation density of the roadside MECs on the target highway in the next time interval: Step S2.1: predicting the traffic volume in the target time period based on the traffic volume in the time interval and the traffic volume in each time interval that has occurred in the target time period; Step S2.2: Based on the predicted traffic volume during the target time period and the ratio of traffic volume entering the target highway during the target time period obtained in Step A, combined with the activation density of roadside MECs on the target highway during the target time period obtained in Step B, the activation density of roadside MECs on the target highway during the next time interval is updated. Step S2.3: Based on the activation density of the roadside MECs on the target highway in the next time interval, combined with the location of each roadside MEC on the target highway and the usage in the time interval, obtain the start-stop plan of each roadside MEC on the target highway in the next time interval.

6. A roadside MEC start-stop control system based on fuzzy control, applied to the roadside MEC start-stop control method based on fuzzy control according to any one of claims 1 to 5, characterized in that: Including data acquisition module, density control module, solution output module, The data acquisition module is used to collect the traffic volume entering the target highway during the target time period and the maximum speed of the entering vehicles; The density control module obtains the activation density of roadside MECs on the target highway during the target time period based on the traffic volume entering the target highway during the target time period, the maximum speed of the entering vehicles, and the communication range of the roadside MECs on the target highway. The plan output module obtains the start-stop plan of each roadside MEC on the target highway within the target time period based on the activation density of the roadside MEC on the target highway within the target time period and the location and usage of each roadside MEC on the target highway.

7. The roadside MEC start-stop control system based on fuzzy control according to claim 6, characterized in that: It also includes a plan verification module, which is used to verify the rationality of the start-stop plan of the MEC on each road side of the target highway within the target time period output by the plan output module. If the plan is reasonable, the start-stop plan is the start-stop plan of the MEC on each road side of the target highway within the target time period; if the plan is unreasonable, it returns to the density control module to modify the fuzzy rules until the plan is reasonable, and the start-stop plan of the MEC on each road side of the target highway within the target time period is obtained.

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