Wireless network multi-gateway joint deployment method for digital twin workshop

Abstract

The invention provides a wireless network multi-gateway joint deployment method for a digital twin workshop. Comprising the following steps: analyzing the influence of the GW position, channel and power on the data transmission rate of a terminal node (Station, STA) and adjacent link interference based on a signal to interference plus noise ratio model, and constructing a terminal node satisfaction (Station Station Rate, SSR) optimization model; the method comprises the following steps of: mapping an Army Ant Search Optimizer (AASO) and a multi-GW deployment problem to form a multi-GW joint deployment strategy based on the AASO; according to the method, the SSR is remarkably improved, the coverage range of a high-quality wireless network is expanded, and good applicability and robustness are shown under different experimental conditions. Technical support is provided for wireless network design of the DTW, related personnel are helped to evaluate the satisfaction capability of the network to equipment transmission requirements, and network design is optimized. The invention aims to promote the development of intelligent factories and digital workshops and improve the production efficiency and the reliability of information transmission so as to better meet the modern industrial requirements.

Description

Technical Field

[0001] The present invention relates to the field of industrial network deployment methods; in particular, to a wireless network multi-gateway joint deployment method for a digital twin workshop. Background Art

[0002] In dense industrial network environments, the deployment of WiFi networks faces many challenges, which not only affect the performance of the network, but also the efficiency and reliability of the entire industrial system. First, due to the limited available channel resources, it is extremely difficult to implement a solution where all adjacent links use non-overlapping channels (NOC) at the same time. Therefore, it is necessary to reasonably configure partially overlapping channels (POC) to reduce interference between adjacent channels. The solution to this problem is related to the overall interference level of the network and the quality of its data transmission.

[0003] On the other hand, the transmission power of GW has a significant impact on the data transmission rate, coverage range and interference range. Therefore, rationally planning the deployment location of GW and adjusting its transmission power are important challenges that cannot be ignored in the deployment of WiFi networks. In order to solve the problem of joint deployment of multiple GWs, researchers have proposed a variety of effective algorithms. For example, an allocation algorithm based on partially overlapping channels was proposed. This algorithm theoretically calculates the interference range of adjacent links and allocates channels for all links in the network, thereby effectively reducing the overall interference of the network. In addition, some studies have optimized the location of access points GW by accurately predicting the received signal strength indication, reducing complexity by dividing the test area into grids and excluding inappropriate location combinations, thereby improving signal coverage quality. Other researchers have proposed a dynamic transmission power control method based on wireless link occupancy, which reduces the transmission power while ensuring the data transmission rate, effectively reducing the interference between the same channel and adjacent channels. In view of the constraint condition of the probability of service interruption, some scholars have proposed an algorithm that combines GW location and coverage radius optimization to achieve the goal of maximizing the average throughput of the terminal in the dynamic state.

[0004] In existing research, heuristic algorithms are widely used in solutions to GW deployment problems due to their strong adaptability and high solution efficiency. For example, in a complex three-dimensional heterogeneous medium environment, a genetic algorithm is used to optimize the GW position to improve the overall signal strength of the terminal device; in a non-uniform deployment environment, the GW position and transmission power are jointly optimized to ensure full coverage while reducing the total transmission power and network overlap rate. There are also studies that use the fruit fly optimization algorithm to significantly improve the network coverage under the conditions of a specified number of GWs and transmission power.

[0005] Although the above studies have conducted in-depth analysis and optimization of wireless network interference issues from aspects such as GW deployment location, power control, and channel interference management, most of these studies focus on downlink performance optimization. In industrial digital workshops, the uplink (i.e., workshop data uploaded to the server) often faces greater data transmission requirements and task loads than the downlink (server instructions sent to the device). Therefore, the stability of the uplink is crucial. In addition, considering the differences in data transmission rate requirements of heterogeneous STAs, how to reasonably allocate wireless network resources to ensure transmission fairness is still an urgent problem to be solved. Summary of the invention

[0006] The purpose of the present invention is to provide a wireless network multi-gateway joint deployment method for a digital twin workshop.

[0007] In view of the shortcomings of the prior art, the present invention proposes a multi-GW joint deployment method for DTW. First, based on the SINR model, the influence of GW position, channel allocation and transmission power on STA uplink data transmission rate and adjacent link interference is deeply analyzed, and an optimization model of SSR is constructed; secondly, by mapping AASO with the multi-GW deployment problem, a multi-GW joint deployment strategy based on AASO is formed, which realizes the efficient allocation of wireless resources, reduces link interference, and improves the concurrency capability of the network. The optimized deployment method of the present invention significantly improves SSR, expands the coverage of high-quality wireless networks, and demonstrates good applicability and robustness under different experimental conditions. The research results provide technical support for the wireless network planning and design of DTW, which helps relevant personnel to evaluate the network's ability to meet the transmission requirements of equipment and optimize network design. By building an efficient and sustainable wireless network architecture, the present invention aims to promote the further development of smart factories and digital workshops, improve overall production efficiency and the reliability of information transmission, so as to better meet the needs of modern industry.

[0008] The present invention is achieved through the following technical solutions

[0009] The present invention relates to a wireless network multi-gateway joint deployment method for a digital twin workshop, comprising the following steps: analyzing the influence of gateway position, channel and power on STA data transmission rate and adjacent link interference based on a signal to noise ratio model, and constructing an SSR optimization model; and mapping AASO with multi-GW deployment problems to form a multi-GW joint deployment strategy based on AASO.

[0010] Preferably, the specific steps of the above-mentioned wireless network multi-gateway joint deployment method for digital twin workshops are:

[0011] Step 1: Initialize the deployment plan

[0012] For multi-GW deployment, an optimization algorithm based on AASO is designed. The specific steps are as follows: Assume that the population size is pop, and the dimension of each marching ant is D = 4×num GW , which contains the deployed num GW The triplet of GWs, (x i ,y i ), f i , Pt i >, where (x i ,y i ) is the position coordinate of GW, f i is the channel occupied by GW, Pt i is the transmission power of GW; the initial positions of all army ants are freely distributed in the search space, which is expressed as follows:

[0013]

[0014] Then, based on the calculation formula of the SSR optimization model, the SSR value that each army ant in the population can obtain is evaluated; the four deployment plans with the highest SSR in the initialization plan set are used as the initial potential prey positions for subsequent optimization steps;

[0015] Step 2: Update the deployment plan set

[0016] The number of army ants affected by each prey in each iteration follows a Poisson distribution, and the number of army ants affected each time is determined by roulette. If in the tth iteration, Ant i Prey j Attraction, the update formula of its position coordinates is:

[0017]

[0018] Among them, Position t (Prey j ,Ant i ) is the number of prey Prey in the tth iteration j Ant i attractiveness; It is the location of the prey; is the position of the army ant; Θ is the Hadamard product operator; N(0,1) 1×D is a 1×D matrix whose elements follow a standard Gaussian distribution; in each iteration, the ant Ant i Will attack based on the impact of different types of prey, Ant i The position update formula is:

[0019]

[0020] in, is the attack coefficient, usually 2; is a random number between (0,1], which is The product of reflects the acceleration weight of the army ants moving towards the prey; In the tth iteration, Ant i The number of prey; in a single iteration, if Ant i Not attracted by any prey, Ant i Through the information sharing mechanism within the population, any two army ants in the population are randomly selected to update their positions. The mathematical expression is:

[0021]

[0022] Where Cauchy(0,1) 1×D is a 1×D dimensional matrix whose elements obey the standard Cauchy distribution. To avoid falling into the local optimal solution, AASO designs the ant bridge mechanism to explore the global space. When the position of the prey does not change in multiple consecutive iterations, half of the army ants with poor fitness will form an ant bridge. The army ants use the ant bridge as a benchmark and randomly select a dimension for mutation. If the fitness value after mutation is better than that before mutation, the position will be saved in the next iteration to explore the new space.

[0023] Step 3: Calculate the SSR of each ant and update the deployment plan

[0024] After each iteration, the obtained The best deployment schemes are generated and the army ants are required to update their positions according to these deployment schemes. represents the number of prey in the tth iteration. As the army ants continue to attack, the number of prey continues to decrease. Its mathematical expression is:

[0025]

[0026] Where t is the current iteration number, num iteration is the maximum number of iterations;

[0027] Step 4: Determine the termination condition of the search

[0028] The optimization algorithm executes the above steps until one of the following termination conditions is met: 1. The difference between the current solution and the target solution is less than the preset accuracy threshold; 2. The maximum number of iterations is reached.

[0029] Preferably, the method for establishing the SSR optimization model is specifically as follows:

[0030] When two adjacent nodes in the POC are within each other's interference range, the links of the two nodes must not be activated at the same time to ensure successful transmission; and The stability of the gateway k and a i They should be within the co-channel propagation range of their respective transmitting nodes;

[0031] Transmitting nodes l , its co-channel propagation range is expressed as:

[0032]

[0033] Among them, C t is the path loss constant, which is related to the height and gain of the transmitting antenna and the receiving antenna; SINR min Indicates the minimum SINR required for a STA node to access the network;

[0034] When j In s l The interference range d I (s l ,s j ), the two STAs cannot activate their communication links at the same time due to interference. and when and When in POC, channel f i The device in can only receive channel f k The signal power in the middle part, at this time s l The adjacent channel interference radius is:

[0035]

[0036] in, is the interference radius factor, with a value range of [0,1], which is used to quantify the effect of POC on the reduction of the adjacent channel interference range;

[0037] Assume j and l The link distance between them is d(s j ,s l ), if d(s j ,s l )≥max{d I (s j ,s l ),d I (s l ,s j )},but and The nodes on the channel are not within the interference range of each other's adjacent channels, that is, s j and l There is no link interference between them;

[0038] Based on the above analysis, for any STA, the condition that it does not interfere with the adjacent link is determined by the node position, channel and power of the adjacent GW.

[0039] Define a collection It is used to represent STAs that will not interfere with adjacent links. The size of this set directly reflects the effectiveness of the deployment scheme in reducing link interference. The formula is as follows:

[0040]

[0041] In the uplink, the sender s j The MCS is selected by the receiver a i The SINR decision; when s j ∈S(a i ) successfully to a i When sending data, since the channel is occupied, S(a i ) cannot send a i Send data to avoid STAs in the same basic service set (BSS) from sending data to a i interference;

[0042] By calculating the channel interference range between STAs, it is determined whether the links can be activated simultaneously. If d(s j ,s l )<max{d I (s j ,s l ),d I (s l ,s j )},but and cannot be activated at the same time, so nodes within the interference range of sj will not affect a i Cause interference;

[0043] So, a i The interference comes from s j Nodes in other BSSs outside the interference range, because at the same time, at most one link in a BSS is in working state, at most one sending node will interfere with a i produces interference, so a i The maximum interference is:

[0044]

[0045] Among them, n k Indicates that i In the adjacent channel state, the distance a in each BSS i The nearest node; IN is n k The collection of Pt k Indicates n k The transmission power of Δ (n k ,s j ) is the channel overlap, which increases with n k and j The distance between channels increases and decreases; d(n k ,a i ) is n k with a i The Euclidean distance between them; θ is the path attenuation index; a i The minimum received SINR is expressed as:

[0046]

[0047] Where N is the background noise, which can be ignored. Since the interference power received by GW is much greater than the noise power in the channel, the background noise will be ignored in subsequent studies for the convenience of discussion. Depending on the data transfer rate required Find the corresponding SINR threshold Among them, the following needs to be met: j The actual data transfer rate r j Greater than or equal to when a i The received SINR is equal to When i With s j The maximum acceptable distance between them is:

[0048]

[0049] If a i With s j The distance d(a i ,s j )≤R max (a i ,s j ), and It is concluded that for any STA, its transmission rate is jointly affected by the node location, channel allocation and power control of all GWs;

[0050] Define a collection Represents all STAs that can communicate at the required transmission rate; the size of this set directly reflects the degree to which the deployment scheme meets the heterogeneous STA data transmission rate requirements:

[0051]

[0052] The SSR optimization model is constructed using the normalization method, aiming to maximize the number of STAs that meet the transmission rate requirements and avoid interference with adjacent links. The mathematical expression of the model is as follows:

[0053]

[0054] Where n represents the total number of STAs.

[0055] The present invention establishes a multi-GW joint deployment model for wireless networks oriented to DTW, and proposes a multi-GW joint deployment method based on AASO. By rationally configuring the location, channel resources and transmission power of GW, the utilization of network resources is optimized, and efficient data transmission of heterogeneous STAs is guaranteed, so as to achieve the purpose of reducing link interference and improving SSR. The present invention estimates the SINR of each STA uplink, selects the optimal MCS under the premise of ensuring transmission reliability, and ensures that STA obtains the maximum data transmission rate while avoiding interference with adjacent links. This technology optimizes the fairness of transmission in a multi-GW deployment environment and improves network concurrency and reliability.

[0056] The present invention has the following advantages:

[0057] (1) The present invention proposes a multi-GW joint deployment strategy based on AASO. By reasonably configuring the location, channel resources and transmission power of GW, it ensures that as many STAs as possible operate at the required data transmission rate while avoiding interference with adjacent links. This method significantly improves the SSR and network concurrency performance and optimizes the allocation of overall wireless network resources.

[0058] (2) Based on the signal-to-interference-noise ratio model, the present invention analyzes the impact of gateway location, channel and power on STA data transmission rate and adjacent link interference, constructs an SSR optimization model, and proposes a multi-GW joint deployment method based on an army ant search optimizer.

[0059] (3) The present invention utilizes adaptive rate control and anti-interference technology to achieve wide coverage of wireless high-quality network services and significantly improve terminal node satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a comparison chart of the SSR and coverage performance between the non-overlapping channel strategy and the present invention;

[0061] Figure 2 It is a comparison chart of the fixed GW location strategy and the present invention in terms of SSR and coverage performance;

[0062] Figure 3 It is a comparison chart of the fixed power strategy and the present invention in SSR performance;

[0063] Figure 4 It is a comparison chart of the coverage performance between the fixed power strategy and the present invention;

[0064] Figure 5 It is a schematic diagram of a wireless network multi-gateway joint deployment method for a digital twin workshop of the present invention;

[0065] Figure 6 It is a schematic diagram of a wireless network multi-gateway joint deployment method for a digital twin workshop according to an embodiment of the present invention;

[0066] Figure 7 It is a schematic diagram of a wireless network multi-GW joint deployment method for a digital twin workshop of the present invention;

[0067] Figure 8 It is a schematic diagram of the deployment of the wireless network GW of the digital twin workshop of the present invention. DETAILED DESCRIPTION

[0068] The present invention is described in detail below in conjunction with specific embodiments. It should be noted that the following embodiments are only further descriptions of the present invention, but the protection scope of the present invention is not limited to the following embodiments.

[0069] Example

[0070] This embodiment relates to a wireless network multi-gateway joint deployment method for a digital twin workshop. Figure 5 and Figure 6 As shown:

[0071] Step 1: Initialize the deployment plan

[0072] For multi-GW deployment, an optimization algorithm based on AASO is designed. The specific steps are as follows: Assume that the population size is pop, and the dimension of each marching ant is D = 4×num GW , which contains the deployed num GW The triplet of GW<(x i ,y i ),f i ,Pt i >, the initial positions of all marching ants are freely distributed in the search space, which is expressed as follows:

[0073]

[0074] Then, based on the calculation formula of the SSR optimization model, the SSR value that each army ant in the population can obtain is evaluated; the four deployment plans with the highest SSR in the initialization plan set are used as the initial potential prey positions for subsequent optimization steps;

[0075] Step 2: Update the deployment plan set

[0076] The number of army ants affected by each prey in each iteration follows a Poisson distribution, and the number of army ants affected each time is determined by roulette. If in the tth iteration, Ant i Prey j Attraction, the update formula of its position coordinates is:

[0077]

[0078] Where Θ is the Hadamard product operator; N(0,1) 1×D is a 1×D matrix whose elements follow a standard Gaussian distribution. In each iteration, the ant Ant i Will attack based on the impact of different types of prey, Ant i The position update formula is:

[0079]

[0080] in, is the attack coefficient, usually 2; is a random number between (0,1], which is The product of reflects the acceleration weight of the army ants moving towards the prey; In the tth iteration, Ant i The number of prey; in a single iteration, if Ant i Not attracted by any prey, Ant i Through the information sharing mechanism within the population, any two army ants in the population are randomly selected to update their positions. The mathematical expression is:

[0081]

[0082] Where Cauchy(0,1) 1×D is a 1×D dimensional matrix whose elements follow the standard Cauchy distribution; in order to avoid falling into the local optimal solution, AASO designed an ant bridge mechanism to explore the global space. When the position of the prey does not change in multiple consecutive iterations, half of the marching ants with poor fitness will form an ant bridge; the marching ants use the ant bridge as a benchmark and randomly select a dimension for mutation; if the fitness value after mutation is better than before mutation, the position will be saved to the next iteration to explore new space;

[0083] Step 3: Calculate the SSR of each ant and update the deployment plan

[0084] After each iteration, the obtained The best deployment plan is generated, and the army ants are required to update their positions according to these deployment plans; represents the number of prey in the tth iteration. As the army ants continue to attack, the number of prey continues to decrease. Its mathematical expression is:

[0085]

[0086] Where t is the current iteration number, num iteration is the maximum number of iterations;

[0087] Step 4: Determine the termination condition of the search

[0088] The optimization algorithm executes the above steps until one of the following termination conditions is met: 1. The difference between the current solution and the target solution is less than the preset accuracy threshold; 2. The maximum number of iterations is reached.

[0089] Furthermore, the method for establishing the SSR optimization model is specifically as follows:

[0090] When two adjacent nodes in the POC are within each other's interference range, the links of the two nodes must not be activated at the same time to ensure successful transmission; and The stability of k and a i They should be within the co-channel propagation range of their respective transmitting nodes;

[0091] Transmitting nodes l , its co-channel propagation range is expressed as:

[0092]

[0093] Among them, C t is the path loss constant, which is related to the height and gain of the transmitting antenna and the receiving antenna; SINR min Indicates the minimum SINR required for a STA node to access the network;

[0094] When j In s l The interference range d I (s l ,s j ), the two STAs cannot activate their communication links at the same time due to interference. and when and When in POC, channel f iThe device in can only receive channel f k The signal power in the middle part, at this time s l The adjacent channel interference radius is:

[0095]

[0096] in, is the interference radius factor, with a value range of [0,1], which is used to quantify the effect of POC on the reduction of the adjacent channel interference range;

[0097] Assume j and l The link distance between them is d(s j ,s l ), if d(s j ,s l )≥max{d I (s j ,s l ),d I (s l ,s j )},but and The nodes on the channel are not within the interference range of each other's adjacent channels, that is, s j and l There is no link interference between them;

[0098] Based on the above analysis, for any STA, the condition that it does not interfere with the adjacent link is determined by the node position, channel and power of the adjacent GW.

[0099] Define a collection It is used to represent STAs that will not interfere with adjacent links. The size of this set directly reflects the effectiveness of the deployment scheme in reducing link interference. The formula is as follows:

[0100]

[0101] In the uplink, the sender s j The MCS is selected by the receiver a i The SINR decision; when s j ∈S(a i ) successfully to a i When sending data, since the channel is occupied, S(a i ) cannot send a i Send data to avoid STAs in the same BSS from sending data to a i interference;

[0102] By calculating the channel interference range between STAs, it is determined whether the links can be activated simultaneously. If d(sj ,s l )<max{d I (s j ,s l ),d I (s l ,s j )},but and Cannot be activated at the same time, so in s j Nodes within the interference range will not interfere with a i Cause interference;

[0103] So, a i The interference comes from s j Nodes in other BSSs outside the interference range, because at the same time, at most one link in a BSS is in working state, at most one sending node will interfere with a i produces interference, so a i The maximum interference is:

[0104]

[0105] Among them, n k Indicates that i In the adjacent channel state, the distance a in each BSS i The nearest node; IN is n k The collection of Pt k Indicates n k The transmission power of Δ (n k ,s j ) is the channel overlap, which increases with n k and j The distance between channels increases and decreases; d(n k ,a i ) is n k with a i The Euclidean distance between them; θ is the path attenuation index; a i The minimum received SINR is expressed as:

[0106]

[0107] The present invention is based on the existing adaptive rate control technology, by avoiding interference between STA and adjacent links, calculating the minimum SINR that STA can receive under preset constraints, and using this as the basis for selecting MCS; Figure 8 As shown, the present invention deploys multiple GWs to meet the network service requirements of different STAs in DTW, thereby achieving real-time synchronous update of physical entity and virtual entity status;

[0108] Among them, N is the background noise, which can be ignored. Since the interference power received by GW is much larger than the noise power in the channel, the background noise will be ignored in subsequent studies for the convenience of discussion. in accordance with Find the corresponding SINR threshold Among them, the following needs to be met: j R j Greater than or equal to when a i The received SINR is equal to When i With s j The maximum acceptable distance between them is:

[0109]

[0110] If a i With s j The distance d(a i ,s j )≤R max (a i ,s j ), and It is concluded that for any STA, its transmission rate is jointly affected by the node location, channel allocation and power control of all GWs;

[0111] Define a collection Represents all STAs that can communicate at the required transmission rate; the size of this set directly reflects the degree to which the deployment scheme meets the heterogeneous STA data transmission rate requirements:

[0112]

[0113] The SSR optimization model is constructed using the normalization method, aiming to maximize the number of STAs that meet the transmission rate requirements and avoid interference with adjacent links. The mathematical expression of the model is as follows:

[0114]

[0115] In the present invention, the STA set and the GW set are defined as S and A respectively. Its resource parameters are defined as a triple <(x i ,y i ),f i ,Pt i >, where (x i ,y i ) is a i The position coordinates, f ifor a i Occupied channels, Pt i for a i The transmission power of STA s j , whose resource parameters are defined as a tuple, including its location coordinates (x' j ,y' j ) and the required data transfer rate After the GW deployment is completed, each STA selects the GW with the strongest signal to establish a connection based on the received signal strength indication, thus forming a BSS. i ) indicates that i Connected STAs. All STAs in the same BSS share the same channel, and their transmission power is consistent with that of their corresponding GW.

[0116] verify:

[0117] This paper proposes a multi-GW joint deployment strategy based on AASO, which ensures that as many STAs as possible can operate at the required data transmission rate while avoiding interference with adjacent links by reasonably configuring the location, channel resources and transmission power of GW. This method significantly improves SSR and network concurrency performance and optimizes the allocation of overall wireless network resources.

[0118] To verify the above key technologies, the present invention uses MATLAB simulation tools to carry out experiments, specifically:

[0119] 200 STAs are randomly deployed in an area of ​​100m×100m, and the performance of the proposed method and three baseline strategies in terms of SSR and network coverage are compared. The experimental results show that Figure 1 As shown in the figure, compared with the GW deployment strategy using non-overlapping channels, the SSR of the present invention in different network environments is improved by an average of 19.89%, and full coverage can be achieved when the number of GWs is 20, which saves 2 GW nodes compared to the non-overlapping channel strategy. Figure 2 It can be seen from the figure that compared with the fixed GW location strategy, the SSR of the present invention is improved by 3.66% on average. Figure 3 and Figure 4 It can be seen that compared with the fixed GW transmission power strategy, the SSR of the present invention in different network environments is improved by an average of 7.34%, and compared with the strategy of 18dBm transmission power, 3 GW can be saved, and compared with 19dBm, 2 GW can be saved.

[0120] The multi-GW joint deployment method of the present invention is as follows Figure 7As shown. The present invention reasonably deploys multiple GW nodes to meet the network service requirements of different STAs in DTW, thereby realizing real-time updates of the status of physical entities and virtual entities. The dotted circle in the figure represents the coverage range of the GW, and the annotated information includes optimization parameters such as the location, channel selection and transmission power of the GW, which are the key optimization objects of the present invention. The present invention uses AASO to optimize the deployment scheme of the GW. The figure shows that the army ants determine the final attack direction through the odor tracking and recruitment mechanism, thereby updating the GW position, channel allocation and transmission power in their respective representative deployment schemes. Through multiple iterations, the army ants gradually find the deployment scheme with the highest SSR, and finally achieve wide coverage of high-quality wireless network services.

[0121] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various modifications or variations within the scope of the claims, which will not affect the essence of the present invention.

Claims

1. A wireless network multi-gateway joint deployment method for a digital twin workshop, characterized in that: The following steps are involved: Based on the signal-to-interference-noise ratio model, the influence of gateway location, channel and power on STA data transmission rate and adjacent link interference was analyzed, and an SSR optimization model was constructed. By mapping AASO with the multi-GW deployment problem, a multi-GW joint deployment strategy based on AASO was formed.

2. The wireless network multi-gateway joint deployment method for a digital twin workshop according to claim 1, characterized in that: The specific steps are: Step 1: Initialize the deployment plan For multi-GW deployment, an optimization algorithm based on AASO is designed. The specific steps are as follows: Assume that the population size is pop, and the dimension of each marching ant is D = 4×num GW , which contains the deployed num GW The triplet of GWs (x i ,y i ),f i ,Pt i >, where (x i ,y i ) is the position coordinate of GW, f i is the channel occupied by GW, Pt i is the transmission power of GW; the initial positions of all army ants are freely distributed in the search space, which is expressed as follows: Then, based on the calculation formula of the SSR optimization model, the SSR value that each army ant in the population can obtain is evaluated; the four deployment plans with the highest SSR in the initialization plan set are used as the initial potential prey positions for subsequent optimization steps; Step 2: Update the deployment plan set The number of army ants affected by each prey in each iteration follows a Poisson distribution, and the number of army ants affected each time is determined by roulette. If in the tth iteration, Ant i Prey j Attraction, the update formula of its position coordinates is: Among them, Position t (Prey j ,Ant i ) is the number of prey Prey in the tth iteration j Ant i attractiveness; It is the location of the prey; is the position of the army ant; Θ is the Hadamard product operator; N(0,1) 1×D is a 1×D matrix whose elements follow a standard Gaussian distribution; in each iteration, the ant Ant i Will attack based on the impact of different types of prey, Ant i The position update formula is: in, is the attack coefficient, usually 2; k is a random number between (0,1], which is related to The product of reflects the acceleration weight of the army ants moving towards the prey; In the tth iteration, Ant i The number of prey; in a single iteration, if Ant i Not attracted by any prey, Ant i Through the information sharing mechanism within the population, any two army ants in the population are randomly selected to update their positions. The mathematical expression is: Where Cauchy(0,1) 1×D is a 1×D dimensional matrix whose elements follow the standard Cauchy distribution; when the position of the prey does not change in multiple consecutive iterations, half of the army ants with poor fitness will form an ant bridge; the army ants use the ant bridge as a benchmark and randomly select a dimension for mutation; if the fitness value after mutation is better than before mutation, the position will be saved to the next iteration to explore new space; Step 3: Calculate the SSR of each ant and update the deployment plan After each iteration, the obtained The best deployment plan is generated, and the army ants are required to update their positions according to these deployment plans; represents the number of prey in the tth iteration. As the army ants continue to attack, the number of prey continues to decrease. Its mathematical expression is: Where t is the current iteration number, num iteration is the maximum number of iterations; Step 4: Determine the termination condition of the search The optimization algorithm executes the above steps until one of the following termination conditions is met:

1. The difference between the current solution and the target solution is less than the preset accuracy threshold; 2. The maximum number of iterations is reached.

3. The wireless network multi-gateway joint deployment method for a digital twin workshop according to claim 2, characterized in that: The method for establishing the SSR optimization model is specifically as follows: When two neighboring nodes on partially overlapping channels are within each other's interference range, the links of the two nodes must not be activated at the same time to ensure successful transmission; k and a i Within the same channel propagation range of their respective transmitting nodes, the uplink and In a stable state; Transmitting nodes l , its co-channel propagation range is expressed as: Among them, C t is the path loss constant, which is related to the height and gain of the transmitting antenna and the receiving antenna; SINR min Indicates the minimum SINR required for a STA node to access the network; When j In s l The interference range d I (s l ,s j ), the two STAs cannot activate their communication links at the same time due to interference. and when and When in POC, channel f i The device in can only receive channel f k The signal power in the middle part, at this time s l The adjacent channel interference radius is: in, is the interference radius factor, with a value range of [0,1], which is used to quantify the effect of POC on the reduction of the adjacent channel interference range; Assume j and l The link distance between them is d(s j ,s l ), if d(s j ,s l )≥max{d I (s j ,s l ),d I (s l ,s j )},but and The nodes on the channel are not within the interference range of each other's adjacent channels, that is, s j and l There is no link interference between them; Define a collection It is used to represent STAs that will not interfere with adjacent links. The size of this set directly reflects the effectiveness of the deployment scheme in reducing link interference. The formula is as follows: In the uplink, the sender s j The MCS is selected by the receiver a i The SINR decision; when s j ∈S(a i ) successfully to a i When sending data, since the channel is occupied, S(a i ) cannot send messages to a i Send data to avoid STAs in the same basic service set from sending data to a i interference; By calculating the channel interference range between STAs, it is determined whether the links can be activated simultaneously. If d(s j ,s l )<max{d I (s j ,s l ),d I (s l ,s j )},but and Cannot be activated at the same time, nodes within the interference range of sj will not affect a i Cause interference; So, a i The interference comes from s j Nodes in other BSSs outside the interference range, because at the same time, at most one link in a BSS is in working state, at most one sending node will interfere with a i Interference occurs, so a i The maximum interference is: Among them, n k Indicates that i In the adjacent channel state, the distance a in each BSS i The nearest node; IN is n k The collection of Pt k Indicates n k The transmission power of Δ (n k ,s j ) is the channel overlap, which increases with n k and j The distance between channels increases and decreases; d(n k ,a i ) is n k with a i The Euclidean distance between them; θ is the path attenuation index; a i The minimum received SINR is expressed as: Where N is the background noise, which can be ignored; any STA can set the data transmission rate according to its required data transmission rate. Find the corresponding SINR threshold Among them, the following needs to be met: j The actual data transfer rate r j Greater than or equal to Right now when a i The received SINR is equal to When i With s j The maximum acceptable distance between them is: If a i With s j The distance d(a i ,s j )≤R max (a i ,s j ),but and It is concluded that for any STA, its transmission rate is jointly affected by the node location, channel allocation and power control of all GWs; Define a collection Represents all STAs that can communicate at the required transmission rate; the size of this set directly reflects the degree to which the deployment scheme meets the heterogeneous STA data transmission rate requirements: The SSR optimization model is constructed using the normalization method, aiming to maximize the number of STAs that meet the transmission rate requirements and avoid interference with adjacent links. The mathematical expression of the model is as follows: Where n represents the total number of STAs.

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