Weighted address selection method based on maximum coverage algorithm and all-in-one machine thereof

Abstract

The invention provides a weighted address selection method based on a maximum coverage algorithm and an all-in-one machine thereof, and relates to the technical field of material distribution network processing, and the method comprises the following steps: S1, determining a demand point set and weights thereof, the weights reflecting the importance degree or demand quantity of demand points; secondly, determining an available library station set and material limitation conditions, and determining a material demand point set and a candidate library station position set; a material demand point position set: D [i] = {1, 2,..., N}, and each demand point i has a certain material demand quantity; the scheme is clear and visual, can combine with an emergency guarantee task, integrates factors, such as road conditions, geographical environments and safety, which influence the guarantee effect into a maximum coverage algorithm, and flexibly configures the priority and weight of each influence factor, so that the calculation of the method is more accurate, and the result better meets the actual requirement; and the accuracy and the usability of the method are improved. When the method is applied to more emergency fields, the method has the flexibility of being applied to multiple fields.

Description

Technical Field

[0001] The present invention relates to the technical field of material distribution network processing, and particularly to a weighted address selection method based on the maximum coverage algorithm and an all-in-one machine thereof. Background Art

[0002] The location selection of depots and stations is the core link for optimizing the material distribution network in guarantee tasks, which is directly related to the material distribution efficiency, cost control, and service guarantee level for demand points. During the location selection process, the superiority of the geographical location is first considered. The ideal location should be close to the main transportation hubs for the rapid collection and distribution of materials. At the same time, the depots and stations should be located in areas where demand points are concentrated to shorten the distribution distance, increase the distribution speed, and reduce the transportation cost. Secondly, traffic convenience is an important consideration factor for location selection. The depots and stations should be located in areas with a developed transportation network and easy access for transport vehicles, and the surrounding roads should have good traffic conditions to avoid traffic congestion and ensure the smooth flow of materials. The geographical environment and safety are also aspects that cannot be ignored. The location selection should avoid environmentally sensitive areas to reduce the impact on the surrounding environment and ensure that the depots and stations can guarantee materials in a safe environment. The location selection of depots and stations is a complex decision-making process involving multiple factors, and it is necessary to comprehensively consider geographical location, traffic convenience, time cost-benefit, material requirements of demand points, environmental impact, and safety factors, etc.

[0003] The maximum coverage algorithm is an optimization strategy aimed at maximizing the coverage range under the premise of limited resources. It is widely applied in fields such as logistics distribution, public services such as hospitals or schools. Through scientific location selection, it ensures that various materials can cover as many demand points as possible. The core of the algorithm is to screen out the best subset of locations from the candidate locations under the condition of meeting various material constraints (such as material quantity, material category, material guarantee time requirements, etc., guarantee capabilities) to achieve the maximum coverage of demand points. On the basis of the maximum coverage, uncertain factors are added for comprehensive analysis, such as the current state of the road (road grade, current on / off state of the road, congestion situation, etc.), geographical environment (altitude from the guarantee point to the demand point, road slope, etc.), and safety factors (whether there is interference, whether it is within the safety prevention and control range, whether the communication is normal, etc.).

[0004] The maximum coverage algorithm and various variant algorithms such as the greedy algorithm, heuristic algorithm, and integer linear programming can find approximate optimal solutions within a reasonable time when dealing with large-scale data sets. The greedy algorithm is a computational method that makes the optimal (i.e., most favorable) choice in each step during the problem-solving process, hoping to finally obtain the global optimal solution. Its core idea is to adopt the local optimal solution in each step and hope to achieve the global optimal solution through the accumulation of local optimal solutions. The heuristic algorithm is a class of algorithms based on experience or intuition, used to find approximate optimal solutions to problems when it is impossible to solve them precisely or the cost of precise solution is too high. The maximum coverage algorithm aims to select a set of locations under limited resources to maximize the number of demand points covered. Through an iterative selection and evaluation process, it finds a location combination that can cover as many demand points as possible. However, in practical applications, the algorithm cannot fully consider all constraints and conditions in actual applications, such as traffic flow, terrain changes, safety factors, etc., making it impossible to guarantee tasks within the shortest time during actual use.

[0005] Therefore, it is necessary to provide a weighted address selection method based on the maximum coverage algorithm and its all-in-one machine to solve the above technical problems. Summary of the Invention

[0006] The present invention provides a weighted address selection method based on the maximum coverage algorithm and its all-in-one machine, which solves the technical problem that existing algorithms in related technologies cannot fully consider all constraints and conditions in actual applications.

[0007] To solve the above technical problems, a weighted address selection method based on the maximum coverage algorithm provided by the present invention includes the following steps:

[0008] S1: Define the set of demand points and their weights, and these weights reflect the importance or demand volume of the demand points; secondly, determine the set of available depot stations and material limitation conditions;

[0009] Define the set of material demand points and the set of candidate depot station locations;

[0010] Set of material demand point locations: D[i] = {1, 2,..., N}, each demand point i has a certain material demand volume, and the location i is represented as i(x, y), and the value of i is 1, 2,..., N;

[0011] Set of candidate depot station locations: F[j] = {1, 2,..., M}, each candidate depot station location j can be a possible location for the depot station, and the location j is represented as j(u, v), and the value of j is 1, 2,..., M;

[0012] Define the maximum number of candidate depot station locations that can be selected and the coverage radius of the depot station locations;

[0013] Coverage radius of the depot location: Represented by R, each selected depot can provide guarantee services within its coverage radius;

[0014] Limit on the number of candidate depots: Represented by P, it is the maximum number of candidate depot locations that can be established through this algorithm. Usually, when performing guarantee tasks, multiple depots of different material types will be built to provide guarantee services for demand points of various materials;

[0015] S2: Define the distance or time cost measurement standard between the demand point and the depot;

[0016] Define the measurement standard with the shortest time as the criterion;

[0017] Defining the shortest time criterion means that when considering the depot covering the demand point, the depot that can reach the demand point in the shortest time is preferentially selected. This criterion is applicable to situations with strict requirements for response time, such as guarantee services, emergency services, etc. The following is a description of how to define the shortest time criterion in this algorithm:

[0018] In the algorithm of the present invention, the shortest time criterion can be defined through the following steps:

[0019] (1) Determine the time cost between the demand point and the candidate depot: Quantify the time cost between each demand point and each candidate depot, which is usually based on factors such as road conditions, geographical environment, safety, etc.;

[0020] (2) Select the depot with the shortest time: In each iteration of the subsequent algorithm, clearly select the depot location that can cover the most demand points in the shortest time;

[0021] (3) Consider the guarantee resource limitations: While meeting the shortest time criterion, it is also necessary to consider the quantity of resources required for the guarantee services owned by the depot or other resource limitations;

[0022] This algorithm adopts the shortest time criterion and focuses on quickly responding to the requirements of guarantee tasks. Under limited resources, the algorithm preferentially considers the depots that can quickly cover the demand points. By calculating the time cost from each depot to the demand point, it selects the depots that can cover the most uncovered demand points in the shortest time. This criterion is particularly applicable to time-sensitive scenarios;

[0023] S3: Algorithm initialization, the algorithm selects one or more depot addresses from the depot set as the initial solution;

[0024] Algorithm initialization is the first step of algorithm execution, which lays the foundation for the subsequent iterative process. The initialization step usually includes the following content:

[0025] (1) Set of material demand points

[0026] Determine the locations of all requirement points to be covered and assign weights to them to represent their importance and demand volume, and mark all requirement points as uncovered;

[0027] (2) Candidate depot location set

[0028] All depot locations available for covering requirement points, including their quantity and covering capabilities. Randomly select one or more depots from the candidate depot location set as the initial solution, usually the location that covers the most uncovered requirement points;

[0029] (3) Time cost metric

[0030] Define the time metric standard between each guarantee requirement point and the candidate depot;

[0031] (4) Termination condition

[0032] Define the termination condition of the algorithm, such as reaching the maximum value of candidate depot locations or being unable to increase the number of covered requirement points by replacing depots;

[0033] The purpose of the initialization step is to provide a starting point for the algorithm to initiate the iterative process;

[0034] S4: Enter the iterative optimization stage. In each iteration of the algorithm, according to certain rules, select a new depot address to join the current solution set or replace the existing depot to increase the number of covered requirement points. This process needs to fully consider the limitations such as guarantee materials, road conditions from the depot to the requirement point, geographical environment, safety factors, etc., to ensure that the established conditions are not exceeded. When the resource limit is reached or it is impossible to increase the covered requirement points by replacing depots, the algorithm stops iterating;

[0035] Finally, the algorithm outputs a depot combination that covers the largest number of requirement points as the optimal solution. If there are multiple results, they need to be evaluated according to the objective function (such as total time cost, maximum coverage, etc.) to determine the best solution. The weighted maximum coverage algorithm is based on the maximum coverage algorithm and adds restrictive conditions such as road conditions, geographical environment, safety factors, etc., so as to better adapt to the actual guarantee task.

[0036] Preferably, define the weighted factors to be considered to form a weighted factor set;

[0037] Set of weighted factors: W[k] = W[1, 2, ..., N] = W[{dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}, {dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}, …… {dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}]. According to different guarantee tasks, the priority order of different factors can be defined, and different factor weights can be set. When calculating each candidate location, factors such as road conditions, geographical environment, and safety should be considered;

[0038] W[k]: Location k represents the candidate depot location, and the value of k is 1, 2, ..., N. When calculating each candidate depot location, the factors {dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr} that need to be considered;

[0039] dl-sta: Factor of whether the road is unobstructed or congested;

[0040] dl-sec: Factor of the current safety state of the road;

[0041] hj-sec: Factor that the geographical environment is safe without threats;

[0042] hj-nat: Factor that the geographical environment is not easily affected by natural disasters;

[0043] hj-wea: Factors such as environmental climate (whether there is heavy rain), etc.;

[0044] dq-dan: Factor that there are no dangerous places nearby (such as explosive chemical plants);

[0045] dq-thr: Factors such as whether it is easily threatened, etc.

[0046] Preferably, in the algorithm iteration, the depot address selected according to the shortest time rule is added to the current solution set or replaces the existing depot location;

[0047] Based on the maximum coverage algorithm, this algorithm adds multiple factors affecting material support and selects the depot location according to the shortest time standard. The mathematical model can be expressed as:

[0048]

[0049]

[0050] Among them:

[0051] x ij: If the material demand point i is covered by the candidate depot station j, then x ij = 1, otherwise x ij = 0.

[0052] y j : If the candidate depot station j is selected, then y j = 1, otherwise y j = 0.

[0053] During the algorithm process, the set of candidate depot station positions is continuously iterated and optimized until a solution that can achieve maximum coverage and ensure the shortest time under the rule constraints is found, thereby improving the overall efficiency of the depot station guarantee service.

[0054] A one - machine for weighted address selection method based on the maximum coverage algorithm, comprising: an operating machine, a detachable panel, and a placement component;

[0055] An inclined display screen is embedded inside the operating machine, and an operating screen is installed below the display screen inside the operating machine;

[0056] The placement component includes a mounting base. A sliding sleeve is fixedly provided on the side wall of the operating machine. An elastic sliding rod is slidably connected inside the sliding sleeve. A mounting plate is snap - fitted on the outer wall of the mounting base. A fixing pin is fixedly provided at the middle position of the inner wall of the mounting base. A lifting plate is slidably connected to the outside of the fixing pin. A limiting groove is opened at the middle position of the lifting plate. Positioning grooves are opened on both sides of the limiting groove inside the lifting plate. Sliding grooves are opened on both sides of the two positioning grooves inside the mounting base. Side plates are slidably connected inside the two sliding grooves. A spring is installed below the fixing pin at the middle position of the lifting plate. Pulleys are rotatably connected to the outer walls of the two side plates;

[0057] A ball seat is fixedly provided on the back of the mounting base. A knob is threadedly connected to the outer wall of the ball seat. A ball head is embedded inside the ball seat.

[0058] Preferably, the outer diameter of the fixing pin and the inner width of the limiting groove are mutually adapted, and the two pulleys and the two positioning grooves are in sliding connection.

[0059] Preferably, the bottom end of the spring is fixedly connected to the mounting base, the top end of the spring is fixedly connected to the inner wall of the lifting plate, and the ball head is fixedly connected to the elastic sliding rod.

[0060] Preferably, it further includes a rotating component, which includes a first pedestal and a second pedestal. A bearing plate is installed inside the manipulator, and a motor is installed on the outer wall of the bearing plate through bolts. On both sides of the axis of the first pedestal, pin shafts are fixedly provided. A middle plate is installed inside the second pedestal, and a fixed grid plate is fixedly provided on the inner wall of the second pedestal. Elastic groove plates are fixedly provided on the outer wall of the second pedestal and on both sides of the fixed grid plate. A movable grid plate is slidably connected inside the two elastic groove plates;

[0061] A movable rod is fixedly provided on the side wall of the elastic sliding rod, and a notch adapted to the movable rod is formed on the outer wall of the sliding sleeve.

[0062] Preferably, the output shaft of the motor is keyed to the axis of the pin shaft, and the outer walls of the two pin shafts are rotatably connected to the bearing plate through bearings. Both sides of the operation screen are rotatably connected to the first pedestal through bearings.

[0063] Preferably, the first pedestal and the second pedestal are installed by bolts, and the outer wall of the fixed grid plate is in close contact with the inner wall of the movable grid plate.

[0064] Compared with the related art, the weighted address selection method and the integrated machine based on the maximum coverage algorithm provided by the present invention have the following beneficial effects:

[0065] Compared with the prior art, the technical solution proposed by the present invention is clear and intuitive. It can combine emergency support tasks, integrate factors such as road conditions, geographical environment, and safety that affect the support effect into the maximum coverage algorithm, and can flexibly configure the priorities and weights of various influencing factors according to the actual situation, making the calculation of this method more accurate, making the results more in line with actual needs, and improving the accuracy and usability of the method of the present invention. When this method is applied to more emergency fields, it also has the flexibility for multiple fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the structures shown in these drawings without creative efforts.

[0067] Figure 1 It is a schematic flow chart of the weighted address selection method based on the maximum coverage range provided by the present invention;

[0068] Figure 2 It is a schematic flow chart of determining the set of material demand points and the set of candidate depot locations provided by the present invention;

[0069] Figure 3 Schematic diagram of the initialization process of the weighted address selection method based on the maximum coverage range provided by the present invention;

[0070] Figure 4 Optimal structural schematic diagram provided by the present invention;

[0071] Figure 5 For Figure 4 Schematic diagram of the placement component and the detachable panel structure shown;

[0072] Figure 6 For Figure 5 Schematic diagram of the split structure of the placement component shown;

[0073] Figure 7 For Figure 6 Schematic diagram of the back structure of the mounting seat shown;

[0074] Figure 8 For Figure 6 Schematic diagram of the connection structure between the lifting plate and the side plate shown;

[0075] Figure 9 For Figure 4 Schematic diagram of the rotating component structure shown;

[0076] Figure 10 For Figure 9 Schematic diagram of the bottom view shown;

[0077] Figure 11 Schematic diagram of the non-trigger working state of the movable grille plate provided by the present invention;

[0078] Figure 12 For Figure 11 Schematic diagram of the trigger working state of the movable grille plate shown;

[0079] Figure 13 Schematic diagram of the split structure of the movable grille plate and the fixed grille plate provided by the present invention.

[0080] Explanation of the reference numerals in the drawings:

[0081] 1. Manipulator;

[0082] 2. Display screen, 3. Detachable panel, 4. Operating screen;

[0083] 5. Placement component, 51. Sliding sleeve, 52. Elastic sliding rod, 53. Notch, 54. Mounting seat, 55. Mounting plate, 56. Moving rod, 57. Lifting plate, 58. Chute, 59. Positioning groove, 510. Fixed pin, 511. Limiting groove, 512. Spring, 513. Side plate;

[0084] 514. Ball seat, 515. Knob, 516. Ball head, 517. Pulley;

[0085] 6. Rotating assembly, 61. Bearing plate, 62. Motor, 63. First pedestal, 64. Second pedestal, 65. Middle plate, 66. Pin;

[0086] 67. Elastic groove plate, 68. Movable grille plate, 69. Fixed grille plate.

[0087] The realization, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0088] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0089] The present invention provides a weighted address selection method based on the maximum coverage algorithm and an all-in-one machine thereof.

[0090] First embodiment:

[0091] Please refer to Figures 1 to 3 , a weighted address selection method based on the maximum coverage algorithm, characterized by including the following steps:

[0092] S1: Define the set of demand points and their weights, where these weights reflect the importance or demand volume of the demand points; secondly, determine the set of available depot stations and material restriction conditions;

[0093] Define the set of material demand points and the set of candidate depot station locations;

[0094] Set of material demand point locations: D[i] = {1, 2,..., N}, each demand point i has a certain material demand volume, and the location i is represented as i(x, y), and the value of i is 1, 2,..., N;

[0095] Set of candidate depot station locations: F[j] = {1, 2,..., M}, each candidate depot station location j can be a possible location of the depot station, and the location j is represented as j(u, v), and the value of j is 1, 2,..., M;

[0096] Define the maximum number of candidate depot station locations that can be selected and the coverage radius of the depot station locations;

[0097] Coverage radius of depot station locations: represented by R, each selected depot station can provide guarantee services within its coverage radius;

[0098] Candidate depot quantity limit: Represented by P, it is the maximum number of candidate depot locations that can be established through this algorithm. Usually, when performing guarantee tasks, multiple depots of different material types will be built to provide guarantee services for various materials at demand points;

[0099] S2: Define the distance or time cost measurement standard between demand points and depots;

[0100] Define the measurement standard with the shortest time as the criterion;

[0101] Defining the shortest time criterion means that when considering the coverage of demand points by depots, the depot that can reach the demand point in the shortest time is preferentially selected. This criterion is applicable to situations with strict requirements for response time, such as guarantee services, emergency services, etc. The following is a description of how to define the shortest time criterion in this algorithm:

[0102] In the algorithm of the present invention, the shortest time criterion can be defined through the following steps:

[0103] (1) Determine the time cost between demand points and candidate depots: Quantify the time cost between each demand point and each candidate depot, which is usually based on factors such as road conditions, geographical environment, safety, etc.;

[0104] (2) Select the depot with the shortest time: In each iteration of the subsequent algorithm, clearly select the depot location that can cover the most demand points in the shortest time;

[0105] (3) Consider guarantee resource limitations: While meeting the shortest time criterion, it is also necessary to consider the quantity of resources required for the guarantee services owned by the depot or other resource limitations;

[0106] This algorithm adopts the shortest time criterion and focuses on quickly responding to the requirements of guarantee tasks. Under limited resources, the algorithm preferentially considers the depots that can quickly cover demand points. By calculating the time cost from each depot to the demand point, it selects the depots that can cover the most uncovered demand points in the shortest time. This criterion is particularly applicable to time-sensitive scenarios;

[0107] S3: Algorithm initialization, the algorithm selects one or more depot addresses from the depot set as the initial solution;

[0108] Algorithm initialization is the first step in the execution of the algorithm, which lays the foundation for the subsequent iterative process. The initialization step usually includes the following content:

[0109] (1) Set of material demand points

[0110] Determine the locations of all demand points that need to be covered, assign weights to them to represent their importance and demand quantity, and mark all demand points as uncovered;

[0111] (2) Candidate depot location set

[0112] All depot locations available for covering demand points, including their quantities and covering capabilities. Randomly select one or more depots from the candidate depot location set as the initial solution, usually the location that covers the most uncovered demand points;

[0113] (3) Time cost metric

[0114] Define the degree-time metric standard between each guarantee demand point and the candidate depot;

[0115] (4) Termination condition

[0116] Define the termination condition of the algorithm, such as reaching the maximum value of candidate depot locations or being unable to increase the number of covered demand points by replacing depots;

[0117] The purpose of the initialization step is to provide a starting point for the algorithm to initiate the iterative process;

[0118] S4: Enter the iterative optimization phase. In each iteration of the algorithm, according to certain rules, select a new depot address to add to the current solution set or replace the existing depot to increase the number of covered demand points. This process needs to fully consider the limitations such as guarantee materials, road conditions from the depot to the demand point, geographical environment, safety factors, etc., to ensure that the established conditions are not exceeded. When the resource limit is reached or it is impossible to increase the covered demand points by replacing depots, the algorithm stops iterating;

[0119] Finally, the algorithm outputs a depot combination that covers the largest number of demand points as the optimal solution. If there are multiple results, they need to be evaluated according to the objective function (such as total time cost, maximum coverage, etc.) to determine the best solution. The weighted maximum coverage algorithm adds restrictive conditions such as road conditions, geographical environment, safety factors, etc. on the basis of the maximum coverage algorithm, so as to better adapt to the actual guarantee task.

[0120] Define the weighted factors to be considered to form a weighted factor set;

[0121] Set of weighted factors: W[k] = W[1, 2, ..., N] = W[{dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}, {dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}, ……{dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}]. According to different guarantee tasks, the priority order of different factors can be defined, and different factor weights can be set. When calculating each candidate location, factors such as road conditions, geographical environment, and safety should be considered;

[0122] W[k]: Location k represents the candidate depot location, and the value of k is 1, 2, ..., N. When calculating each candidate depot location, the factors {dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr} that need to be considered;

[0123] dl-sta: Factor of whether the road is unobstructed or congested;

[0124] dl-sec: Factor of the current safety state of the road;

[0125] hj-sec: Factor that the geographical environment is safe and threat-free;

[0126] hj-nat: Factor that the geographical environment is not easily affected by natural disasters;

[0127] hj-wea: Factors such as environmental climate (whether there is heavy rain), etc.;

[0128] dq-dan: Factor that there is no dangerous place nearby (such as an explosive chemical plant);

[0129] dq-thr: Factors such as whether it is easily threatened, etc.

[0130] The algorithm iterates, and the depot address selected according to the shortest time rule is added to the current solution set or replaces the existing depot location;

[0131] Based on the maximum coverage algorithm, this algorithm adds multiple factors affecting material support and selects the depot location according to the shortest time standard. The mathematical model can be expressed as:

[0132]

[0133] Among them:

[0134] x ij : If the material demand point i is covered by the candidate depot j, then x ij = 1, otherwise xij = 0.

[0135] y j : If candidate depot site j is selected, then y j = 1, otherwise y j = 0.

[0136] During the algorithm process, the set of candidate depot site locations is continuously iterated and optimized until a solution that can achieve maximum coverage and ensure the shortest time under the rule constraints is found, thereby improving the overall efficiency of depot site support services.

[0137] In this embodiment: For the usage requirements of depot site address selection in support tasks, its core goal is to maximize the number of demand points covered under limited resource constraints. The principle of the maximum coverage method is to cover as many demand points or demand quantities as possible with a given number of service points, so that the number of demand points served is the largest or the demand quantity is the largest, and on the basis of maximum coverage, the sum of the distances from the demand points to the task support center is minimized. However, the support task environment is complex, and the support task needs to be completed in the shortest time. In addition to considering the shortest distance from the support point to the demand point, information such as roads, weather, and environment that need to be considered during task support is added. By constructing a mathematical model, factors such as the service capacity of candidate depot site addresses, the distribution of demand points, service scope, road conditions, weather conditions, and environmental conditions are comprehensively considered to determine the optimal depot site address location. During the implementation process of the entire task support, the weighted maximum coverage method needs to cope with a series of technical challenges, such as the computational complexity brought by considering weather, roads, and environmental factors, the priority setting when considering multiple factors, and the change of demand point priorities. To solve these problems, algorithm designers usually adopt techniques such as expert prediction method, heuristic search, and regression analysis to ensure that the selected depot site location can find an approximately optimal site selection scheme in terms of service scope, service capacity, etc. In addition, this algorithm also needs to consider the uncertainty of demand points and the adaptability to dynamic environments, and improve the accuracy and practicality of the algorithm through methods such as dynamic programming and actual testing;

[0138] Based on the maximum coverage algorithm, various influencing factors are fully considered, and the algorithm is improved to better apply to actual support tasks. On the basis that the coverage range of the depot site location for demand points is wider, factors such as road conditions, geographical environment, and safety are introduced. Road conditions include factors such as unobstructed roads without congestion and roads within a safe range. Geographical environment includes factors such as the safety and threat-free of the depot site location, the location not being vulnerable to natural disasters (such as landslides, mudslides, etc.), and climate factors (such as whether there is heavy rain). Safety factors include factors such as no dangerous places (such as explosive chemical plants) and not being vulnerable to threats. The algorithm is optimized through the priority weighting of various factors, and finally the material support for demand points is completed within the shortest time.

[0139] Second Embodiment:

[0140] Please refer to Figures 3 to 8 , an all-in-one machine for a weighted address selection method based on the maximum coverage algorithm, comprising: an operating machine 1, a detachable panel 3, and a placement component 5;

[0141] An inclined display screen 2 is embedded inside the operating machine 1, and an operating screen 4 is installed below the display screen 2 inside the operating machine 1;

[0142] The placement component 5 includes a mounting base 54. A sliding sleeve 51 is fixedly provided on the side wall of the operating machine 1. An elastic sliding rod 52 is slidably connected inside the sliding sleeve 51. A mounting plate 55 is snap-fitted and installed on the outer wall of the mounting base 54. A fixing pin 510 is fixedly provided at the middle position of the inner wall of the mounting base 54. A lifting plate 57 is slidably connected to the outside of the fixing pin 510. A limiting groove 511 is formed at the middle position of the lifting plate 57. Positioning grooves 59 are formed on both sides of the limiting groove 511 inside the lifting plate 57. Chute grooves 58 are formed on both sides of the two positioning grooves 59 inside the mounting base 54. Side plates 513 are slidably connected inside the two chute grooves 58. A spring 512 is installed at the middle position of the lifting plate 57 and below the fixing pin 510. Pulleys 517 are rotatably connected to the outer walls of the two side plates 513;

[0143] A ball seat 514 is fixedly provided on the back of the mounting base 54. A knob 515 is threadedly connected to the outer wall of the ball seat 514. A ball head 516 is embedded inside the ball seat 514.

[0144] Please refer to Figure 4 : When the user is performing the first embodiment, if the user needs to clarify the set of requirement points and their weights, the user needs to separately call the required requirement points and weights on the operating screen 4;

[0145] If the user needs to independently add weights, the user needs to operate on the detachable panel 3.

[0146] Please refer to Figure 5 and Figure 6 : When placing the detachable panel 3, the user needs to directly place the detachable panel 3 on the bottom of the lifting plate 57. During the placement process, due to the weight of the detachable panel 3 itself, the lifting plate 57 will descend. During the descending process, the two positioning grooves 59 will be driven to descend simultaneously. Through the restriction of the two positioning grooves 59 on the pulleys 517, the side plates 513 on both sides can be made to move relatively along the trajectories of the chute grooves 58 and the positioning grooves 59. During the relative movement process, the two side walls of the detachable panel 3 can be restricted and stabilized.

[0147] The outer diameter of the fixed pin 510 and the inner width of the limit groove 511 are adapted to each other, and the two pulleys 517 and the two positioning grooves 59 are slidably connected.

[0148] The bottom end of the spring 512 is fixedly connected to the mounting seat 54, the top end of the spring 512 is fixedly connected to the inner wall of the lifting plate 57, and the ball head 516 and the elastic sliding rod 52 are fixedly connected.

[0149] It can be understood that: since the lifting plate 57 and the mounting seat 54 are limited by the fixed pin 510, the lifting plate 57 can only move in the vertical direction;

[0150] Secondly, by setting the spring 512, the spring 512 can be compressed during the downward movement of the lifting plate 57, and the lifting plate 57 can be automatically reset when the detachable panel 3 is removed.

[0151] Secondly, please refer to Figure 7 : During the actual use process, the user can rotate the knob 515 to achieve a stable state between the ball seat 514 and the ball head 516, so as to achieve universal angle adjustment control of the mounting seat 54, ensuring that the user can view or operate the detachable panel 3 from the best angle.

[0152] In this embodiment: The all-in-one machine in this case has a design with only three screens. The display screen 2 is mainly responsible for displaying the real-time process of the method. The detachable panel 3 is mainly used for the operation task of adding weights. The middle operation screen 4 is used to clarify the requirement point set and its weights. The different designs of the three screens ensure that the user can more quickly achieve the operation settings they need. Secondly, the detachable panel 3 is designed for independent disassembly and installation, ensuring that the detachable panel 3 can be taken and used by multiple people, improving the overall compatibility of the device. Secondly, the self-weight type of downward placement and stable method can not only place detachable panels 3 of different models, but also facilitate the user to take and use.

[0153] The third embodiment:

[0154] Please refer to Figures 9 to 13 , and further includes a rotating assembly 6. The rotating assembly 6 includes a first pedestal 63 and a second pedestal 64. A bearing plate 61 is installed inside the operating machine 1. A motor 62 is installed on the outer wall of the bearing plate 61 through bolts. On both sides of the axis center of the first pedestal 63, a retaining pin 66 is fixedly provided. A middle plate 65 is installed inside the second pedestal 64. A fixed grid plate 69 is fixedly provided on the inner wall of the second pedestal 64. Elastic groove plates 67 are fixedly provided on the outer wall of the second pedestal 64 and on both sides of the fixed grid plate 69. A movable grid plate 68 is slidably connected inside the two elastic groove plates 67;

[0155] A moving rod 56 is fixedly arranged on the side wall of the elastic sliding rod 52, and a notch 53 adapted to the moving rod 56 is formed in the outer wall of the sliding sleeve 51.

[0156] Please refer to Figure 9 and Figure 10 : After the first pedestal 63 and the second pedestal 64 are installed, a complete circular shape is formed, and the operation screen 4 is installed inside the first pedestal 63. When the user uses it normally, the operation can be carried out at a fixed angle. At the same time, the operation screen 4 can be freely rotated on the first pedestal 63 to adjust the angle, and it can be displayed from multiple angles;

[0157] At the same time, in the actual working condition, the user can install a peripheral heat dissipation component inside the second pedestal 64 to dissipate heat from the entire operating machine 1.

[0158] During actual operation, the direction of the operation screen 4 is directly upward. In the state of shutdown at night, the user can start the motor 62 to drive the pin 66 to rotate. The pin 66 is engaged to drive the installed first pedestal 63 and the second pedestal 64 to rotate. After rotation, the operation screen 4 is rotated to directly below, and the heat dissipation component inside the second pedestal 64 is rotated to the uppermost position, so that the switching between the working state and the heat dissipation state can be realized.

[0159] The output shaft of the motor 62 is keyed to the axis of the pin 66. The outer walls of the two pins 66 are rotatably connected to the bearing plate 61 through bearings, and both sides of the operation screen 4 are rotatably connected to the first pedestal 63 through bearings.

[0160] The first pedestal 63 and the second pedestal 64 are installed by bolts, and the outer wall of the fixed grille plate 69 and the inner wall of the movable grille plate 68 are mutually attached.

[0161] Please refer to Figures 11 to 13 : When the rotating assembly 6 switches from the normal working state to the heat dissipation state, since the heat dissipation state mostly occurs during the operation period of the operating machine 1 at night, the user needs to slide the placing assembly 5 to the initial state. When pushing the elastic sliding rod 52 during the sliding process, the moving rod 56 can be driven to slide inside the notch 53. When the moving rod 56 slides, it can push the movable grille plate 68 to move inside the elastic groove plate 67. By moving the movable grille plate 68 on the fixed grille plate 69, all the inner holes can be opened to maximize the heat dissipation effect.

[0162] This embodiment: A rotating component 6 is designed on the basis of the traditional operating machine 1, so that it has two functions of rotation switching. Under normal use, the angle and function of the operating screen 4 can be changed arbitrarily, and the heat dissipation function can be switched to the top at night, ensuring that the operating machine 1 can not only provide basic heat dissipation, but also provide auxiliary heat dissipation to avoid overheating inside the operating machine 1. At the same time, during the switching process, it cooperates with the moving rod 56 in the second embodiment to open all the heat dissipation holes between the movable grid plate 68 and the fixed grid plate 69, ensuring that the heat dissipation is faster and more stable. Secondly, it also has a simple dust removal function.

[0163] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A weighted address selection method based on a maximum coverage algorithm, characterized in that: The following steps are involved: S1: Clearly define the set of demand points and their weights, which reflect the importance or demand of the demand points; Secondly, determine the available warehouse and station collection and material restrictions; Clarify the material demand point set and candidate warehouse location set; Material demand point location set: D[i] = {1, 2, ..., N}, each demand point i has a certain material demand, the position i is represented by i(x, y), and the value of i is 1, 2, ..., N; Candidate depot location set: F[j] = {1, 2, ..., M}, each candidate depot location j can be used as a possible location of a depot, location j is represented by j(u, v), and j value is 1, 2, ..., M; Specify the maximum number of candidate depot locations that can be selected and the coverage radius of the depot locations; Depot location coverage radius: denoted by R. Each selected depot can provide guaranteed services within its coverage radius. Candidate depot quantity limit: P represents the maximum number of candidate depot locations that can be established through this algorithm. Usually, when performing support tasks, multiple depots of different material types will be built to provide support services for various types of materials at demand points. S2: Define the distance or time cost metric between the demand point and the depot; Define the metric, taking the shortest time as the standard; Defining the shortest time standard means that when considering the warehouse station covering the demand point, the warehouse station that can reach the demand point in the shortest time is preferred. This standard is applicable to situations where there are strict requirements on response time, such as security services, emergency services, etc. The following is a description of how to define the shortest time standard in this algorithm: In the algorithm of the present invention, the shortest time standard can be defined by the following steps: (1) Determine the time cost between the demand point and the candidate depot: Quantify the time cost between each demand point and each candidate depot, which is usually based on factors such as road conditions, geographical environment, and safety; (2) Select the depot with the shortest time: In each subsequent iteration of the algorithm, explicitly select the depot locations that can cover the most demand points in the shortest time; (3) Considering the resource constraints of the guarantee: While meeting the shortest time standard, it is also necessary to consider the number of resources or other resource constraints required for the guarantee service owned by the warehouse station; This algorithm adopts the shortest time standard and focuses on rapid response to guarantee mission requirements. Under limited resources, the algorithm gives priority to the depots that can quickly cover the demand points. By calculating the time cost from each depot to the demand point, it selects those depots that can cover the most uncovered demand points in the shortest time. This standard is particularly suitable for time-sensitive scenarios; S3: Algorithm initialization, the algorithm selects one or more storage station addresses from the storage station set as the initial solution; Algorithm initialization is the first step in algorithm execution, which lays the foundation for subsequent iterative processes. The initialization step usually includes the following: (1) Material demand point collection Determine the locations of all demand points that need to be covered, assign weights to them to indicate their importance and demand, and mark all demand points as uncovered; (2) Candidate depot location set All the depot locations that can be used to cover the demand points, including their number and coverage capacity. Randomly select one or more depots from the candidate depot location set as the initial solution, usually the location that covers the most uncovered demand points; (3) Time cost measurement Define the time measurement standard between each support demand point and the candidate depot; (4) Termination conditions Define the termination conditions of the algorithm, such as reaching the maximum number of candidate depot locations or failing to increase the number of covered demand points by replacing depots; The purpose of the initialization step is to provide a starting point for the algorithm to start the iterative process; S4: Entering the iterative optimization phase, the algorithm selects new depot addresses to add to the current solution set or replaces existing depots in each iteration according to certain rules to increase the number of covered demand points. This process must fully consider restrictions such as the supply of materials, road conditions from depots to demand points, geographical environment, and safety factors to ensure that the established conditions are not exceeded. When the resource limit is reached or it is impossible to increase the coverage of demand points by replacing depots, the algorithm stops iterating; Finally, the algorithm outputs a combination of depots and stations that covers the largest number of demand points as the optimal solution. If there are multiple results, they need to be evaluated according to the objective function (such as total time cost, maximum coverage, etc.) to determine the best solution. The weighted maximum coverage algorithm is based on the maximum coverage algorithm, adding restrictions such as road conditions, geographical environment, and safety factors to better adapt to actual security tasks.

2. The weighted address selection method based on the maximum coverage algorithm according to claim 1, characterized in that: Define the weighted factors that need to be considered to form a set of weighted factors; Weighted factor set: W[k] = W[1, 2, ..., N] = W[{dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}, {dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}, ... {dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}], according to different guarantee tasks, the priority order of different factors can be defined, and different factor weights can be set. When calculating each candidate location, factors such as road conditions, geographical environment, and safety should be considered; W[k]: Position k represents the candidate depot location, and the value of k is 1, 2, ..., N. When calculating each candidate depot location, the factors to be considered are {dl-sta, dl-sec, hj-sec, hj-nat, hj-wea, dq-dan, dq-thr}; dl-sta: factors of road smoothness or congestion; dl-sec: current road safety status factor; hj-sec: The geographical environment is safe and free of threatening factors; hj-nat: The geographical environment is not susceptible to natural disasters; hj-wea: environmental climate (whether there is heavy rain) and other factors; dq-dan: There are no dangerous places (such as explosive chemical plants) nearby; dq-thr: whether one is vulnerable to threats and other factors.

3. The weighted address selection method based on the maximum coverage algorithm according to claim 1, characterized in that: The algorithm iterates and the depot address selected according to the shortest time rule is added to the current solution set, or the existing depot location is replaced; This algorithm adds multiple factors that affect material support based on the maximum coverage algorithm, and selects the location of the warehouse according to the shortest time standard. The mathematical model can be expressed as: in: x ij :If material demand point i is covered by candidate warehouse station j, then x ij =1, otherwise x ij =0. y j : If candidate station j is selected, then y j =1, otherwise y j =0. During the algorithm process, the set of candidate depot locations is continuously iterated and optimized until a solution is found that can achieve maximum coverage and shortest time under the constraints of the rules, thereby improving the overall depot guarantee service efficiency.

4. A weighted address selection method based on a maximum coverage algorithm, characterized in that: The integrated machine is used for a weighted address selection method based on a maximum coverage algorithm as claimed in any one of claims 1, comprising: an operating machine, a detachable panel and a placement component; An oblique display screen is embedded inside the operating machine, and an operating screen is installed inside the operating machine and below the display screen; The placing component includes a mounting seat, a sliding sleeve is fixedly provided on the side wall of the operating machine, an elastic sliding rod is slidably connected inside the sliding sleeve, a mounting plate is clamped and installed on the outer wall of the mounting seat, a fixing pin is fixedly provided at the middle position of the inner wall of the mounting seat, a lifting plate is slidably connected outside the fixing pin, a limiting groove is provided in the middle position of the lifting plate, a positioning groove is provided inside the lifting plate and on both sides of the limiting groove, a sliding groove is provided inside the mounting seat and on both sides of the two positioning grooves, side plates are slidably connected inside the two sliding grooves, a spring is installed at the middle position of the lifting plate and below the fixing pin, and pulleys are rotatably connected to the outer walls of the two side plates; A ball seat is fixedly arranged on the back of the mounting seat, a knob is threadedly connected to the outer wall of the ball seat, and a ball head is embedded in the interior of the ball seat.

5. The all-in-one machine for weighted address selection method based on maximum coverage algorithm according to claim 4, characterized in that: The outer diameter of the fixing pin and the inner width of the limiting groove are matched with each other, and the two pulleys and the two positioning grooves are slidably connected.

6. The all-in-one machine for weighted address selection method based on maximum coverage algorithm according to claim 4, characterized in that: The bottom end of the spring is fixedly connected to the mounting seat, the top end of the spring is fixedly connected to the inner wall of the lifting plate, and the ball head and the elastic sliding rod are fixedly connected.

7. The all-in-one machine for weighted address selection method based on maximum coverage algorithm according to claim 4, characterized in that: It also includes a rotating assembly, which includes a first pedestal and a second pedestal, a bearing plate is installed inside the operating machine, a motor is installed on the outer wall of the bearing plate by bolts, a bayonet is fixed on both sides of the axis of the first pedestal, a middle plate is installed inside the second pedestal, a fixed grid plate is fixed on the inner wall of the second pedestal, an elastic slot plate is fixed on the outer wall of the second pedestal and located on both sides of the fixed grid plate, and a movable grid plate is slidably connected inside the two elastic slot plates; A moving rod is fixedly arranged on the side wall of the elastic sliding rod, and a notch matched with the moving rod is opened on the outer wall of the sliding sleeve.

8. The all-in-one machine for weighted address selection method based on maximum coverage algorithm according to claim 7, characterized in that: The output shaft of the motor is key-connected to the axis of the bayonet, the outer walls of the two bayonet are rotatably connected to the bearing plate through bearings, and the two sides of the operating screen are rotatably connected to the first pedestal through bearings.

9. The all-in-one machine for weighted address selection method based on maximum coverage algorithm according to claim 7, characterized in that: The first pedestal and the second pedestal are installed by bolts, and the outer wall of the fixed grid plate and the inner wall of the movable grid plate are fitted to each other.

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