Method, device and equipment for determining deployment location of construction machinery service network points

Abstract

The present invention provides a method, device and equipment for determining the deployment location of service outlets of construction machinery. The method includes obtaining the constraint conditions of the service outlets; determining the number of service outlets of the construction machinery according to the constraint conditions; and based on the service distance criterion of the construction machinery, performing clustering processing with the number of service outlets as the number of cluster centers to determine the deployment location of the service outlets of the construction machinery. Since the number of service outlets is accurately determined, and then based on the number of service outlets and the service distance criterion, the deployment location of the service outlets of the construction machinery is planned more scientifically and reasonably, avoiding the problem of unreasonable selection by manual experience. The optimization of the site selection decision can be ensured through the clustering algorithm, and different service requirements can be better met.

Description

Technical Field

[0001] The present invention relates to the technical field of planning and site selection, and particularly to a method, device and equipment for determining the deployment location of service outlets for construction machinery. Background Art

[0002] With the improvement of demand, the number of construction machinery such as pile drivers is increasing continuously, the distribution range is becoming wider and wider and is constantly expanding. Therefore, the demand for deploying new service outlets has increased rapidly. At present, the decision-making on the deployment of service outlets for construction machinery mostly relies on experience, and it is judged according to human experience where the service outlets should be deployed.

[0003] However, the method of deploying outlets relying on experience cannot guarantee the optimization of decision-making, and it is difficult to ensure the best demand optimization. Summary of the Invention

[0004] The present invention provides a method, device and equipment for determining the deployment location of service outlets for construction machinery, so as to solve the defect of determining the deployment location of service outlets for construction machinery by manual experience in the prior art, and to accurately determine the number of service outlets, and then reasonably determine the deployment location of service outlets for construction machinery by means of a clustering algorithm according to the number of service outlets.

[0005] The present invention provides a method for determining the deployment location of service outlets for construction machinery, including:

[0006] Obtaining the constraint conditions of the service outlets;

[0007] Determining the number of the service outlets of the construction machinery according to the constraint conditions;

[0008] Based on the service distance criterion of construction machinery, performing clustering processing with the number of the service outlets as the number of clustering centers to determine the deployment location of the service outlets of the construction machinery.

[0009] According to the method for determining the deployment location of service outlets for construction machinery provided by the present invention, the constraint conditions include at least one of the following: the product of the weights of different construction machinery and the service distance is the smallest, the farthest service distance is less than or equal to a first distance, the average service distance is less than or equal to a second distance, and the minimum number of equipment served is greater than or equal to a preset number.

[0010] According to the method for determining the deployment location of service outlets for construction machinery provided by the present invention, when the constraint condition is the product of the weights of different construction machinery and the service distance, the obtaining of the constraint conditions of the service outlets includes:

[0011] Determining a construction machinery set, a service outlet set, the weight of each piece of equipment, and the distance from each piece of equipment to the service outlet;

[0012] Based on preset rules, determine the product of the weights of different construction machines and the service distances according to the set of construction machines, the set of service outlets, the weight of each piece of equipment, and the distance from each piece of equipment to the service outlet.

[0013] According to a method for determining the deployment location of service outlets for construction machines provided by the present invention, based on the construction machine service distance criterion, using the number of service outlets as the number of clustering centers for clustering processing to determine the deployment location of the service outlets for construction machines, including:

[0014] Determine clustering starting points with the same number as the number of service outlets;

[0015] Determine the weights of construction machines for each service node, and determine the weighted distance between each service node and the clustering starting point according to the weights of the construction machines;

[0016] Based on the construction machine service distance criterion and the weighted distance, iterate the clustering starting points until the weighted distance meets the construction machine service distance criterion, and determine the position of the target clustering center as the deployment location of the service outlets for construction machines.

[0017] According to a method for determining the deployment location of service outlets for construction machines provided by the present invention, the determination of the weights of construction machines for each service node includes:

[0018] Determine the working condition data of each construction machine;

[0019] According to the working condition data of each construction machine, determine the weight of each construction machine for each service node.

[0020] According to a method for determining the deployment location of service outlets for construction machines provided by the present invention, the working condition data includes working hours and start-up rates;

[0021] Correspondingly, the determination of the weight of each construction machine for each service node according to the working condition data of each construction machine includes:

[0022] Determine the eigenvector matrix according to the working hours and the start-up rates;

[0023] Determine the dimensionality-reduced data according to the eigenvector matrix;

[0024] Based on preset rules and the dimensionality-reduced data, determine the weight of each construction machine for each service node.

[0025] According to a method for determining the deployment location of service outlets for construction machines provided by the present invention, after determining the position of the target clustering center as the deployment location of the service outlets for construction machines, it further includes:

[0026] Determine the actual driving distance from each service node to the target cluster center;

[0027] Add the actual driving distance to the clustering samples for clustering update, and obtain the position of the updated cluster center as the deployment position of the construction machinery service network point.

[0028] According to a method for determining the deployment position of a construction machinery service network point provided by the present invention, the iteration of the clustering starting point based on the construction machinery service distance criterion and the weighted distance includes:

[0029] Determine the total weight and the new cluster center of each clustering starting point;

[0030] Traverse all service nodes, and determine the new weighted distance from each service node to the new cluster center;

[0031] Select the nearest distance among the new weighted distances to join the clustering for iteration. When the new weighted distance after iteration meets the construction machinery service distance criterion, stop the iteration.

[0032] The present invention also provides a device for determining the deployment position of a construction machinery service network point, including:

[0033] An acquisition module, configured to acquire the constraint conditions of the service network point;

[0034] A first determination module, configured to determine the number of the service network points of the construction machinery according to the constraint conditions;

[0035] A second determination module, configured to perform clustering processing based on the construction machinery service distance criterion, using the number of the service network points as the number of cluster centers, and determine the deployment position of the construction machinery service network point.

[0036] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method for determining the deployment position of a construction machinery service network point as described in any one of the above are implemented.

[0037] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining the deployment position of a construction machinery service network point as described in any one of the above are implemented.

[0038] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method for determining the deployment position of a construction machinery service network point as described in any one of the above are implemented.

[0039] A method, device and equipment for determining the deployment location of service outlets of construction machinery provided by the present invention. The method includes obtaining the constraint conditions of the service outlets; determining the number of service outlets of the construction machinery according to the constraint conditions; and based on the service distance criterion of the construction machinery, performing clustering processing with the number of service outlets as the number of cluster centers to determine the deployment location of the service outlets of the construction machinery. Since the number of service outlets is accurately determined, and then based on the number of service outlets and the service distance criterion, the deployment location of the service outlets of the construction machinery is planned more scientifically and reasonably, avoiding the problem of unreasonable selection based on manual experience. The optimization of the site selection decision can be ensured through the clustering algorithm, and different service requirements can be better met. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in 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 drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 is a flowchart of the method for determining the deployment location of service outlets of construction machinery provided by an embodiment of the present invention;

[0042] Figure 2 is a flowchart of the clustering processing provided by an embodiment of the present invention;

[0043] Figure 3 is a structural diagram of the device for determining the deployment location of service outlets of construction machinery provided by an embodiment of the present invention;

[0044] Figure 4 is a structural diagram of the electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0046] The following will describe Figures 1-4 a method, device and equipment for determining the deployment location of service outlets of construction machinery of the present invention.

[0047] Figure 1 is a flowchart of the method for determining the deployment location of service outlets of construction machinery provided by an embodiment of the present invention,Figure 2 It is a schematic flowchart of the clustering process provided by an embodiment of the present invention.

[0048] As Figure 1 shown, a method for determining the deployment location of service points for construction machinery provided by an embodiment of the present invention includes the following steps. Among them, the construction machinery includes machinery such as excavators, cranes, loaders, and pile drivers. In this embodiment, a pile driver is used as the construction machinery for detailed description:

[0049] 101. Obtain the constraint conditions of the service points.

[0050] Specifically, the constraint conditions may include at least one of: the product of the weights of different construction machinery and the service distance is minimized, the farthest service distance is less than or equal to the first distance, the average service distance is less than or equal to the second distance, and the minimum number of equipment served is greater than or equal to the preset number. Specifically, when the constraint condition is the product of the weights of different construction machinery and the service distance, the way to obtain the constraint conditions of the service points may specifically be to determine the construction machinery set, the service point set, the weight of each piece of equipment, and the distance from each piece of equipment to the service point; based on a preset rule, according to the construction machinery set, the service point set, the weight of each piece of equipment, and the distance from each piece of equipment to the service point, determine the product of the weights of different construction machinery and the service distance, as shown in formula (1):

[0051] Minimize=∑ i ∑ j w i d ij Y ij (1)

[0052] Among them, Minimize represents the minimization of the product of the weights of construction machinery and the service distance, i represents the construction machinery set, j represents the service point set, w i represents the weight of each piece of construction machinery, d ij represents the distance from construction machinery i to service point j, Y ij =1 means that service point j serves construction machinery i, and Y ij =0 means that service point j does not serve construction machinery i.

[0053] Finally, that is, when the following conditions are simultaneously met: the farthest service distance of the network points is less than or equal to the first distance, the average service distance of the network points is less than or equal to the second distance, the minimum number of devices served is greater than or equal to the preset number, and each device belongs to only one network point, the corresponding number of service network points at this time is determined as the number of service network points to be deployed. Through automatic calculation, the number of service network points is scientifically determined, that is, the number of clustering centers. At the same time, it solves the problem that the traditional clustering algorithm determines the number of clustering centers by manually selecting the number of clustering centers. The number of clustering centers determined by using the operation research model can better meet the different needs of each branch company.

[0054] 101. Determine the number of service network points of the working machine according to the constraint conditions.

[0055] In a specific implementation process, in order to meet the usage requirements of pile drivers in different regions, corresponding service network points need to be deployed in different areas, and the first thing to be determined is the specific number of service network points. Taking City A as an example, there are usage requirements for pile drivers in many places in City A. If there is only one service network point set in a certain place, it is difficult to ensure timely service to different regions. Therefore, it is necessary to determine the number of target service network points according to the different needs of different regions. Accurately determining the number of service network points is the premise for determining the deployment location of the network points. If the number of service network points is small, the service pressure on each service network point is too high. If the number of service network points is large, it is difficult for each service network point to ensure sufficient workload, that is, the number of service network points does not match, resulting in relatively low final service efficiency. Therefore, in this embodiment, it is necessary to more accurately determine the number of service network points according to the first determined constraint conditions.

[0056] 102. Based on the service distance criterion of the working machine, perform clustering processing with the number of service network points as the number of clustering centers to determine the deployment location of the service network points of the working machine.

[0057] Specifically, after determining the number of service outlets, it is necessary to accurately deploy the service outlets at different locations in City A. The criterion for determining the specific deployment location can be determined according to the service distance. That is, after the service outlets are deployed, the distance from each service node to its corresponding service outlet is the minimum value, so as to ensure the service efficiency. The service node can be understood as the operation location of the pile driver. In this embodiment, the mean algorithm is selected to cluster the service outlets and service nodes. The number of service outlets is used as the number of cluster centers, and the clustering process is carried out based on the service distance criterion of the operation machinery, so that the cluster centers determined after clustering are the specific deployment locations of the final service outlets. For example, the clustering algorithm can adopt the k-means clustering algorithm. The k-means clustering algorithm is an iterative clustering analysis algorithm. Its steps are as follows: The data is pre-divided into K groups, that is, the number of service outlets, and then K objects are randomly selected as the initial cluster centers. Then, the distance between each object and each seed cluster center is calculated, and each object is assigned to the cluster center closest to it. The cluster centers and the objects assigned to them represent a cluster. Each time a sample is assigned, the cluster center of the cluster will be recalculated according to the existing objects in the cluster. This process will be repeated continuously until a certain termination condition is met. The termination condition can be that no (or the minimum number) of objects are reassigned to different clusters, no (or the minimum number) of cluster centers change anymore, and the sum of squared errors is locally minimized.

[0058] A method for determining the deployment location of service outlets for operation machinery provided in this embodiment determines the number of service outlets for operation machinery; based on the service distance criterion of operation machinery, clustering is performed with the number of service outlets as the number of cluster centers to determine the deployment location of service outlets for operation machinery. Since the number of service outlets is accurately determined, and then based on the number of service outlets and the service distance criterion, the deployment location of service outlets for operation machinery is planned more scientifically and reasonably, avoiding the problem of unreasonable selection based on manual experience. The clustering algorithm can ensure the optimization of the site selection decision and better meet different service requirements.

[0059] Further, on the basis of the above embodiment, in this embodiment, to determine the number of service outlets for operation machinery, it specifically may include obtaining the constraint conditions of the service outlets.

[0060] Further, on the basis of the above embodiment, in this embodiment, based on the service distance criterion of operation machinery, clustering is performed with the number of service outlets as the number of cluster centers to determine the deployment location of service outlets for operation machinery, such as Figure 2As shown, it may include: determining clustering starting points with the same number as the number of service outlets. Taking the k-means clustering algorithm as an example, it is to determine k clustering starting points, determining the operation machinery weights of each service node, and determining the weighted distances between each service node and the k clustering starting points based on the operation machinery weights. Then, based on the operation machinery service distance criterion and the weighted distances, iterate the clustering starting points until the weighted distances meet the operation machinery service distance criterion, and determine the position of the target clustering center as the deployment position of the operation machinery service outlets. The specific process may include: determining the total weight of the k clustering starting points and the new clustering center; traversing all service nodes to determine the new weighted distances from each service node to the k new clustering centers; selecting the nearest distance among the k new weighted distances to join the clustering for iteration, and stopping the iteration when the new weighted distances after iteration meet the operation machinery service distance criterion. That is, when the position of the clustering center, i.e., the service outlet, changes when adding a new clustering sample, continue to iterate until the clustering center no longer changes, indicating that the iteration is completed at this time, and the corresponding clustering center is the specific deployment position of the service outlet.

[0061] Among them, affected by factors such as equipment start-up rate, differences in large and small machine models, machine age / usage time, etc., the demand degree of each equipment for services is different. Therefore, determining the operation machinery weights of each service node may include: determining the working condition data of each operation machinery; determining the weight of each operation machinery of each service node according to the working condition data of each operation machinery. The working condition data may include parameters such as working hours, start-up rate, machine model, machine age, etc.; correspondingly, determining the weight of each operation machinery of each service node according to the working condition data of each operation machinery, taking working hours and start-up rate as an example, may include: determining the feature vector matrix according to working hours and start-up rate; determining the dimensionality-reduced data according to the feature vector matrix; determining the weight of each operation machinery of each service node based on a preset rule and the dimensionality-reduced data. Specifically, it can be like the calculation process in formula (2):

[0062]

[0063]

[0064] P = E T

[0065] Y = PX

[0066] W = ((Y + |min(Y)|)·p break ) α (2)

[0067] where X is the working hours and operating rate matrix; C is the covariance matrix of the obtained working hours and operating rate; E is the eigenvector matrix of n features, which is the eigenvector matrix of 2 features in this embodiment; Y is the data after dimensionality reduction; P break is the summons occupancy ratio of the model to which each device belongs; α is the importance coefficient of Y; W is the final reference weight of each device, λ 1 is the working hours, λ 2 is the operating rate, and P can be understood as an intermediate conversion variable.

[0068] Thus, the weight of each device can be obtained, enabling a better calculation of the weighted distance between each device and the clustering center based on the weight, that is, matching the weight according to the working ability of the device. For example, to complete the same task, the working efficiency of one construction machine is 2 hours, while that of another is 3 hours. Therefore, the corresponding weights can be appropriately matched according to the working efficiency. Even if the service distance is a bit farther, but the working efficiency of the construction machine is high, it will result in a high final completion efficiency. Therefore, the main purpose of determining the weight of each construction machine is to more reasonably comprehensively match the working conditions of the construction machine and the service distance and then determine the weighted distance.

[0069] By considering factors such as the model, age, workload, and operating rate of the device into the algorithm and taking the intensity relationship between the maintenance frequency and the demand for service points as an important reference factor for setting the weight coefficient, the clustering center is biased towards devices with more workload and more intensive working areas, which can also ensure that the finally determined service points are more in line with the actual needs and services, and better guarantee the service quality and working efficiency.

[0070] Further, on the basis of the above embodiment, in order to overcome the defect that the straight-line distance is used for iteration in the distance determination of the clustering algorithm, after initially determining the position of the target clustering center as the deployment position of the construction machine service point, it may further include: determining the actual driving distance from each service node to the target clustering center; adding the actual driving distance to the clustering sample for clustering update, and obtaining the position of the updated clustering center as the deployment position of the construction machine service point, that is, making the calculation of the distance more in line with the actual environmental information, considering the influence of geographical and terrain environments on the setting of service points, and more accurately ensuring that the setting of service points meets different needs.

[0071] The present invention optimizes the problem of selecting the number of cluster centers by the K-means clustering algorithm by adopting the method of operations linear programming. In the algorithm, it can make real-time automatic adjustments according to parameters such as the number of pile driver equipment and the distribution of pile driver equipment, so that the selection of the number of cluster centers is more reasonable and does not require human intervention; and the derivation and explanation of weight calculation are added to achieve the purpose of shifting the cluster centers to areas with more workload, higher start-up rate and denser equipment distribution, so that the final deployment of service network locations can meet different needs to the greatest extent.

[0072] Based on the same general inventive concept, the present application also protects a device for determining the deployment location of an operating machinery service point. The device for determining the deployment location of an operating machinery service point provided by the present invention is described below. The device for determining the deployment location of an operating machinery service point described below and the method for determining the deployment location of an operating machinery service point described above can be referenced to each other.

[0073] Figure 3 This is a schematic diagram of the structure of the device for determining the deployment location of the operating machinery service network provided by an embodiment of the present invention.

[0074] like Figure 3 As shown, an embodiment of the present invention provides a device for determining a deployment location of a working machinery service network point, comprising:

[0075] An acquisition module 31 is used to acquire the constraint conditions of the service outlets;

[0076] A first determination module 32, used to determine the number of service outlets of the operating machinery according to the constraint conditions;

[0077] The second determination module 33 is used to perform clustering processing based on the operating machinery service distance criterion and take the number of service outlets as the number of cluster centers to determine the deployment location of the operating machinery service outlets.

[0078] The present embodiment provides a device for determining the deployment location of a working machinery service outlet. The device obtains the constraint conditions of the service outlets, and determines the number of service outlets for the working machinery according to the constraint conditions. The device performs clustering processing based on the number of service outlets as the number of cluster centers based on the working machinery service distance criterion to determine the deployment location of the working machinery service outlets. Since the number of service outlets is accurately determined, the deployment location of the working machinery service outlets is planned more scientifically and reasonably based on the number of service outlets and the service distance criterion, thereby avoiding the problem of unreasonable selection based on manual experience. The clustering algorithm can ensure the optimization of site selection decisions and better meet different service needs.

[0079] Further, the constraint conditions in this embodiment include at least one of the following: the product of the weights of different construction machines and the service distances is minimized, the farthest service distance is less than or equal to the first distance, the average service distance is less than or equal to the second distance, and the minimum number of equipment serving is greater than or equal to the preset number.

[0080] Further, the first determination module 31 in this embodiment is specifically further configured to:

[0081] Determine the set of construction machines, the set of service points, the weight of each piece of equipment, and the distance from each piece of equipment to the service point;

[0082] Based on the preset rules, determine the product of the weights of different construction machines and the service distances according to the set of construction machines, the set of service points, the weight of each piece of equipment, and the distance from each piece of equipment to the service point.

[0083] Further, the second determination module 32 in this embodiment is specifically configured to:

[0084] Determine the clustering starting points with the same number as the number of service points;

[0085] Determine the weight of the construction machine for each service node, and determine the weighted distance between each service node and the clustering starting point according to the weight of the construction machine;

[0086] Based on the construction machine service distance criterion and the weighted distance, iterate on the clustering starting points until the weighted distance meets the construction machine service distance criterion, and determine the position of the target clustering center as the deployment position of the construction machine service point.

[0087] Further, the second determination module 32 in this embodiment is specifically further configured to:

[0088] Determine the working condition data of each construction machine;

[0089] According to the working condition data of each construction machine, determine the weight of each construction machine for each service node.

[0090] Further, the working condition data in this embodiment includes working hours and start-up rate;

[0091] Correspondingly, the second determination module 32 is specifically further configured to:

[0092] Determine the eigenvector matrix according to the working hours and the start-up rate;

[0093] Determine the dimensionality-reduced data according to the eigenvector matrix;

[0094] Based on the preset rules and the dimensionality-reduced data, determine the weight of each construction machine for each service node.

[0095] The second determination module 32 in this embodiment is specifically further configured to:

[0096] Determine the total weight of each cluster starting point and the new cluster center;

[0097] Traverse all service nodes to determine the new weighted distance from each service node to the new cluster center;

[0098] Select the nearest distance among the new weighted distances to join the cluster for iteration. When the new weighted distance after iteration meets the service distance criterion of the working machine, stop the iteration.

[0099] Furthermore, the second determination module 32 in this embodiment is specifically further configured to:

[0100] Determine the actual driving distance from each service node to the target cluster center;

[0101] Add the actual driving distance to the cluster samples for cluster update, and obtain the position of the updated cluster center as the deployment position of the service network points of the working machine.

[0102] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0103] As Figure 4 shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call the logical instructions in the memory 430 to execute the method for determining the deployment position of the service network points of the working machine. The method includes: obtaining the constraint conditions of the service network points, and determining the number of service network points of the working machine according to the constraint conditions; based on the service distance criterion of the working machine, performing clustering processing with the number of service network points as the number of cluster centers, and determining the deployment position of the service network points of the working machine.

[0104] In addition, when the logical instructions in the above-mentioned memory 430 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0105] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for determining the deployment location of service points of construction machinery provided by the above-mentioned various methods. The method includes: obtaining the constraint conditions of the service points, and determining the number of service points of the construction machinery according to the constraint conditions; based on the service distance criterion of the construction machinery, performing clustering processing with the number of service points as the number of cluster centers to determine the deployment location of the service points of the construction machinery.

[0106] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for determining the deployment location of service points of construction machinery provided by the above-mentioned various methods. The method includes: obtaining the constraint conditions of the service points, and determining the number of service points of the construction machinery according to the constraint conditions; based on the service distance criterion of the construction machinery, performing clustering processing with the number of service points as the number of cluster centers to determine the deployment location of the service points of the construction machinery.

[0107] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0108] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the deployment location of service outlets for construction machinery, characterized in that, it includes: Obtain the constraint conditions of the service outlets; According to the constraint conditions, determine the number of the service outlets for the construction machinery; Based on the service distance criterion of the construction machinery, perform clustering processing with the number of the service outlets as the number of cluster centers, and determine the deployment location of the service outlets for the construction machinery; The performing clustering processing with the number of the service outlets as the number of cluster centers based on the service distance criterion of the construction machinery to determine the deployment location of the service outlets for the construction machinery includes: Determine cluster starting points with the same number as the number of the service outlets; Determine the construction machinery weight of each service node, and determine the weighted distance between each service node and the cluster starting point according to the construction machinery weight; Based on the service distance criterion of the construction machinery and the weighted distance, iterate the cluster starting points until the weighted distance meets the service distance criterion of the construction machinery, and determine the position of the target cluster center as the deployment location of the service outlets for the construction machinery.

2. The method for determining the deployment location of service outlets for construction machinery according to claim 1, characterized in that, the constraint conditions include at least one of the following: the product of different construction machinery weights and service distances is the smallest, the farthest service distance is less than or equal to the first distance, the average service distance is less than or equal to the second distance, and the minimum number of equipment served is greater than or equal to the preset number.

3. The method for determining the deployment location of service outlets for construction machinery according to claim 2, characterized in that, when the constraint condition is the product of different construction machinery weights and service distances, the obtaining the constraint conditions of the service outlets includes: Determine the construction machinery set, the service outlet set, the weight of each piece of equipment, and the distance from each piece of equipment to the service outlet; Based on the preset rules, determine the product of different construction machinery weights and service distances according to the construction machinery set, the service outlet set, the weight of each piece of equipment, and the distance from each piece of equipment to the service outlet.

4. The method for determining the deployment location of service outlets for construction machinery according to claim 1, characterized in that, the determining the construction machinery weight of each service node includes: Determine the working condition data of each piece of construction machinery; According to the working condition data of each piece of construction machinery, determine the weight of each piece of construction machinery at each service node.

5. The method for determining the deployment location of service outlets for construction machinery according to claim 4, characterized in that, the working condition data includes working hours and startup rate; Correspondingly, the determining the weight of each piece of construction machinery at each service node according to the working condition data of each piece of construction machinery includes: Determine the eigenvector matrix according to the working hours and the startup rate; Determine the dimensionality-reduced data according to the eigenvector matrix; Based on the preset rules and the dimensionality-reduced data, determine the weight of each piece of construction machinery at each service node.

6. The method for determining the deployment location of service outlets for construction machinery according to claim 1, characterized in that, after determining the position of the target cluster center as the deployment location of the service outlets for the construction machinery, it further includes: Determine the actual driving distance from each service node to the target cluster center; Add the actual driving distance to the clustering samples for clustering update, and obtain the position of the updated cluster center as the deployment position of the construction machinery service network point.

7. The method for determining the deployment position of a construction machinery service network point according to claim 1, characterized in that, the iteration of the clustering starting point based on the construction machinery service distance criterion and the weighted distance includes: Determine the total weight and the new cluster center of each clustering starting point; Traverse all service nodes to determine the new weighted distance from each service node to the new cluster center; Select the nearest distance among the new weighted distances to join the clustering for iteration, and stop the iteration when the new weighted distance after iteration meets the construction machinery service distance criterion.

8. A device for determining the deployment position of a construction machinery service network point, characterized in that, comprising: An acquisition module for acquiring the constraint conditions of the service network point; A first determination module for determining the number of the service network points of the construction machinery according to the constraint conditions; A second determination module for performing clustering processing with the number of the service network points as the number of cluster centers based on the construction machinery service distance criterion to determine the deployment position of the construction machinery service network point; The performing clustering processing with the number of the service network points as the number of cluster centers based on the construction machinery service distance criterion to determine the deployment position of the construction machinery service network point includes: Determine clustering starting points with the same number as the number of service network points; Determine the construction machinery weight of each service node, and determine the weighted distance between each service node and the clustering starting point according to the construction machinery weight; Based on the construction machinery service distance criterion and the weighted distance, iterate the clustering starting point until the weighted distance meets the construction machinery service distance criterion, and determine the position of the target cluster center as the deployment position of the construction machinery service network point.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the program, the steps of the method for determining the deployment position of a construction machinery service network point according to any one of claims 1 to 7 are implemented.

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