Method for checking deployment of ground equipment of a bee colony with the aid of expert knowledge

Abstract

The application discloses a kind of expert knowledge auxiliary check colony ground equipment deployment method, comprising: first, according to the prior deployment scheme generated by expert knowledge;Second, according to prior deployment scheme and random initialization, initial deployment scheme is obtained using initialization module based on expert knowledge, and initial deployment scheme is sequentially checked using check module;Then, in optimization stage, obtain neighborhood optimization deployment scheme by simplified neighborhood optimization module, and sequentially check neighborhood optimization deployment scheme using check module;Finally, save and output the deployment scheme after this iteration optimization.The method of the application can not only improve the convergence speed in the optimization process of deployment scheme, reduce optimization time, but also improve operational effectiveness.

Description

Technical Field

[0001] This invention relates to an expert knowledge-assisted verification method for deploying swarm ground equipment, belonging to the field of large-scale regional air defense and anti-missile technology. Technical Background

[0002] Modern regional air defense operations primarily utilize various ground-based air defense weapons (such as anti-aircraft missiles, anti-aircraft guns, and anti-aircraft rockets) to intercept various types of combat aircraft, missiles, and other air attack weapons. Pre-battle air defense fire deployment requires simultaneous analysis of both friendly and enemy situations. Based on the analysis results, and considering factors such as climate and terrain, a deployment plan is determined, and firepower is rationally allocated. However, the fire deployment problem is a non-deterministic polynomial (NP) problem, and the solution time of traditional methods increases exponentially with the problem size. In large-scale air attack operations, hundreds or even thousands of weapons and equipment may be deployed in a single operation. Traditional weapon deployment methods, such as enumeration and plane-cutting methods, cannot meet the real-time requirements of operations. Therefore, finding a ground equipment deployment method with strong real-time performance and high solution accuracy is of paramount importance for modern large-scale regional air defense operations. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides an expert knowledge-assisted verification method for deploying bee colony ground equipment. First, a priori deployment plan is generated based on expert knowledge. Then, based on the priori deployment plan and random initialization, an initial deployment plan is obtained using an expert knowledge-based initialization module. Finally, a neighborhood-optimized deployment plan is obtained through a simplified neighborhood optimization module. Furthermore, in both the initialization and optimization phases, a verification module is used to verify the newly generated deployment plan. Once a deployment plan that meets the penetration rate requirements and is deemed suitable for placement is generated, it is immediately saved, output, and the optimization process exits.

[0004] The specific invention solution is as follows:

[0005] Expert knowledge assists in verifying the deployment methods of bee colony ground equipment, including the following steps:

[0006] S1, Setting the required penetration rate P Th Based on the regional segmentation information and expert knowledge, a priori deployment plan X0 is generated, the penetration rate P0 of the priori deployment plan X0 is calculated, and X0 and P0 are assigned to the optimized deployment plan X, respectively. Opt And optimize penetration rate P Opt , represented as X Opt =X0 and P Opt =P0;

[0007] The regional segmentation information includes natural information such as topography, climate, soil, and vegetation, as well as human factors including human activities and cultural heritage.

[0008] The required penetration rate P Th Based on engineering experience and actual needs;

[0009] The penetration rate P0 is calculated based on the weighting coefficient of the deployment location and the performance parameters of the deployed weapons;

[0010] The expert knowledge mentioned includes the scope of defense, attack methods, and protection conditions;

[0011] The aforementioned support conditions include command and communication, logistical support, and weaponry and equipment;

[0012] S2. Based on the prior deployment scheme X0 and combined with random initialization, obtain the initial deployment scheme using the expert knowledge-based initialization module. r = 1, 2, ..., N SchInit and penetration rate r = 1, 2, ..., N SchInit Use the verification module to determine the scheme. Is the selected deployment location suitable? If so, then proceed with the plan. and penetration rate Assign values ​​to optimize deployment scheme X respectively Opt and optimize penetration rate P Opt If the condition is met, proceed to step S5; otherwise, proceed to step S3.

[0013] The number N of the initial deployment schemes SchInit Based on engineering experience and actual needs;

[0014] The verification module determines whether the selected deployment location is suitable for placing the selected weapon type based on the geographical information of the selected deployment location in the current deployment plan.

[0015] The geographic information includes latitude, longitude, altitude, land use rate, climate and environment, human activities, and natural disasters;

[0016] S3. Based on neighborhood optimization using traditional artificial bee colony optimization algorithms, and according to the initial deployment plan... r = 1, 2, ..., N SchInit N is obtained using a simplified neighborhood optimization module. NearOpt Individual neighborhood optimization deployment scheme m = 1, 2, ..., N NearOpt and penetration rate m = 1, 2, ..., N NearOpt Use the verification module to determine the scheme. Check if the selected deployment location is suitable. If the verification conditions are met, then proceed with the plan. and penetration rate Assign values ​​to the optimized deployment scheme X respectively Opt and optimize penetration rate P Opt If the condition is met, proceed to step S5; otherwise, proceed to step S4.

[0017] The number N of the neighborhood optimization deployment schemes NearOpt Based on engineering experience and actual needs;

[0018] S4. Select the minimum penetration rate P during the optimization phase. min ,Right now And the penetration rate P min and the corresponding deployment scheme X min Assign values ​​to the optimized penetration rate P respectively Opt and optimized deployment scheme X Opt ;

[0019] S5, Optimize deployment plan X Opt and optimize penetration rate P Opt Save and output the optimized deployment plan for this iteration.

[0020] Specifically:

[0021] Step S1 further includes:

[0022] S11. Based on the regional segmentation information and expert knowledge, the ground deployment location (numbered N) can be determined using expert knowledge. i ,i∈[1,N area N grid Placement number a) k For devices with k = 1, 2, ..., K, denoted as ρ{N} i :a k};

[0023] The N area This indicates the number of sub-regions into which the region is divided;

[0024] The N grid The number of ground deployment locations divided into each sub-region;

[0025] The N area N grid The ground deployment locations are sequentially numbered into a set Ω, and represented as: Ω = {N} i}, i = 1, 2, ..., N area N grid ;

[0026] The number of deployed device types is N. typeThe total number of devices is K. All devices are sequentially numbered to form a set O, which is represented as O = {a1, a2, ..., a...}. k ,...,a K};

[0027] S12. The location of the type of equipment cannot be determined based on expert knowledge (numbered N). j ,j∈[1,N area N grid ]), can place any device other than the one already placed there, denoted as ρ{N j :Λ}.

[0028] Step S2 further includes:

[0029] S21. Initialize the loop counter variable r = 0;

[0030] S22. Based on the prior deployment plan X0, the ground deployment location numbers of the already placed equipment together form the set Ω. Pos The ground deployment locations where no equipment was placed together constitute the set Ω. N_Pos The selected device numbers together form set O. Equ The unselected device numbers together form set O. N_Equ Then Ω = Ω Pos ∪Ω N_Pos , O = O Equ ∪Ο N_Equ ;

[0031] S23. Update the loop variable r = r + 1, and check if the loop variable r is greater than N. SchInit If r>N SchInit If so, proceed to step S3; otherwise, proceed to step S24.

[0032] S24. Obtain the r-th initial deployment scheme using the expert knowledge-based initialization module. and penetration rate

[0033] Step S24 further includes:

[0034] S241, From set Ω N_Pos Randomly select N N_Equ Each ground deployment location number constitutes a new set.

[0035] The N N_Equ Equal to set O N_Equ The number of elements;

[0036] S242, Set O N_Equ The devices in the middle are randomly placed into the set. At the corresponding ground deployment location, generate the r-th initial deployment plan. And calculate the deployment plan penetration rate

[0037] S25, Comparison of required penetration rate P Th With deployment plan penetration rate The size relationship, if Save the current deployment plan The breakthrough rate is determined, and the process returns to step S23; otherwise, step S26 is executed.

[0038] S26. Use the verification module to determine the deployment plan. If the verification result indicates that placement is not suitable, save the current deployment plan. and penetration rate Then return to step S23; otherwise, proceed to step S27.

[0039] S27, Deployment Plan and penetration rate Assign values ​​to the optimized deployment scheme X respectively Opt and optimize penetration rate P Opt Then proceed to step S5.

[0040] Step S3 further includes:

[0041] S31. Initialize the loop counter variable m = 0;

[0042] S32. Update the loop variable m = m + 1, and check if the loop variable m is greater than N. NearOpt If m>N NearOpt If so, proceed to step S4; otherwise, proceed to step S33.

[0043] S33. Obtain the m-th neighborhood optimization deployment scheme using the simplified neighborhood optimization module. and penetration rate

[0044] Step S33 further includes:

[0045] S331, According to the m-th initial deployment plan A random position swap index p∈[1,N] is generated. N_Equ ] and a ground location number to be exchanged

[0046] S332. Determine whether the deployment location number corresponding to the location exchange index p belongs to the set Ω. Pos If p∈Ω PosIf the condition is met, proceed to step S331; otherwise, proceed to step S333.

[0047] S333, Deploy the m-th plan The ground deployment location corresponding to the p-th element is replaced with the ground deployment location corresponding to the ground location number l to generate the m-th neighborhood optimized deployment scheme. Then return to continue executing step S32.

[0048] S34, Comparison of required penetration rate P Th With deployment plan penetration rate The size relationship, if The plan and plan Save the scheme with a relatively low penetration rate as the scheme. and penetration rate Right now and Return to step S32; otherwise, proceed to step S35.

[0049] S35. Use the verification module to determine the deployment plan. If the verification result indicates that placement is not suitable, save the current deployment plan. and penetration rate Return to step S32; otherwise, proceed to step S36.

[0050] S36, Deployment Plan and penetration rate Assign values ​​to the optimized deployment scheme X respectively Opt and optimize penetration rate P Opt Then proceed to step S5.

[0051] This invention optimizes deployment schemes based on existing expert knowledge, a simplified neighborhood optimization module, and a verification module. By utilizing existing expert knowledge, compared to traditional optimization methods, the initial deployment scheme generated by this invention at this stage is of higher quality and closer to the optimal deployment scheme, which greatly improves the convergence speed of the optimization process. Simultaneously, the use of the simplified neighborhood optimization module not only significantly reduces the time cost of generating new deployment schemes but also improves the optimization efficiency of the deployment schemes. Furthermore, the application of the verification module makes the optimized deployment scheme more in line with actual combat needs and improves combat effectiveness. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating an embodiment of the present invention.

[0053] Figure 2 This is a flowchart of the initialization phase in an embodiment of the present invention.

[0054] Figure 3 This is a flowchart of the initialization module based on expert knowledge in an embodiment of the present invention.

[0055] Figure 4 This is a flowchart of the optimization stage in an embodiment of the present invention.

[0056] Figure 5 This is a simplified flowchart of the neighborhood optimization module in an embodiment of the present invention. Detailed Implementation

[0057] The present invention will now be described in detail with reference to embodiments and accompanying drawings. However, it should be understood that the embodiments and drawings are for illustrative purposes only and do not constitute any limitation on the scope of protection of the present invention. All reasonable modifications and combinations included within the scope of the inventive spirit of the present invention fall within the scope of protection of the present invention.

[0058] like Figure 1 As shown, the expert knowledge-assisted verification method for deploying bee colony ground equipment includes:

[0059] S1. Setting the required penetration rate P Th Based on the regional segmentation information and expert knowledge, a priori deployment plan X0 is generated, the penetration rate P0 of the priori deployment plan X0 is calculated, and X0 and P0 are assigned to the optimized deployment plan X, respectively. Opt And optimize penetration rate P Opt , represented as X Opt =X0 and P Opt =P0;

[0060] The regional segmentation information includes natural information such as topography, climate, soil, and vegetation, as well as human factors including human activities and cultural heritage.

[0061] The required penetration rate P Th Based on engineering experience and actual needs;

[0062] The penetration rate P0 is calculated based on the weighting coefficient of the deployment location and the performance parameters of the deployed weapons;

[0063] The expert knowledge mentioned includes the scope of defense, attack methods, and protection conditions;

[0064] The aforementioned support conditions include command and communication, logistical support, and weaponry and equipment;

[0065] Step S1 further includes:

[0066] S11. Based on the regional segmentation information and expert knowledge, the ground deployment location (numbered N) can be determined using expert knowledge. i ,i∈[1,N areaN grid Placement number a) k For devices with k = 1, 2, ..., K, denoted as ρ{N} i :a k};

[0067] The N area This indicates the number of sub-regions into which the region is divided;

[0068] The N grid The number of ground deployment locations divided into each sub-region;

[0069] The N area N grid The ground deployment locations are sequentially numbered into a set Ω, and represented as: Ω = {N} i}, i = 1, 2, ..., N area N grid ;

[0070] The number of deployed device types is N. type The total number of devices is K. All devices are sequentially numbered to form a set O, which is represented as O = {a1, a2, ..., a...}. k ,...,a K};

[0071] S12. The location of the type of equipment cannot be determined based on expert knowledge (numbered N). j ,j∈[1,N area N grid ]), can place any device other than the one already placed there, denoted as ρ{N j :Λ}.

[0072] S2. Based on the prior deployment scheme X0 and combined with random initialization, obtain the initial deployment scheme using the expert knowledge-based initialization module. r = 1, 2, ..., N SchInit and penetration rate r = 1, 2, ..., N SchInit Use the verification module to determine the scheme. Is the selected deployment location suitable? If so, then proceed with the plan. and penetration rate Assign values ​​to optimize deployment scheme X respectively Opt and optimize penetration rate P Opt If the condition is met, proceed to step S5; otherwise, proceed to step S3.

[0073] The number N of the initial deployment schemes SchInit Based on engineering experience and actual needs;

[0074] The verification module determines whether the selected deployment location is suitable for placing the selected weapon type based on the geographical information of the selected deployment location in the current deployment plan.

[0075] The geographic information includes latitude, longitude, altitude, land use rate, climate and environment, human activities, and natural disasters;

[0076] like Figure 2 As shown, step S2 further includes:

[0077] S21. Initialize the loop counter variable r = 0;

[0078] S22. Based on the prior deployment plan X0, the ground deployment location numbers of the already placed equipment collectively form the set Ω. Pos The ground deployment locations where no equipment was placed together constitute the set Ω. N_Pos The selected device numbers together form set O. Equ The unselected device numbers together form set O. N_Equ Then Ω = Ω Pos ∪Ω N_Pos , O = O Equ ∪Ο N_Equ ;

[0079] S23. Update the loop variable r = r + 1, and check if the loop variable r is greater than N. SchInit If r>N SchInit If so, proceed to step S3; otherwise, proceed to step S24.

[0080] S24. Obtain the r-th initial deployment scheme using the expert knowledge-based initialization module. and penetration rate

[0081] like Figure 3 As shown, step S24 further includes:

[0082] S241. From set Ω N_Pos Randomly select N N_Equ Each ground deployment location number constitutes a new set. The remaining position numbers form a set and

[0083] The N N_Equ equals set The number of elements;

[0084] S242. Set O N_Equ The devices in the middle are randomly placed into the set. At the corresponding ground deployment location, generate the r-th initial deployment plan. And calculate the deployment plan penetration rate

[0085] S25. Compare the required penetration rate P Th With deployment plan penetration rate The size relationship, if Save the current deployment plan The breakthrough rate is determined, and the process returns to step S23; otherwise, step S26 is executed.

[0086] S26. Use the verification module to determine the deployment plan. If the verification result indicates that placement is not suitable, save the current deployment plan. and penetration rate Then return to step S23; otherwise, proceed to step S27.

[0087] S27. Deployment Plan and penetration rate Assign values ​​to the optimized deployment scheme X respectively Opt and optimize penetration rate P Opt Then proceed to step S5.

[0088] S3. Based on the neighborhood optimization of the traditional artificial bee colony optimization algorithm, according to the initial deployment plan... r = 1, 2, ..., N SchInit N is obtained using a simplified neighborhood optimization module. NearOpt Individual neighborhood optimization deployment scheme m = 1, 2, ..., N NearOpt and penetration rate m = 1, 2, ..., N NearOpt Use the verification module to determine the scheme. Check if the selected deployment location is suitable. If the verification conditions are met, then proceed with the plan. and penetration rate Assign values ​​to the optimized deployment scheme X respectively Opt and optimize penetration rate P Opt If the condition is met, proceed to step S5; otherwise, proceed to step S4.

[0089] The number N of the neighborhood optimization deployment schemes NearOpt Based on engineering experience and actual needs;

[0090] like Figure 4 As shown, step S3 further includes:

[0091] S31. Initialize the loop counter variable m = 0;

[0092] S32. Update the loop variable m = m + 1, and check if the loop variable m is greater than N. NearOpt If m>N NearOpt If so, proceed to step S4; otherwise, proceed to step S33.

[0093] S33. Obtain the m-th neighborhood optimization deployment scheme using the simplified neighborhood optimization module. and penetration rate

[0094] like Figure 5 As shown, step S33 further includes:

[0095] S331. Based on the m-th initial deployment plan A random position swap index p∈[1,N] is generated. N_Equ ] and a ground location number to be exchanged

[0096] S332. Determine whether the deployment location number corresponding to the location swap index p belongs to set Ω. Pos If p∈Ω Pos If the condition is met, proceed to step S331; otherwise, proceed to step S333.

[0097] S333. Deploy the m-th plan. The ground deployment location corresponding to the p-th element is replaced with the ground deployment location corresponding to the ground location number l to generate the m-th neighborhood optimized deployment scheme. Then return to continue executing step S32.

[0098] S34. Compare the required penetration rate P Th With deployment plan penetration rate The size relationship, if The plan and plan Save the scheme with a relatively low penetration rate as the scheme. and penetration rate Right now and Return to step S32; otherwise, proceed to step S35.

[0099] S35. Use the verification module to determine the deployment plan. If the verification result indicates that placement is not suitable, save the current deployment plan. and penetration rate Return to step S32; otherwise, proceed to step S36.

[0100] S36. Deployment plan and penetration rate Assign values ​​to the optimized deployment scheme X respectively Opt and optimize penetration rate P Opt Then proceed to step S5.

[0101] S4. Select the minimum penetration rate P during the optimization phase. min ,Right now And the penetration rate P min and the corresponding deployment scheme X min Assign values ​​to the optimized penetration rate P respectively Opt and optimized deployment scheme X Opt ;

[0102] S5. Optimize deployment plan X Opt and optimize penetration rate P Opt Save and output the optimized deployment plan for this iteration.

[0103] Example 1

[0104] The following detailed explanation of step S1 will be provided with specific examples.

[0105] Suppose there are currently 10 deployment locations, and the set of their location numbers is Ω = {p1, p2, ..., p...} 10 There are three types of equipment: A, B, and C. There are 3 pieces of equipment in type A, 2 pieces of equipment in type B, and 1 piece of equipment in type C. What is the set of equipment numbers?

[0106]

[0107] Assuming that based on existing expert knowledge, it can be determined that the deployment locations corresponding to device numbers a6 and a1 are respectively placed at locations p1 and p3, i.e., ρ{p1,a6} and ρ{p3,a1}; then the set Ω N_Pos ={p2,p4,p5,...,p 10 The deployment position corresponding to the element in} can be used to place the set O. N_Pos = any device type corresponding to the elements in {a2,a2,...,a5}. In this case, the prior deployment scheme X0 is X0 = [ρ{p1,a6},ρ{p2,Λ},ρ{p3,a1},ρ{p4,Λ},...,ρ{p...]. 10 ,Λ}).

[0108] Example 2

[0109] The following detailed explanation of step S33 will be provided with specific examples.

[0110] Assume the first initial deployment plan to be optimized is selected. It can be represented as Given a randomly generated location exchange index p = 4 and a ground location number to be exchanged l = p9, the deployment location number corresponding to the location exchange index p does not belong to the set Ω. Pos In the deployment plan In the middle, the deployment position corresponding to the fourth element is changed from p4 to p9 to generate the first neighborhood-optimized deployment scheme.

[0111] The above embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An expert-assisted verification method for deploying bee colony ground equipment, characterized in that: Includes the following steps: S1, Setting the required penetration rate Based on regional segmentation information and expert knowledge, a preliminary deployment plan is generated. Calculate the prior deployment scheme penetration rate and will and Assign values ​​to the optimized deployment scheme respectively And optimize penetration rate , represented as and ; The regional segmentation information includes natural information such as topography, climate, soil, and vegetation, as well as human factors including human activities and cultural heritage. The required penetration rate Based on engineering experience and actual needs; The aforementioned penetration rate It is calculated based on the weighting coefficient of the deployment location and the performance parameters of the deployed weapons; The expert knowledge mentioned includes the scope of defense, attack methods, and protection conditions; The aforementioned support conditions include command and communication, logistical support, and weaponry and equipment; S2, Based on the prior deployment plan In addition to random initialization, an initial deployment plan is obtained using an initialization module based on expert knowledge. and penetration rate ; Use the verification module to determine the scheme Is the selected deployment location suitable? If so, then proceed with the plan. and penetration rate Assign values ​​to optimize deployment schemes and optimize penetration rate If the condition is met, proceed to step S5; otherwise, proceed to step S3. The number of initial deployment schemes mentioned Based on engineering experience and actual needs; The verification module determines whether the selected deployment location is suitable for placing the selected weapon type based on the geographical information of the selected deployment location in the current deployment plan. The geographic information includes latitude, longitude, altitude, land use rate, climate and environment, human activities, and natural disasters; S3. Based on neighborhood optimization using traditional artificial bee colony optimization algorithms, and according to the initial deployment plan... , Obtained using a simplified neighborhood optimization module Individual neighborhood optimization deployment scheme , and penetration rate , ; Use the verification module to determine the scheme Check if the selected deployment location is suitable. If the verification conditions are met, then proceed with the plan. and penetration rate Assign values ​​to the optimized deployment scheme respectively and optimize penetration rate If the condition is met, proceed to step S5; otherwise, proceed to step S4. The number of neighborhood optimization deployment schemes Based on engineering experience and actual needs; S4. Select the minimum penetration rate during the optimization phase. ,Right now and the penetration rate and corresponding deployment solutions Assign values ​​to the optimized penetration rate respectively and optimized deployment plan ; S5, Optimize deployment plan and optimize penetration rate Save and output the optimized deployment plan for this iteration.

2. The expert knowledge-assisted verification method for deploying bee colony ground equipment according to claim 1, characterized in that, Step S1 further includes: S11. Based on the regional segmentation information and expert knowledge, place the ground deployment locations that can be determined by expert knowledge by assigning them the following numbers: The device is represented as The ground deployment location is numbered as follows: ; The This indicates the number of sub-regions into which the region is divided; The The number of ground deployment locations divided into each sub-region; The aforementioned The ground deployment locations are sequentially numbered into sets. And expressed as: ; The number of deployed device types is: The total number of devices is All devices are numbered sequentially to form a set. , and represent as ; S12. If expert knowledge cannot determine the location of what type of equipment to place, placing any equipment other than the already placed equipment is indicated as... The location number cannot be determined as to be where the type of equipment is placed. .

3. The expert knowledge-assisted verification method for deploying bee colony ground equipment according to claim 2, characterized in that, Step S2 further includes: S21. Initialize the loop counter variable ; S22, Based on the prior deployment plan The set consists of the ground deployment location numbers of the already placed equipment. The set consists of the ground deployment location numbers where no equipment has been placed. The selected device numbers together constitute a set. The unselected device numbers together constitute the set. ,but , ; S23, Update the loop variable Check the loop variable Is it greater than ,like If so, proceed to step S3; otherwise, proceed to step S24. S24. Obtain the first... using the expert knowledge-based initialization module. Initial deployment plan and penetration rate ; S25, Comparison requirements for penetration rate With deployment plan penetration rate The size relationship, if Save the current deployment plan The breakthrough rate is determined, and the process returns to step S23; otherwise, step S26 is executed. S26. Use the verification module to determine the deployment plan. If the verification result indicates that placement is not suitable, save the current deployment plan. and penetration rate If the condition is met, return to step S23; otherwise, proceed to step S27. S27, Deployment Plan and penetration rate Assign values ​​to the optimized deployment scheme respectively and optimize penetration rate Then proceed to step S5.

4. The expert knowledge-assisted verification method for deploying bee colony ground equipment according to claim 3, characterized in that, Step S24 further includes: S241, From the set Random selection Each ground deployment location number constitutes a new set. ; The aforementioned equals set The number of elements; S242, Set The devices in the middle are randomly placed into the set. At the corresponding ground deployment location, to generate the first Initial deployment plan And calculate the deployment plan penetration rate .

5. The expert knowledge-assisted verification method for deploying bee colony ground equipment according to claim 4, characterized in that, Step S3 further includes: S31. Initialize the loop counter variable ; S32, Update the loop variable Check the loop variable Is it greater than ,like If so, proceed to step S4; otherwise, proceed to step S33. S33. Obtain the first... using the simplified neighborhood optimization module. Individual neighborhood optimization deployment scheme and penetration rate ; S34, Comparison requirements for penetration rate With deployment plan penetration rate The size relationship, if The plan and plan Save the scheme with a relatively low penetration rate as the scheme. and penetration rate ,Right now and If the condition is met, return to step S32; otherwise, proceed to step S35. S35. Use the verification module to determine the deployment plan. If the verification result indicates that placement is not suitable, save the current deployment plan. and penetration rate If the condition is met, return to step S32; otherwise, proceed to step S36. S36, Deployment Plan and penetration rate Assign values ​​to the optimized deployment scheme respectively and optimize penetration rate Then proceed to step S5.

6. The expert knowledge-assisted verification method for deploying bee colony ground equipment according to claim 5, characterized in that, Step S33 further includes: S331, according to the... Initial deployment plan Randomly generate a position swap index A ground location number to be exchanged ; S332, Determine the position exchange index Does the corresponding deployment location number belong to the set? ,like If the condition is met, proceed to step S331; otherwise, proceed to step S333. S333, the first Deployment plan The Replace the ground deployment location corresponding to each element with a ground location number. The corresponding ground deployment location, to generate the first Individual neighborhood optimization deployment scheme Then return to continue executing step S32.

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