CN112948748A - Multi-benefit-subject-based major infrastructure site selection optimization method and system - Google Patents
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Abstract
The invention discloses a multi-benefit agent-based major infrastructure site selection optimization method and a system, wherein the method comprises the following steps: acquiring benefit index data of a plurality of benefit agents and benefit agents, and constructing a multi-agent model according to the benefit agents and the benefit index data; determining a candidate address set of the major infrastructure according to the multi-agent model; acquiring a cellular automaton model, inputting a candidate address set into the cellular automaton model, and simulating the development trend of benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results; and determining a target site selection result of the major infrastructure according to the benefit change simulation results. The invention considers the competition and cooperation relationship of multiple beneficial agents in the construction of the major infrastructure, quantitatively describes the influence of the major infrastructure construction on the future development of the urban group, and realizes the site selection optimization of the major infrastructure of the urban group.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a system for optimizing site selection of major infrastructure based on multi-benefit agents.
Background
The major infrastructure is a large-scale public infrastructure having profound influence on the country, the society, politics, science and technology, economic development, environmental governance, public safety, the interests of people and the like, such as: the three gorges project, the spherical radio telescope with the caliber of 500 meters (Chinese skyhook), the Gangzhu-Auao bridge, the Beijing great airport, the south-to-north water diversion, the west-to-east gas transportation and other projects. The important infrastructure is the important material foundation for the rapid development of national economy. The investment of major infrastructure is huge, the interest correlators are numerous, the reasonable site selection of the major infrastructure is related to the sustainable development of economy, society and resources, and the influence is complex and profound.
The existing facility site selection optimization method is generally carried out aiming at public service infrastructures in cities, such as hospitals, schools, business centers, bus stations and the like, but the facility site selection optimization method in the prior art has the following problems: first, the existing methods generally generalize the addressing problem into a demand-supply balance problem, ignoring the competing game process of multiple benefit agents; secondly, the existing methods mostly select sites according to the current situation characteristics of a target area, and the long-term influence of facility construction on the development of important factors such as regional territory, industry, traffic, population and the like is not considered; thirdly, the existing method usually focuses on researching the influence of the internal factors of the cities on the site selection problem, and the influence of the interrelation of the cities on the site selection of the major infrastructure is less considered.
Thus, there is still a need for improvement and development of the prior art.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method and a system for optimizing the site of a major infrastructure based on multi-benefit agents, aiming at solving the problems existing in the method for optimizing the site of a facility in the prior art: first, the existing methods generally generalize the addressing problem into a demand-supply balance problem, ignoring the competing game process of multiple benefit agents; secondly, the existing methods mostly select sites according to the current situation characteristics of a target area, and the long-term influence of facility construction on the development of important factors such as regional territory, industry, traffic, population and the like is not considered; thirdly, the existing method usually focuses on researching the influence of the internal factors of the cities on site selection, and less considers the influence of the interrelation of the cities on the site selection of the major infrastructure on the city group scale.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect, an embodiment of the present invention provides a method for optimizing a location of a large infrastructure based on multiple stakeholders, where the method includes:
acquiring a plurality of benefit main bodies and benefit index data of the benefit main bodies, and constructing a multi-agent model according to the benefit main bodies and the benefit index data;
determining a candidate site set of a major infrastructure according to the multi-agent model;
obtaining a cellular automaton model, inputting the candidate address set into the cellular automaton model, and simulating the development trend of the benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results;
and determining a target site selection result of the major infrastructure according to the benefit change simulation results.
In one implementation, the building a multi-agent model from the stakeholder and the stakeholder data comprises:
constructing a plurality of single intelligent agent models according to the benefit main body and the benefit index data;
and constructing a multi-agent model according to a plurality of single-agent models.
In one implementation, the constructing the plurality of single agent models according to the benefit agent and the benefit target data includes:
performing benefit analysis on the benefit subject and the benefit index data to obtain a benefit analysis result;
determining a utility value according to the benefit analysis result;
determining a space address selection set according to the utility value;
the benefit index data, the benefit analysis result, the utility value and the spatial addressing set form a plurality of single intelligent agent models.
In one implementation, the determining a set of spatial addresses according to the utility value includes:
determining the utility weight of each interest principal by an analytic hierarchy process according to the utility value;
and multiplying and accumulating the utility value and the utility weight to obtain a spatial address selection set.
In one implementation, the determining a candidate set of sites for a significant infrastructure from the multi-agent model comprises:
making a decision, scheduling and coordinating the site selection tendency of the multi-agent model to obtain a first site selection set;
and on the basis of the first addressing set, making a decision, scheduling and coordinating addressing trends among the multi-agent models to obtain a candidate addressing set of major infrastructure.
In one implementation, the obtaining a cellular automaton model, inputting the candidate address set into the cellular automaton model, and simulating a development trend of the benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results includes:
obtaining a cellular automaton model, and inputting the candidate address set into the cellular automaton model; the cellular automaton model comprises cellular units, a cellular space, a neighborhood and a transformation rule;
obtaining a single benefit index change rule;
when the candidate site set comprises the major infrastructure, performing iterative simulation on the benefit index data and the single benefit index change rule based on the cellular automaton model to obtain a first single benefit simulation result;
when the candidate site set does not contain the major infrastructure, performing iterative simulation on the benefit index data and the single benefit index change rule based on the cellular automaton model to obtain a second single benefit simulation result;
obtaining a single benefit variation difference value according to the first single benefit simulation result and the second single benefit simulation result, and obtaining a plurality of benefit variation simulation results according to the single benefit variation difference value.
In one implementation, the determining a target location result for a significant infrastructure from the benefit change simulation results comprises:
and solving the optimal solution of the benefit change simulation results according to the balance analysis criteria of the benefit index data, and determining the target site selection result of the major infrastructure.
In a second aspect, an embodiment of the present invention further provides a system for optimizing a big infrastructure site based on multiple stakeholders, where the system includes: the multi-agent model building module is used for obtaining a plurality of benefit main bodies and benefit index data of the benefit main bodies and building a multi-agent model according to the benefit main bodies and the benefit index data;
a candidate site set obtaining module, configured to determine a candidate site set of a major infrastructure according to the multi-agent model;
the benefit change simulation result acquisition modules are used for acquiring a cellular automaton model, inputting the candidate address set into the cellular automaton model, and simulating the development trend of the benefit index data based on the cellular automaton model to obtain benefit change simulation results;
and the target site selection result acquisition module is used for determining a target site selection result of the major infrastructure according to the benefit change simulation results.
In a third aspect, an embodiment of the present invention further provides an intelligent terminal, including a memory, and one or more programs, where the one or more programs are stored in the memory, and configured to be executed by one or more processors, where the one or more programs include instructions for executing the method for multi-benefit agent based big infrastructure location optimization as described in any one of the above.
In a fourth aspect, embodiments of the present invention also provide a non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform a multi-stakeholder-based critical infrastructure location optimization method as described in any one of the above.
The invention has the beneficial effects that: the method comprises the steps of firstly obtaining a plurality of benefit main bodies and benefit index data of the benefit main bodies, and constructing a multi-agent model according to the benefit main bodies and the benefit index data; then determining a candidate site selection set of the major infrastructure according to the multi-agent model; then obtaining a cellular automaton model, inputting the candidate address set into the cellular automaton model, and simulating the development trend of the benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results; finally, determining a target site selection result of the major infrastructure according to the benefit change simulation results; therefore, the competition and cooperation relationship of multiple beneficial agents in the construction of the major infrastructure is considered, the influence of the major infrastructure construction on the future development of the urban group is quantitatively depicted, the site selection optimization of the major infrastructure of the urban group is realized, the construction engineering benefit of the major infrastructure is promoted, and the sustainable development of the economy, the society and the resources of the urban group is promoted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for optimizing a location of a large infrastructure based on multiple benefit agents according to an embodiment of the present invention.
Fig. 2 is a schematic block diagram of a multi-benefit agent-based big infrastructure location optimization apparatus according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
The invention discloses a method and a system for optimizing the site selection of major infrastructure based on multi-benefit agents, and in order to make the purpose, the technical scheme and the effect of the invention clearer and clearer, the invention is further described in detail by referring to the attached drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Because the prior art has the following problems: first, the existing methods generally generalize the addressing problem into a demand-supply balance problem, ignoring the competing game process of multiple benefit agents; secondly, the existing methods mostly select sites according to the current situation characteristics of a target area, and the long-term influence of facility construction on the development of important factors such as regional territory, industry, traffic, population and the like is not considered; thirdly, the existing method usually focuses on researching the influence of the internal factors of the cities on the site selection problem, and the influence of the interrelation of the cities on the site selection of the major infrastructure is less considered.
In order to solve the problems in the prior art, the embodiment provides a multiple-benefit-subject-based major infrastructure site selection optimization method, and site selection optimization of major infrastructures of an urban group is realized through the method, so that the construction engineering benefits of the major infrastructures are improved, and further sustainable development of economy, society and resources of the urban group is promoted. During specific implementation, a plurality of benefit agents and benefit index data of the benefit agents are obtained, and a multi-agent model is built according to the benefit agents and the benefit index data; then determining a candidate site selection set of the major infrastructure according to the multi-agent model; then obtaining a cellular automaton model, inputting the candidate address set into the cellular automaton model, simulating the development trend of the benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results, and simulating the development trend of the benefit index data according to the cellular automaton to obtain a plurality of benefit change simulation results; and finally, determining a target site selection result of the major infrastructure according to the benefit change simulation results.
Illustrate by way of example
The existing facility site selection optimization technology and method are generally carried out aiming at public service infrastructures in cities, such as hospitals, schools, business centers, bus stations and the like, and site selection optimization is realized by constructing a P-center model, a P-median model or a maximum demand coverage model. The P center model distributes demand points and supply points in the system in a certain plane range, compares the demand quantity and the supply quantity of each point into the weight of each point in the system, and obtains the center of gravity of the system as an optimal addressing position; the P-median model takes the shortest weighted distance from all demand points to the facility positions as an optimization function, and P facility positions are solved as the optimal site selection positions; the maximum demand coverage model considers the situation that the supply resources cannot meet all demands, and the optimal site selection position is solved by giving the conditions of facility construction investment, service capacity and the like to maximize the demand as an optimization function. The site selection model plays an important role in site selection of urban facilities, but has defects when being applied to site selection of major infrastructure of urban groups: first, the existing methods generally generalize the addressing problem into a demand-supply balance problem, ignoring the competing game process of multiple benefit agents; secondly, the existing methods mostly select sites according to the current situation characteristics of a target area, and the long-term influence of facility construction on the development of important factors such as regional territory, industry, traffic, population and the like is not considered; thirdly, the existing method usually focuses on researching the influence of the internal factors of the cities on the site selection problem, and the influence of the interrelation of the cities on the site selection of the major infrastructure is less considered. In the embodiment of the invention, a method and a system for optimizing the site selection of the major infrastructure based on the multiple benefit agents are provided to solve the problems, in the embodiment, a plurality of benefit agents and benefit index data of the benefit agents are obtained firstly, and a multiple agent model is constructed according to the benefit agents and the benefit index data; then determining a candidate site selection set of the major infrastructure according to the multi-agent model; then obtaining a cellular automaton model, inputting the candidate address set into the cellular automaton model, simulating the development trend of the benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results, and simulating the development trend of the benefit index data according to the cellular automaton to obtain a plurality of benefit change simulation results; and finally, determining a target site selection result of the major infrastructure according to the benefit change simulation results. The invention considers the competition and cooperation relationship of multiple beneficial agents in the construction of the major infrastructure, quantitatively describes the influence of the major infrastructure construction on the future development of the urban group, realizes the site selection optimization of the major infrastructure of the urban group, is beneficial to promoting the construction engineering benefit of the major infrastructure, and further promotes the sustainable development of the economy, the society and the resources of the urban group.
Exemplary method
The embodiment provides a multi-benefit-subject-based major infrastructure site selection optimization method, which can be applied to an intelligent terminal of a computer. As shown in fig. 1 in detail, the method includes:
s100, obtaining a plurality of benefit agents and benefit index data of the benefit agents, and constructing a multi-agent model according to the benefit agents and the benefit index data;
in particular, the construction of a significant infrastructure can have a significant and profound impact on different stakeholders. Aiming at the site selection optimization decision of major infrastructure of the urban group, the invention selects governments, organizations and individuals as typical benefit subjects to depict the competition process of the benefit subjects. Wherein: government: as a main maker of policy and macro planning, the method has the core benefit targets of improving the economic development level, promoting the stable development of the society and guaranteeing the people's living and entertainment industry; organizing: the production, marketing, transportation and service activities performed by an organization are all affected by infrastructure construction. Organizing to achieve the goal of maximizing the benefits of the organization according to the conclusion of rational human hypothesis; individual: as the most important component of society, individuals are often affected by infrastructure construction on an indirect level. The interest target is more considered from the aspects of life quality, work value and the like to seek the maximization of individual interest. The method is characterized in that the site selection of the major infrastructure is closely related to the benefits of typical benefit agents, reasonable site selection can better give consideration to the benefits of all typical benefit agents, so that benefit index data of the typical benefit agents are obtained firstly, and respective benefit index data of the typical benefit agents are established according to different typical benefit agents, so that the benefit change of each benefit agent is conveniently and quantitatively depicted. Wherein: government: by analyzing the characteristics of government, GDP, employment ratio, regional intermediary centrality, ecological protection and the like are selected to describe the benefit change of government bodies; organizing: by analyzing the characteristics of the organization, selecting investment cost, capital return rate, flow rate and flow indexes of the area in the urban group to depict the benefit change of the organization main body; individual: by analyzing individual preference, three interest indexes of disposable income, accessibility and payroll level are selected to characterize individual interest change. It should be noted that the benefit agent and index system are not limited to the above listed elements, and may be modified and expanded as desired. And then constructing a multi-agent model according to the benefit index data to prepare for subsequent site selection. Correspondingly, the building of the multi-agent model according to the benefit agent and the benefit index data comprises the following steps: constructing a plurality of single intelligent agent models according to the benefit main body and the benefit index data; and constructing a multi-agent model according to a plurality of single-agent models.
Specifically, a plurality of single intelligent agent models are constructed according to the benefit main body and the benefit index data; correspondingly, the step of constructing a plurality of single intelligent agent models according to the benefit main body and the benefit index data comprises the following steps: performing benefit analysis on the benefit subject and the benefit index data to obtain a benefit analysis result; determining a utility value according to the benefit analysis result; determining a space address selection set according to the utility value; the benefit index data, the benefit analysis result, the utility value and the spatial addressing set form a plurality of single intelligent agent models. Specifically, benefit analysis is carried out on the benefit index data to obtain a benefit analysis result; for example, where D represents benefit index data of a benefit Agent (Agent), established at the beginning of the simulation, S represents the result of benefit analysis for each benefit; for example, government events: observing where major infrastructure sites can protect the ecological environment; secondly, observing which of the land used in the non-ecological environment can promote economic development and employment, and sequencing the development quality of the land. Organizing the events: observing that major infrastructure sites are arranged in a traffic convenience area and an existing organization development area; observing major infrastructure types and specially matching different organization types. Individual events: firstly, observing that the site selection of major infrastructure needs to be far away from residential areas; observing whether the site selection of the major infrastructure can improve the individual life quality or the work quality; then, according to the benefit analysis result, determining a utility value; wherein F represents a utility value of an event; for example, the government: the government utility function is calculated with reference to population indicators and traffic indicators. Firstly, setting all ecological lands as unavailable according to the land utilization type in the calculation process; screening all land for construction, and calculating the development quality by using population indexes and traffic indexes:
in the formula (d)minRepresenting the reciprocal of the minimum distance to the traffic, P representing a population index, E representing an ecological index, E representing an ecological land, and obtaining a utility value F corresponding to the government utility function according to the government utility functionquality(ii) a Organization utility function: calculating different areas through the economic index (I) and the traffic index (D), wherein the formula is as follows:
obtaining a utility value F corresponding to the tissue utility function according to the tissue utility functioncompany. Individual utility function: using land use data and for populated areas (d)1) And city functional area (d)2) Respectively calculating the distance by superposingCalculating D ═ D1+d2) And obtaining a utility value D corresponding to the individual utility function according to the individual utility function. Then according to the utility value, determining a space addressing set; correspondingly, the determining the spatial address set according to the utility value includes: determining the utility weight of each interest principal by an analytic hierarchy process according to the utility value; and multiplying and accumulating the utility value and the utility weight to obtain a spatial address selection set. Wherein A ispsRepresenting a set of possible candidate addresses for spatial addressing. For example, the weights of different typical benefit agents are determined by an analytic hierarchy process based on utility values of different typical benefit agents of the utility function:
in the formula ApsPossible solutions for spatial addressing, pi,jUtility value for each event (utility value corresponding to government utility function is FqualityThe utility value corresponding to the tissue utility function is FcompanyThe utility value corresponding to the individual utility function is D), wfThe preference of the government, organization and individual Agent for site selection is finally obtained as the weighted value (A)psThe larger the value, the more likely the addressing, and vice versa). The benefit index data, the benefit analysis result, the utility value and the spatial addressing set form a plurality of single intelligent agent models.
And after obtaining a plurality of single intelligent agent models, constructing a multi-intelligent agent model according to the plurality of single intelligent agent models. For example, in the present embodiment, in order to construct the final multi-stakeholder addressing set, the addressing preferences of three typical stakeholders (government, organization, individual) need to be coordinated, and the coordination is performed according to the preferences of different typical stakeholders and events:
in the formula, U is a multi-agent model, ApsAddressing a space with a possible set of candidate addresses, ZkFor units that consider events globally, A is the event represented by each typical stakeholder.
Having obtained the multi-agent model, the following steps may be performed as shown in FIG. 1: s200, determining a candidate address set of the major infrastructure according to the multi-agent model; correspondingly, in order to obtain the candidate site selection set of the major infrastructure, the determining the candidate site selection set of the major infrastructure according to the multi-agent model comprises the following steps:
s201, making a decision, scheduling and coordinating the site selection tendency of the multi-agent model to obtain a first site selection set;
s202, on the basis of the first addressing set, making a decision, scheduling and coordinating addressing trends among the multi-agent models to obtain a candidate addressing set of major infrastructure.
Specifically, data collection and preprocessing are performed first, and basic geographic data indexes are selected, such as basic geographic data indexes of territory, industry, population, traffic, ecology and the like are selected to assist candidate set construction and benefit change simulation. The method specifically comprises the following steps: regional GDP, pedestrian GDP, permanent population, floating population, railway network, expressway, national road, provincial road, city main road, subway network, permanent basic farmland area, ecological protection red line, forest land area, cultivated land area, grassland area, wetland area, shrubbery land area, water body area, commercial land area, residential land area, per capita income and the like. It should be noted that the basic geographic data index is not limited to the above listed elements, and may be extended as needed. Then, making a decision, scheduling and coordinating the site selection tendency of the multi-agent model to obtain a first site selection set; for example, the decision making on the addressing propensity of the multi-agent model includes bit selection and addressing, wherein the bit selection is used for selecting what area to set the facility, and after the addressing is used for area selection, the specific selection is used for setting the facility at what position of the area. The scheduling and coordination of the site selection tendency of the multi-agent model comprises scheduling and coordination of traffic resources, land resources, water resources, employment resources and the like related to the site selection site. And finally, on the basis of the first addressing set, making a decision, scheduling and coordinating addressing trends among the multi-agent models to obtain a candidate addressing set of major infrastructure.
After obtaining the candidate address set, the following steps as shown in fig. 1 may be performed: step S300, obtaining a cellular automaton model, inputting the candidate address set into the cellular automaton model, and simulating the development trend of the benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results, wherein the steps comprise:
s301, obtaining a cellular automaton model, and inputting the candidate address set into the cellular automaton model; the cellular automaton model comprises cellular units, a cellular space and a neighborhood;
s302, obtaining a single benefit index change rule;
s303, when the candidate address set comprises the major infrastructure, performing iterative simulation on the benefit index data and the single benefit index change rule based on the cellular automaton model to obtain a first single benefit simulation result;
s304, when the candidate address set does not contain the major infrastructure, performing iterative simulation on the benefit index data and the single benefit index change rule based on the cellular automaton model to obtain a second single benefit simulation result;
s305, obtaining a single benefit change difference value according to the first single benefit simulation result and the second single benefit simulation result, and obtaining a plurality of benefit change simulation results according to the single benefit change difference value.
Specifically, a cellular automaton model is obtained firstly, wherein the cellular automaton model is composed of cellular units, cellular space and neighborhood, and the cellular space is the whole research area; the neighborhood is that adjacent space units in the space units are taken from the irregular space units; in practice, the city group space partitioning and index spatialization includes partitioning the city group area, and taking the town streets as space units. Determining research time and space range to obtain data, and obtaining various basic geographic data index data of each space unit of the urban group in different periods and the selected benefit index data of each benefit subject according to the determined time range and space range and through data sources such as a statistical yearbook, a remote sensing image, mobile phone data, social media and the like. And carrying out spatialization processing on the acquired data to acquire index element data of each space unit of the urban group. Inputting the candidate address set into the cellular automaton model after the cellular automaton model exists; then the cellular automaton model has information of a candidate address set, and then a single benefit index change rule is obtained, for example, a multi-scale urban group cellular automaton model space-time flow matrix is constructed based on mobile phone signaling data inside a target urban group; constructing accessibility matrixes of cellular automata models to the infrastructures based on the existing infrastructure site selection distribution and the candidate set positions; and determining a single interest index change rule according to the basic geographic data of each unit, the space-time flow matrix of the cellular automaton model and the reachability matrix. When the candidate site set comprises the major infrastructure, performing iterative simulation on the benefit index data and the single benefit index change rule based on the cellular automaton model to obtain a first single benefit simulation result; when the candidate site set does not contain the major infrastructure, performing iterative simulation on the benefit index data and the single benefit index change rule based on the cellular automaton model to obtain a second single benefit simulation result; for example, after a plurality of iterations are performed according to the benefit index data condition of the initial moment in the target research area and the benefit index change rule of each cellular automaton model, and the preset iteration times are calculated, the benefit index benefit condition of each cellular automaton model in the target research area is calculated. And calculating the benefit difference of the benefit indexes corresponding to the construction facilities at the candidate set position and the construction facilities not at the candidate set position to obtain the benefit change conditions of the two single benefit indexes. In this embodiment, according to the same method, a government single interest change difference, an organization single interest change difference and an individual single interest change difference can be obtained respectively, and a plurality of interest change simulation results, that is, a government interest change simulation result, an organization interest change simulation result and an individual interest change simulation result can be obtained through fusion. Finally, the simulation is respectively carried out on each index in the benefit index system, and the change situation of all benefit indexes of each benefit subject is obtained when the major infrastructure is built or not built at each position point in the candidate site set, for example, when the major infrastructure is built at each position point in the candidate site set, the change situation of the GDP of the government after 5 years in the future, and when the major infrastructure is not built at each position point in the candidate site set, the change situation of the GDP of the government after 5 years in the future, and similarly, the change situations of other benefit indexes of other benefit subjects (including governments, organizations and individuals) are obtained.
After obtaining the results of the benefit variation simulation, the following steps can be performed as shown in fig. 1: and S400, determining a target site selection result of the major infrastructure according to the benefit change simulation results. Correspondingly, the step of determining the target location result of the major infrastructure according to the benefit change simulation results comprises the following steps: s401, solving the optimal solution of the benefit change simulation results according to the balance analysis criteria of the benefit index data, and determining the target site selection result of the major infrastructure. For example, the benefit change situation of each location facility in the candidate location set is calculated according to the benefit change simulation results, and the calculation formula is as follows:
PMerge＝ρ1·Pgovernment+ρ2·Porganization+ρ3·Pindividual
Pgovernment＝α1·B1+α2·B2+α3·B3+α4·B4
Porganization＝α5·B5+α6·B6+α7·B7+α8·B8
Pindividual＝α9·B9+α10·B10+α11·B11
in the formula, PMergeFor multi-benefit agent to fuse benefit indicator changes when building facilities at the yes/no candidate location, Pgovernment，Porganization，PindividualThe overall interest index changes of three types of interest subjects of government, organization and individual respectively, B1～B11For the single interest index variation, ρ, obtained by simulation in step S3051,ρ2,ρ3And alpha1～α11For the weight coefficient, the weight coefficient may be determined by an expert scoring method or an analytic hierarchy method. And selecting the site with the maximum fusion benefit according to the calculated benefit change condition of the facility established at each position in the candidate position set and the balance analysis criterion of the benefit index data, wherein the site is the target site selection result of the major infrastructure obtained by the method.
Exemplary System
As shown in fig. 2, an embodiment of the present invention provides a multi-benefit agent-based major infrastructure site selection optimization system, which includes a multi-agent model building module 501, a candidate site selection set obtaining module 502, a plurality of benefit change simulation result obtaining modules 503, and a target site selection result obtaining module 504, wherein:
the multi-agent model building module 501 is used for obtaining a plurality of benefit agents and benefit index data of the benefit agents and building a multi-agent model according to the benefit agents and the benefit index data;
a candidate address set obtaining module 502, configured to determine a candidate address set of a major infrastructure according to the multi-agent model;
a plurality of benefit change simulation result obtaining modules 503, configured to obtain a cellular automaton model, input the candidate address set into the cellular automaton model, and simulate a development trend of the benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results;
and a target site selection result obtaining module 504, configured to determine a target site selection result of the major infrastructure according to the benefit change simulation results.
Specifically, in the embodiment of the present invention, a multi-agent model building module 501 obtains a plurality of benefit agents and benefit index data of the benefit agents, and builds a multi-agent model according to the benefit agents and the benefit index data; then, determining a candidate site selection set of the major infrastructure according to the multi-agent model through a candidate site selection set acquisition module 502; then, a cellular automaton model is obtained through a plurality of benefit change simulation result obtaining modules 503, the candidate address set is input into the cellular automaton model, the development trend of the benefit index data is simulated based on the cellular automaton model to obtain a plurality of benefit change simulation results, and finally, the target address selection result of the major infrastructure is determined according to the benefit change simulation results through a target address selection result obtaining module 504. The system considers the competition and cooperation relationship of multiple beneficial agents in the major infrastructure construction, quantitatively describes the influence of the major infrastructure construction on the future development of the urban group, realizes the site selection optimization of the major infrastructure of the urban group, is beneficial to promoting the engineering benefit of the major infrastructure construction, and further promotes the sustainable development of economy, society and resources of the urban group.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a schematic block diagram thereof may be as shown in fig. 3. The intelligent terminal comprises a processor, a memory, a network interface, a display screen and a temperature sensor which are connected through a system bus. Wherein, the processor of the intelligent terminal is used for providing calculation and control capability. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the intelligent terminal is used for being connected and communicated with an external terminal through a network. The computer program is executed by a processor to implement a multi-stakeholder-based big infrastructure site selection optimization method. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen, and the temperature sensor of the intelligent terminal is arranged inside the intelligent terminal in advance and used for detecting the operating temperature of internal equipment.
It will be understood by those skilled in the art that the schematic diagram in fig. 3 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the intelligent terminal to which the solution of the present invention is applied, and a specific intelligent terminal may include more or less components than those shown in the figure, or combine some components, or have different arrangements of components.
In one embodiment, an intelligent terminal is provided that includes a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
acquiring a plurality of benefit main bodies and benefit index data of the benefit main bodies, and constructing a multi-agent model according to the benefit main bodies and the benefit index data; determining a candidate site set of a major infrastructure according to the multi-agent model; obtaining a cellular automaton model, inputting the candidate address set into the cellular automaton model, and simulating the development trend of the benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results; and determining a target site selection result of the major infrastructure according to the benefit change simulation results.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
In summary, the present invention discloses a method and a system for optimizing the location of a major infrastructure based on multiple benefit agents, wherein the method comprises: acquiring benefit index data of a plurality of benefit agents and benefit agents, and constructing a multi-agent model according to the benefit agents and the benefit index data; determining a candidate address set of the major infrastructure according to the multi-agent model; acquiring a cellular automaton model, inputting a candidate address set into the cellular automaton model, and simulating the development trend of benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results; and determining a target site selection result of the major infrastructure according to the benefit change simulation results. The invention considers the competition and cooperation relationship of multiple beneficial agents in the construction of the major infrastructure, quantitatively describes the influence of the major infrastructure construction on the future development of the urban group, realizes the site selection optimization of the major infrastructure of the urban group, is beneficial to promoting the construction engineering benefit of the major infrastructure, and further promotes the sustainable development of the economy, the society and the resources of the urban group.
Based on the above embodiments, the present invention discloses a method for optimizing a big infrastructure site based on multi-benefit agent, it should be understood that the application of the present invention is not limited to the above examples, and it will be obvious to those skilled in the art that the above modifications and changes can be made, and all such modifications and changes are within the scope of the appended claims.
Claims (10)
1. A multi-benefit agent based major infrastructure site selection optimization method is characterized by comprising the following steps:
acquiring a plurality of benefit main bodies and benefit index data of the benefit main bodies, and constructing a multi-agent model according to the benefit main bodies and the benefit index data;
determining a candidate site set of a major infrastructure according to the multi-agent model;
obtaining a cellular automaton model, inputting the candidate address set into the cellular automaton model, and simulating the development trend of the benefit index data based on the cellular automaton model to obtain a plurality of benefit change simulation results;
and determining a target site selection result of the major infrastructure according to the benefit change simulation results.
2. The multi-stakeholder-based big infrastructure site selection optimization method of claim 1, wherein the building a multi-agent model from the stakeholders and the stakeholder index data comprises:
constructing a plurality of single intelligent agent models according to the benefit main body and the benefit index data;
and constructing a multi-agent model according to a plurality of single-agent models.
3. The multi-stakeholder-based big infrastructure site selection optimization method of claim 2, wherein the constructing a number of single agent models from the stakeholders and the stakeholder index data comprises:
performing benefit analysis on the benefit subject and the benefit index data to obtain a benefit analysis result;
determining a utility value according to the benefit analysis result;
determining a space address selection set according to the utility value;
the benefit index data, the benefit analysis result, the utility value and the spatial addressing set form a plurality of single intelligent agent models.
4. The multi-stakeholder-based big infrastructure site selection optimization method of claim 3, wherein said determining a set of spatial sites from the utility value comprises:
determining the utility weight of each interest principal by an analytic hierarchy process according to the utility value;
and multiplying and accumulating the utility value and the utility weight to obtain a spatial address selection set.
5. The multi-stakeholder-based big-infrastructure site selection optimization method of claim 4, wherein the determining a candidate site set for big infrastructure according to the multi-agent model comprises:
making a decision, scheduling and coordinating the site selection tendency of the multi-agent model to obtain a first site selection set;
and on the basis of the first addressing set, making a decision, scheduling and coordinating addressing trends among the multi-agent models to obtain a candidate addressing set of major infrastructure.
6. The multi-benefit agent based major infrastructure site selection optimization method according to claim 5, wherein the obtaining of the cellular automaton model, the inputting of the candidate site selection set into the cellular automaton model, and the simulating of the development trend of the benefit index data based on the cellular automaton model to obtain the benefit change simulation results comprises:
obtaining a cellular automaton model, and inputting the candidate address set into the cellular automaton model; the cellular automaton model comprises cellular units, a cellular space and a neighborhood;
obtaining a single benefit index change rule;
when the candidate site set comprises the major infrastructure, performing iterative simulation on the benefit index data and the single benefit index change rule based on the cellular automaton model to obtain a first single benefit simulation result;
when the candidate site set does not contain the major infrastructure, performing iterative simulation on the benefit index data and the single benefit index change rule based on the cellular automaton model to obtain a second single benefit simulation result;
obtaining a single benefit variation difference value according to the first single benefit simulation result and the second single benefit simulation result, and obtaining a plurality of benefit variation simulation results according to the single benefit variation difference value.
7. The multi-benefit agent based big infrastructure siting optimization method of claim 6, wherein said determining a big infrastructure target siting result from the benefit change simulation results comprises:
and solving the optimal solution of the benefit change simulation results according to the balance analysis criteria of the benefit index data, and determining the target site selection result of the major infrastructure.
8. A multi-stakeholder-based big infrastructure site selection optimization system, the system comprising:
the multi-agent model building module is used for obtaining a plurality of benefit main bodies and benefit index data of the benefit main bodies and building a multi-agent model according to the benefit main bodies and the benefit index data;
a candidate site set obtaining module, configured to determine a candidate site set of a major infrastructure according to the multi-agent model;
the benefit change simulation result acquisition modules are used for acquiring a cellular automaton model, inputting the candidate address set into the cellular automaton model, and simulating the development trend of the benefit index data based on the cellular automaton model to obtain benefit change simulation results;
and the target site selection result acquisition module is used for determining a target site selection result of the major infrastructure according to the benefit change simulation results.
9. An intelligent terminal comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory, and wherein the one or more programs being configured to be executed by the one or more processors comprises instructions for performing the method of any of claims 1-7.
10. A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of any of claims 1-7.
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