CN112862176B - Public service facility site selection method and device - Google Patents
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Abstract
The invention provides a public service facility site selection method and device, and relates to the technical field of facility site selection. The public service facility site selection method comprises the steps of obtaining basic data and screening conditions, screening site selection points to be selected according to the screening conditions, carrying out binary coding on the site selection points to be selected, and solving an optimal site selection point by using a multi-target particle swarm algorithm. The multi-target particle swarm algorithm is an iterative-based swarm intelligence algorithm, finds the optimal solution through cooperation and information sharing among individuals in a swarm, and has the characteristics of high search efficiency, good universality, high convergence speed, strong adaptability and the like. The optimal solution can be quickly obtained after the addressing points to be selected are solved by the multi-target particle swarm optimization, so that the time of the facility addressing process is reduced.
Description
Technical Field
The invention relates to the technical field of facility site selection, in particular to a method and a device for site selection of public service facilities.
Background
The site selection problem is widely applied to public service facilities, emergency facilities, industrial plants and communication base stations, such as site selection of factories, warehouses and garbage treatment centers. Site selection is one of the most important long-term decisions, and the quality of site selection directly affects the service mode, the service quality, the service efficiency, the service cost and the like, thereby affecting the profit and the market competitiveness and even determining the destiny of an enterprise. Good site selection brings convenience to the life of people, reduces cost, enlarges profit and market share, improves service efficiency and competitiveness, and poor site selection often brings great inconvenience and loss, even disasters, so the research on the site selection problem has great economic, social and military significance.
The site selection problem is a multi-objective optimization problem, and a commonly used intelligent evolution algorithm is a simulated annealing algorithm in solving the multi-objective optimization problem. When the simulated annealing algorithm is adopted, a large amount of solution time is needed under the complex conditions of more variables, complex objective functions and the like, so that a large amount of time is consumed in the whole site selection process.
Disclosure of Invention
The invention aims to provide a method and a device for site selection of public service facilities, which are used for solving the problem that a large amount of time is consumed in the site selection process of the facilities in the prior art.
In a first aspect, an embodiment of the present application provides a method for addressing a public service facility, including the following steps:
acquiring basic data and address selection conditions; the basic data comprises data of an addressing unit;
screening the data of the addressing units according to the addressing conditions to screen the addressing units meeting the addressing conditions as addressing points to be selected;
coding and arranging the to-be-selected address points to obtain coded address points;
and solving the coded site selection points by a multi-target particle swarm algorithm to obtain optimal site selection points.
In the implementation process, basic data and screening conditions are obtained, the to-be-selected site points meeting the conditions are screened out according to the screening conditions, and then the to-be-selected site points are solved through a multi-target particle swarm algorithm, wherein the multi-target particle swarm algorithm is an iterative-based swarm intelligence algorithm, and in the multi-target particle swarm algorithm, particles search the optimal points to share information currently, so that the method is a single information sharing mechanism to a great extent, and the whole searching and updating process follows the current optimal solution, so that the optimal site points can be obtained quickly. The algorithm has high searching efficiency, good universality, higher convergence rate and strong adaptability, and the site selection points to be selected can quickly obtain the optimal solution after being processed by the multi-target particle swarm algorithm, thereby reducing the time of the facility site selection process.
In some embodiments of the present invention, after the steps of the basic data and the address selecting condition, before the step of screening the data of the address selecting unit according to the address selecting condition to screen the address selecting unit meeting the address selecting condition as the address selecting point to be selected, the method further includes the following steps:
determining the distance from each residential point to the addressing unit according to the data of the addressing unit and the residential point POI data;
determining the distance from the public service facility to the addressing unit according to the data of the addressing unit and the POI data of the public service facility;
and determining the area of the addressing unit according to the data of the addressing unit.
In some embodiments of the invention, the addressing unit is a control map block.
In the implementation process, each control gauge land block has a serial number, and the solving space is converted from two dimensions to one dimension through binary coding of the control gauge land blocks, so that the calculation speed is increased.
In some embodiments of the present invention, the site selection condition includes a preset site selection facility type, a facility scale, and a right of way property; the step of screening the data of the addressing unit according to the addressing condition to screen the addressing unit meeting the addressing condition as the addressing point to be selected comprises the following steps:
screening out the site selection units conforming to the land use property according to the land use property as site selection points to be selected;
screening an unestablished addressing unit which accords with the facility type according to the facility type to serve as an addressing point to be selected;
and screening out site selection units which accord with the facility scale according to the facility scale as site selection points to be selected.
In some embodiments of the present invention, the step of solving the encoded addressing points by a multi-objective particle swarm algorithm and obtaining the optimal addressing points comprises the following steps:
initializing the position and the speed of particles in the particle swarm; the particles in the particle swarm are coded address points;
constructing a fitness function, and calculating the fitness of the particles according to the fitness function;
acquiring an individual optimal value and an overall particle optimal value of the particle according to the fitness, and updating the speed and the position of the particle according to the individual optimal value and the overall particle optimal value;
obtaining a new particle swarm by updating the speed and the position of the particles;
judging whether the fitness calculation meets a preset convergence condition or not;
under the condition that the convergence condition is not met, constructing a fitness function, and calculating the fitness of the particles according to the fitness function;
and outputting the optimal value of the whole particles as an optimal site point when the convergence condition is met.
In the implementation process, after the fitness calculation is carried out on the particles, the individual optimal value and the overall particle optimal value of the particles are obtained, and the speed and the position of the particles are updated according to the individual optimal value and the overall particle optimal value, so that all the particles can be converged to the optimal solution at a higher speed, and the calculation efficiency is improved. The particles are updated only by the internal velocity, so the principle is simpler, the parameters are fewer, and the realization is easier.
In some embodiments of the invention, said step of initializing the position and velocity of particles in the population of particles comprises the steps of:
representing an initial position of each particle in the population of particles in a set of binary encodings;
the initial velocity of each particle in the population of particles is represented as a random floating point number.
In some embodiments of the invention, the fitness function comprises:
maximizing a quality of service function
Wherein, w i Number of population of residential points i, Z ij The proportion of the demand assigned to the public service facility j for the residential point i, <' >>
q ij Quality of service for public service facility j; />
Wherein D1 is the theoretical service radius of the public service facility, and D2 is the maximum service radius of the public service facility; d ij From a resident i to a facility jA distance;
minimized travel cost function
Wherein is the residential point d ik The distance between the residential site i and its nearest public service facility k.
In the implementation process, the service quality q of the public service facility ij The service radius is used for calculating the maximum service quality and the minimum travel cost by adopting a progressive coverage model, and in the actual calculation, even if the service radius exceeds the radius, the service radius is not directly reduced to 0, but is a slowly-reduced process, so that the maximum service quality and the minimum travel cost function are designed aiming at the situation, the model is closer to the reality, and the calculation result is more accurate
In a second aspect, an embodiment of the present application provides a public service facility address selecting device, where the device includes:
the basic data and address selection condition acquisition module is used for acquiring basic data and address selection conditions; the basic data comprises data of an addressing unit;
the to-be-selected addressing point screening module is used for screening the data of the addressing units according to the addressing conditions so as to screen the addressing units meeting the addressing conditions as to-be-selected addressing points;
the address selection point coding module is used for coding and arranging the address selection points to be selected to obtain coded address selection points;
and the multi-target particle swarm algorithm module is used for solving the coded addressing points through a multi-target particle swarm algorithm and obtaining the optimal addressing points.
In some embodiments of the invention, the base data further includes residential POI data and public service POI data, and the public service locating apparatus further includes:
the first distance determining module is used for determining the distance from each residential point to the addressing unit according to the data of the addressing unit and the residential point POI data;
the second distance determining module is used for determining the distance from the public service facility to the addressing unit according to the data of the addressing unit and the POI data of the public service facility;
and the area determining module is used for determining the area of the addressing unit according to the data of the addressing unit.
In some embodiments of the invention, the addressing unit is a control map block.
In some embodiments of the present invention, the site selection condition includes a preset site selection facility type, a facility scale, and a right of way property; the to-be-selected site selection screening module comprises:
the first to-be-selected addressing point screening unit is used for screening the addressing unit which accords with the land property according to the land property as to-be-selected addressing points;
the second to-be-selected site selection point screening unit is used for screening the non-built site selection units which accord with the facility types according to the facility types as to-be-selected site selection points;
and the third to-be-selected site selection point screening unit is used for screening the site selection unit which accords with the facility scale according to the facility scale as the to-be-selected site selection point.
In some embodiments of the present invention, the multi-target particle swarm algorithm module comprises:
an initialization unit for initializing the position and velocity of particles in the particle swarm; the particles in the particle swarm are coded site selection points;
the fitness calculation unit is used for constructing a fitness function and calculating the fitness of the particles according to the fitness function;
the speed and position updating unit is used for acquiring the individual optimal value and the overall particle optimal value of the particle according to the fitness and updating the speed and the position of the particle according to the individual optimal value and the overall particle optimal value;
the particle swarm updating unit is used for obtaining a new particle swarm by updating the speed and the position of the particles;
the judging unit is used for judging whether the fitness calculation meets a preset convergence condition or not;
a returning unit, configured to construct a fitness function if the convergence condition is not satisfied, and calculate the fitness of the particle according to the fitness function;
and the optimal selection site determining unit is used for outputting the optimal value of all the particles as the optimal selection site under the condition of meeting the convergence condition.
In some embodiments of the invention, the initialization unit comprises:
an initial position subunit for representing an initial position of each particle in the population of particles in a set of binary encodings;
an initial velocity subunit configured to represent an initial velocity of each particle in the particle population as a random floating point number.
In some embodiments of the invention, the fitness function comprises:
maximizing a quality of service function
Wherein, w i Number of population of residential points i, Z ij The proportion of the demand assigned to the public service facility j for the residential point i, <' >>
q ij Quality of service for public service facility j;
wherein D1 is the theoretical service radius of the public service facility, and D2 is the maximum service radius of the public service facility; d ij Distance from the residential point i to the facility j;
minimized travel cost function
Wherein is the residential point d ik The distance between a residential point i and its nearest public service facility k.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory for storing one or more programs; a processor. The one or more programs, when executed by the processor, implement the method of any of the first aspects above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method according to any one of the above first aspects.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a method for addressing a public service facility according to an embodiment of the present invention;
fig. 2 is a block diagram of a public service facility addressing apparatus according to an embodiment of the present invention;
FIG. 3 is a detailed flowchart of the location selection of a public service facility by a control and regulation plot according to an embodiment of the present invention;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 100-public service facility addressing means; 110-basic data and address selection condition acquisition module; 120-an address selection point screening module to be selected; 130-addressing point coding module; 140-a multi-target particle swarm algorithm module; 101-a memory; 102-a processor; 103-communication interface.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Referring to fig. 1, fig. 1 is a flowchart of a location selection method for a public service facility according to an embodiment of the present invention. The public service facility site selection method comprises the following steps:
step S110: acquiring basic data and address selection conditions; the basic data includes data of an address unit; further, the basic data also includes residential POI data and public service POI data. The residential point POI data and the public service facility POI data may be acquired through an API interface of the map.
According to the data of the addressing unit and the data of the residential points POI, by means of GIS software processing, the distance d from the residential point i to the addressing unit j can be determined ij ；
According to the data of the addressing unit and the POI data of the public service facility, the distance d from the public service facility k to the addressing unit j can be determined by means of GIS software processing kj ；
The area of the addressing unit j can be determined by means of GIS software processing according to the data of the addressing unit.
Step S120: screening the data of the addressing units according to the addressing conditions to screen the addressing units meeting the addressing conditions as addressing points to be selected; the site selection condition comprises the preset site selection facility type, the facility scale and the land use property;
the types of the site selection facilities comprise residential sites, residential sites and the like, and site selection units with the same property as the input sites can be screened out according to the property of the sites to be used as site selection points to be selected;
the facility type input by the user corresponds to the land property, the service radius of the facility can be determined according to the facility type, and the addressing unit which accords with the facility type is screened out according to the service radius of the facility to be used as the addressing point to be selected;
the land area can be determined according to the facility scale, so that the site selection unit which accords with the facility scale can be screened out according to the land area to serve as a site selection point to be selected.
Step S130: and coding and arranging the address points to be selected to obtain coded address points.
For example, the numbers of the address points to be selected which meet the requirement are 0-5-1, 0-4-9, 0-7-8, 1-2-3 and 1-4-2 respectively, the positions of the five plots can be represented by 0,1,2,3,4 respectively, wherein the 0 th bit represents 0-5-1, and so on.
Step S140: and solving the coded site selection points through a multi-target particle swarm algorithm, and obtaining the optimal site selection points.
In the implementation process, basic data and screening conditions are obtained, the to-be-selected address points meeting the conditions are screened according to the screening conditions, and then the to-be-selected address points are solved through a multi-target particle swarm algorithm, wherein the multi-target particle swarm algorithm is an iterative-based swarm intelligence algorithm, and in the multi-target particle swarm algorithm, the particles share information through currently searching the optimal points, so that the single information sharing mechanism is realized to a great extent, the whole searching and updating process is a process following the current optimal solution, and therefore the optimal address points can be obtained quickly. The algorithm has high searching efficiency, good universality, higher convergence rate and strong adaptability, and the site selection points to be selected can quickly obtain the optimal solution after being processed by the multi-target particle swarm algorithm, thereby reducing the time of the facility site selection process.
Wherein, the address selection unit is a control gauge land block. Referring to fig. 3, fig. 3 is a detailed flowchart of address selection of public service facilities by a control and regulation site block according to an embodiment of the present invention. The addressing unit may be a grid unit, and the target research area is subjected to a gridding process to obtain a grid unit. The addressing unit can also be a control gauge block, and each control gauge block has a number.
In the implementation process, each control gauge land block has a serial number, and the solving space is converted from two dimensions to one dimension through binary coding of the control gauge land blocks, so that the calculation speed is increased.
The method comprises the following steps of screening according to the addressing conditions and the data of the addressing units to screen the addressing units meeting the addressing conditions as the addressing points to be selected.
Firstly, screening out site selection units conforming to the property of the land to serve as site selection points to be selected according to the property of the land;
for example, a primary school needs to be addressed, and the primary school can be configured with three types of land for educational research and development, primary and secondary school land and primary and secondary school kindergarten land. The input land property is the education and scientific research land, the selected land is the education and scientific research land, and other land properties such as the land for primary and secondary schools and the land for primary and secondary school kindergartens are excluded.
For example, the acquired site selection units comprise a residential land plot A, a residential land plot B and an industrial land plot C, the nature of the input land is residential land, and the site selection units with the nature of the residential land A and B are screened according to the condition; and screening the addressing units with the property different from the input land property according to the land property as the addressing points to be selected.
Secondly, screening an unestablished addressing unit which accords with the facility type according to the facility type to serve as an addressing point to be selected; used for removing land parcels within the influence range of the constructed construction. If the facility type is a cultural activity center, excluding the land parcel with the cultural activity center; if the facility type is primary school, the plot with primary school is excluded.
For example, a primary school needs to be addressed, the addressing unit screened to meet the conditions by the land property comprises an addressing unit A and an addressing unit B, at the moment, a built primary school exists, the service radius of the primary school is 1000 meters, the distance from the addressing unit A to the built primary school is 800 meters, the distance from the addressing unit B to the built primary school is 1200 meters, and then the addressing unit B meets the requirements after screening and serves as an addressing point to be selected.
And thirdly, screening out site selection units which accord with the facility scale according to the facility scale as site selection points to be selected. The land area can be determined according to the facility scale, for example, the minimum land area of the primary school to be configured is 1200 square meters, and then the selected address unit with the area larger than 1200 meters is selected as the selected address point.
Specifically, during screening, n addressing units meeting the requirements can be screened out through the frame selection research area according to addressing conditions; if not, adjusting the input condition and re-selecting the frames.
The method comprises the following steps of solving the coded site selection points through a multi-target particle swarm algorithm, and obtaining the optimal site selection points:
initializing the position and the speed of particles in the particle swarm; the particles in the particle swarm are coded address points; wherein the step of initializing the position and velocity of particles in the population of particles comprises the steps of:
representing an initial position of each particle in the population of particles in a set of binary encodings;
the initial velocity of each particle in the population of particles is represented as a random floating point number.
For example, if the required plot numbers are 0-5-1, 0-4-9, 0-7-8, 1-2-3, and 1-4-2, respectively, then 0,1,2,3,4 is used to represent these five plots, respectively, where 0 represents 0-5-1, and the initial position of each particle in the particle group is represented by a set of binary codes, such as 01000, where the position of 1 corresponds to the plot number at the same position, i.e., the particle is located at 0-4-9 plot at this time, and a particle group of size 400 has 400 codes of 01000, where the position of 1 is random. The initial velocity of each particle in the population is represented by a random floating point number between [ -1,1 ]. Meanwhile, the number of iterations is set to 200.
Constructing a fitness function, and calculating the fitness of the particles according to the fitness function; in calculation, the order of pareto frontier of each individual is calculated through non-dominated sorting (based on the decap library of python), and is taken as the fitness.
Acquiring the individual optimal value and the overall particle optimal value of the particle according to the fitness, and updating the speed and the position of the particle according to the individual optimal value and the overall particle optimal value;
setting the position of the individual optimal particle as an individual extreme value
Defining the position of the totality of the most optimal particles as the global extremum->
The update of the velocity and position of the particles is performed according to the following formula:
x i (k+1)＝x i (k)+v i (k+1)
wherein v is i (k) Represents the velocity, x, of the ith particle in the kth generation of particle i (k) Representing the position of the ith particle in the kth generation of particle swarm, w is the inertia weight, k is the current iteration number, c 1 、c 2 For learning factor, xi and eta are uniformly distributed in [0, 1']Random numbers within the interval.
Obtaining a new particle swarm by updating the speed and the position of the particles;
judging whether the fitness calculation meets a preset convergence condition or not;
under the condition that the convergence condition is not met, constructing a fitness function, and calculating the fitness of the particles according to the fitness function; the iteration continues. For example, the preset convergence condition is that the fitness is calculated 400 times, when the execution is performed to 300 times, and the convergence condition is not met, the execution is continued to construct the fitness function, and the fitness of the particle is calculated according to the fitness function.
And under the condition that the convergence condition is met, outputting the optimal value of the whole particles as an optimal site point. Firstly, outputting the binary position code of the optimal address point, and decoding the binary position code into the corresponding land block number through a coding rule. For example, after the execution is completed 400 times, if the convergence condition is satisfied, the binary code of the next best address point is 000001000000, and the corresponding address point is the address point of the position number corresponding to 1.
In the implementation process, after the fitness calculation is carried out on the particles, the individual optimal value and the overall particle optimal value of the particles are obtained, and the speed and the position of the particles are updated according to the individual optimal value and the overall particle optimal value, so that all the particles can be converged in an optimal solution at a higher speed, and the calculation efficiency is improved. The particles are updated only by the internal velocity, so the principle is simpler, the parameters are fewer, and the realization is easier.
Wherein the fitness function comprises:
maximizing a quality of service function
Wherein w i Number of population of residential points i, Z ij The proportion of the demand assigned to the public service facility j for the residential point i, <' >>
q ij Quality of service for public service facility j;
wherein D1 is the theoretical service radius of the public service facility, and D2 is the maximum service radius of the public service facility; d ij Distance from the residential point i to the facility j;
minimized travel cost function
Wherein is the residential point d ik The distance between a residential point i and its nearest public service facility k. />
In the implementation process, the service quality q of the public service facility ij The progressive coverage model is adopted, the service radius is used for calculating the maximum service quality and the minimum travel cost, and in the actual calculation, even if the radius is exceeded,and the value is not directly reduced to 0, but is reduced slowly, so the functions of maximizing the service quality and minimizing the travel cost are designed for the situation, the model is more practical, and the calculation result is more accurate.
Based on the same inventive concept, the present invention further provides a public service facility addressing device 100, please refer to fig. 2, and fig. 2 is a block diagram of a structure of the public service facility addressing device 100 according to an embodiment of the present invention. The device comprises:
a basic data and address selecting condition obtaining module 110, configured to obtain basic data and address selecting conditions; the basic data includes data of an address unit;
the to-be-selected addressing point screening module 120 is configured to screen data of the addressing units according to the addressing conditions to screen addressing units meeting the addressing conditions as to-be-selected addressing points;
an address selection point coding module 130, configured to code and arrange address selection points to be selected to obtain coded address selection points;
and the multi-target particle swarm algorithm module 140 is used for solving the coded addressing points through a multi-target particle swarm algorithm and obtaining optimal addressing points.
Wherein the basic data further includes residential point POI data and public service facility POI data, and the public service facility addressing apparatus 100 further includes:
the first distance determining module is used for determining the distance from each residential point to the addressing unit according to the data of the addressing unit and the residential point POI data;
the second distance determining module is used for determining the distance from the public service facility to the addressing unit according to the data of the addressing unit and the POI data of the public service facility;
and the area determining module is used for determining the area of the addressing unit according to the data of the addressing unit.
Wherein, the address selection unit is a control land block.
The site selection condition comprises a preset site selection facility type, a facility scale and a site use property; the to-be-selected site selection screening module 120 includes:
the first to-be-selected site selection point screening unit is used for screening the site selection unit which accords with the property of the land to serve as the to-be-selected site selection point according to the property of the land;
the second to-be-selected addressing point screening unit is used for screening the non-built addressing units which accord with the facility types according to the facility types as to-be-selected addressing points;
and the third to-be-selected site selection point screening unit is used for screening the site selection units which accord with the facility scale according to the facility scale as to-be-selected site selection points.
The multi-target particle swarm algorithm module 140 includes:
an initialization unit for initializing the position and velocity of particles in the particle swarm; the particles in the particle swarm are coded site selection points;
the fitness calculation unit is used for constructing a fitness function and calculating the fitness of the particles according to the fitness function;
the speed and position updating unit is used for acquiring the individual optimal value and the overall particle optimal value of the particle according to the fitness and updating the speed and the position of the particle according to the individual optimal value and the overall particle optimal value;
the particle swarm updating unit is used for obtaining a new particle swarm by updating the speed and the position of the particles;
the judging unit is used for judging whether the fitness calculation meets the preset convergence condition or not;
the return unit is used for constructing a fitness function under the condition that the convergence condition is not met, and calculating the fitness of the particles according to the fitness function;
and the optimal selection point determining unit is used for outputting the optimal value of all the particles as the optimal selection point under the condition that the convergence condition is met.
Wherein, the initialization unit includes:
an initial position subunit for representing an initial position of each particle in the population of particles in a set of binary encodings;
an initial velocity subunit for representing an initial velocity of each particle in the particle population as a random floating point number.
Wherein the fitness function comprises:
maximizing a quality of service function
Wherein w i Number of population of residential points i, Z ij A proportion of the demand assigned to a public service facility j for a resident i>
q ij Quality of service for public service facility j;
wherein D1 is the theoretical service radius of the public service facility, and D2 is the maximum service radius of the public service facility; d is a radical of ij Distance from the residential point i to the facility j;
minimized travel cost function
Wherein is the residential point d ik The distance between the residential site i and its nearest public service facility k.
Referring to fig. 4, fig. 4 is a schematic structural block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device comprises a memory 101, a processor 102 and a communication interface 103, wherein the memory 101, the processor 102 and the communication interface 103 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used for storing software programs and modules, such as program instructions/modules corresponding to the public service facility addressing device 100 provided by the embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 101, so as to execute various functional applications and data processing. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The processor 102 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It will be appreciated that the configuration shown in fig. 4 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 4 or have a different configuration than shown in fig. 4. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
In summary, the method and the device for locating the public service facility provided by the embodiment of the application are characterized in that basic data and screening conditions are obtained, the to-be-selected locating points meeting the conditions are screened out according to the screening conditions, and then the to-be-selected locating points are solved through a multi-target particle swarm algorithm, wherein the multi-target particle swarm algorithm is an iteration-based swarm intelligence algorithm, in the multi-target particle swarm algorithm, the particles search the optimal points to share information, so that the single information sharing mechanism is realized to a great extent, and the whole searching and updating process is followed by the current optimal solution, so that the optimal locating points can be obtained quickly. The algorithm has high searching efficiency, good universality, higher convergence rate and strong adaptability, and the site selection points to be selected can quickly obtain the optimal solution after being processed by the multi-target particle swarm algorithm, thereby reducing the time of the facility site selection process.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (5)
1. A public service facility site selection method is characterized by comprising the following steps:
acquiring basic data and address selection conditions; the basic data comprises data of an addressing unit, POI data of residential points and POI data of public service facilities; the address selection unit is a control gauge land block; the site selection condition comprises a preset site selection facility type, a facility scale and a site property;
determining the distance from a residential point to the addressing unit according to the data of the addressing unit and the data of the residential point POI; determining the distance from the public service facility to the addressing unit according to the data of the addressing unit and the POI data of the public service facility; determining the area of the addressing unit according to the data of the addressing unit;
screening the data of the addressing units according to the addressing conditions to screen the addressing units meeting the addressing conditions as addressing points to be selected, wherein the method comprises the following steps: screening out the site selection unit which accords with the land use property according to the land use property as a site selection point to be selected; screening out an unestablished addressing unit which accords with the facility type according to the facility type as an addressing point to be selected; screening out site selection units which accord with the facility scale according to the facility scale as site selection points to be selected;
coding and arranging the to-be-selected address points to obtain coded address points;
solving the coded addressing points through a multi-target particle swarm algorithm, and obtaining optimal addressing points; the method comprises the following steps: initializing the position and the speed of particles in the particle swarm; the particles in the particle swarm are coded address points; constructing a fitness function, and calculating the fitness of the particles according to the fitness function; acquiring an individual optimal value and an overall particle optimal value of the particle according to the fitness, and updating the speed and the position of the particle according to the individual optimal value and the overall particle optimal value; obtaining a new particle swarm by updating the speed and the position of the particles; judging whether the fitness calculation meets a preset convergence condition or not; under the condition that the convergence condition is not met, constructing a fitness function, and calculating the fitness of the particles according to the fitness function; under the condition that the convergence condition is met, outputting the optimal value of all the particles as an optimal site selection point;
the fitness function includes:
maximizing a quality of service function
Wherein, w i Number of population of residential points i, Z ij The proportion of the demand assigned to the public service facility j for the residential point i, <' >>
q ij For the quality of service of the public service facility j,
wherein D1 is the theoretical service radius of the public service facility, D2Is the maximum service radius of the public service facility, d ij Distance from residential point i to facility j;
minimized travel cost function
Wherein is the residential point d ik The distance between a residential point i and its nearest public service facility k.
2. A utility siting method according to claim 1, characterized in that said step of initializing the position and the speed of particles in a particle swarm comprises the steps of:
representing an initial position of each particle in the population of particles in a set of binary encodings;
the initial velocity of each particle in the population of particles is represented as a random floating point number.
3. A public service facility addressing apparatus, the apparatus comprising:
the basic data and address selection condition acquisition module is used for acquiring basic data and address selection conditions; the basic data comprises data of an addressing unit, POI data of residential points and POI data of public service facilities; the address selection unit is a control gauge land block; the site selection condition comprises a preset site selection facility type, a facility scale and a site property; the system is also used for determining the distance from a residential point to the addressing unit according to the data of the addressing unit and the data of the residential point POI; determining the distance from the public service facility to the addressing unit according to the data of the addressing unit and the POI data of the public service facility; determining the area of the addressing unit according to the data of the addressing unit;
the to-be-selected addressing point screening module is used for screening the data of the addressing units according to the addressing conditions so as to screen the addressing units meeting the addressing conditions as to-be-selected addressing points, and comprises the following steps: screening out the site selection unit which accords with the land use property according to the land use property as a site selection point to be selected; screening an unestablished addressing unit which accords with the facility type according to the facility type to serve as an addressing point to be selected; screening out site selection units which accord with the facility scale according to the facility scale as site selection points to be selected;
the address selection point coding module is used for coding and arranging the address selection points to be selected to obtain coded address selection points;
the multi-target particle swarm algorithm module is used for solving the coded addressing points through a multi-target particle swarm algorithm and obtaining optimal addressing points; the method comprises the following steps: initializing the position and the speed of particles in the particle swarm; the particles in the particle swarm are coded site selection points; constructing a fitness function, and calculating the fitness of the particles according to the fitness function; acquiring an individual optimal value and an overall particle optimal value of the particle according to the fitness, and updating the speed and the position of the particle according to the individual optimal value and the overall particle optimal value; obtaining a new particle swarm by updating the speed and the position of the particle; judging whether the fitness calculation meets a preset convergence condition or not; under the condition that the convergence condition is not met, constructing a fitness function, and calculating the fitness of the particles according to the fitness function; under the condition that the convergence condition is met, outputting the optimal value of all the particles as an optimal site selection point;
the fitness function includes:
maximizing a quality of service function
Wherein, w i Is the population number of the residential spot i, Z ij A proportion of the demand assigned to a public service facility j for a resident i>
q ij For the quality of service of the public service facility j,
wherein D1 is the theoretical service radius of the public service facility, D2 is the maximum service radius of the public service facility, and D ij Distance from residential point i to facility j;
minimized travel cost function
Wherein is the residential point d ik The distance between the residential site i and its nearest public service facility k.
4. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the one or more programs, when executed by the processor, implement the method of any of claims 1-2.
5. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-2.
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| CN114004417A (en) * | 2021-11-09 | 2022-02-01 | 广东电网有限责任公司江门供电局 | Method and device for building power facility shared base station |
| CN114399520A (en) * | 2021-12-03 | 2022-04-26 | 上海建工四建集团有限公司 | Intelligent optimization method for rural garbage can layout |
| CN115578131B (en) * | 2022-12-07 | 2023-08-25 | 深圳市城市规划设计研究院股份有限公司 | Site selection method and device for cross-city target facilities, electronic equipment and storage medium |
| CN117252745B (en) * | 2023-11-20 | 2024-03-12 | 河北省交通规划设计研究院有限公司 | Public service facility site selection method and device and computer equipment |
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