CN107945857B - Community medical site deployment method based on data fusion - Google Patents
Community medical site deployment method based on data fusion Download PDFInfo
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Abstract
The invention discloses a community medical site deployment method based on data fusion. The method sets a mark point every 1 kilometer by 1 kilometer in the urban map to form a mark point matrix network. Modifying the demand weight of each candidate point according to a specific algorithm, and finally obtaining a mark point matrix network with the demand weight, wherein the mark point matrix network is called a demand map; finding out all maximum value points in the demand map, deploying a station at the maximum value point with the minimum weight, clearing the weight of the mark point of the service area of the station to 0, recalculating all the maximum value points in the demand map, and finding out the maximum value point deployment station with the minimum weight; by adopting the iterative method, the mark point weight of the whole demand map is finally returned to 0. The invention can effectively balance: the number of the stations, the load balance of the stations and the average moving distance of the patient greatly improve the operating efficiency of the urban medical system.
Description
Technical Field
The invention relates to a community medical site deployment method based on data fusion.
Background
Community medical treatment is a development trend of future medical services, and has various functions of saving the patient visit time and saving social medical treatment and traffic resources.
The deployment of community medical sites in cities is typically deployed by street or by several large cells. The population is the only basis for traditional community healthcare site deployment.
However, with the development of urbanization, not only the population of each area tends to be unbalanced, but also the following factors affect the deployment effect of the community medical site.
1. Population structure. Areas of the same population, which are heavily aged, require more medical sites than other areas. Areas with a high proportion of babies, children and women of child bearing age require more medical sites than other areas.
2. Distribution of high-grade hospitals. In areas with high-grade hospitals, the demand of people on community medical institutions is reduced, and the demand on community medical sites is greater in areas farther away from the high-grade hospitals.
The information of the two aspects comes from data of different sources, different types and different objects, and the optimized medical site deployment scheme can be obtained by processing and fusing the data of the two aspects.
In summary, the method of the present invention performs community healthcare site deployment in an attempt to achieve the following goals:
the number of the sites is as small as possible, and the utilization rate of a single site is high. Medical sites need to occupy a large amount of resources such as land, manpower and drug inventory, and the main goal is to improve the utilization rate of a single site and reduce the number of the medical sites as much as possible under the condition of the same service experience;
and balancing the load of each site. The situation that some sites are full of patients and some sites are not treatable by patients is avoided. The average waiting time of the patient is used in the invention to measure the load of one station;
less average arrival time. Average time of arrival of a population at a medical site
Disclosure of Invention
The two key points of the invention are: 1. how to fuse three different attributes of data, demographic and high-level hospital data, into location-related quantified medical needs; 2. how to select a deployment position to cover the requirements according to the position-related medical requirement data obtained after fusion.
The basic idea of data fusion is: and setting a mark point every 1 kilometer by 1 kilometer in the city map to form a mark point matrix network. And modifying the demand weight of each candidate point according to a specific algorithm by using the two types of data respectively, and finally obtaining a mark point matrix network with the demand weight, wherein the mark point matrix network is called a demand map. Since a person's medical needs can be satisfied by sites at different distances, the closer the site is, the better, so a person's needs will be distributed to the vicinity centered on his location, and likewise, the medical needs satisfied by a superior medical institution will be distributed to the vicinity centered on his location.
The basic idea of coverage is: finding out all maximum points (the demand weight is greater than the mark points of all adjacent points) in the demand map, deploying a station at the maximum point with the minimum weight, then clearing 0 the mark point weight of the service area of the station, then recalculating all the maximum points of the demand map, and then finding out the maximum point deployment station with the minimum weight. The present invention employs square area coverage because of the problem of overlap of circular coverage.
And establishing a connecting line between each maximum value point and the maximum value point nearest to the maximum value point, wherein the connecting line is called an edge. The maxima points and edges form a graph G (V, E), where V is the set of maxima points and E is the set of edges. Sites are deployed on the edge of graph G using greedy to cover the demand.
Table 1: demographic structure raw data table PTB
Table 2: superior medical institution raw data table HTB
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
parameter definition:
max _ diff: represents the greater of diff _ j and diff _ i;
level _ weight: representing the total weight to be distributed of the layer where the current mark point [ i, j ] is located;
level _ pcount: indicating how many mark points are on the layer where the current mark point [ i, j ] is;
pr: information representing current people in the original data table of the population structure;
PTB: a table of raw data representing a population structure;
age is: indicates the age of the person currently being treated;
hr: indicating the currently processed superior medical institution information;
count: number of outpatients of the superior medical institution hr;
step 1: weight distribution dist (weight, c _ i, c _ j)
Distributing half weight value weight to mark points [ c _ i, c _ j ], distributing one quarter weight value to 8 mark points adjacent to the mark points [ c _ i, c _ j ], distributing one eighth weight value to 16 mark points outside the 8 mark points, and so on. When the weight value which is not distributed is less than 1% of the initial weight, the weight value is directly discarded. The concrete implementation is as follows:
step 1-1. initializing i ═ 1;
1-2, if i is greater than n, jumping to the step 1-13;
step 1-3. initialize j-1
1-4, if j is larger than m, jumping to the step 1-12;
step 1-5.diff _ i ═ abs (i-c _ i) and diff _ j ═ abs (j-c _ j), where abs (x) denotes the absolute value of x;
step 1-6.max _ diff ═ max (diff _ i, diff _ j), where max (x, y) denotes taking the larger of x and y;
1-7. if max _ diff >5, jump to step 1-11.// the point [ i, j ] is outside the 5 th layer centered at [ c _ i, c _ j ], the remaining total weight to be distributed is less than 1% of the initial value, so no processing is required
Step 1-8.level _ weight ═ weight [ ((0.5) ^ (max _ diff + 1)); // the weights to be distributed for that layer
Step 1-9.level _ pcount ═ max _ diff +1 ^2- (max _ diff) ^2, the number of mark points of this layer;
and 1-10, if the calculated map [ i, j ] is less than 0, reassigning the map [ i, j ] to be 0.
Step 1-11.j is j +1, and skipping to step 1-4;
step 1-12.i + +, skipping to step 1-2;
and 1-13, finishing.
Step 2, fusing the human mouth data and the hospital data to obtain a medical requirement map, which comprises the following specific steps:
step 2-1, initializing a medical requirement map [ n, m ] to show that the city map is covered by a matrix with the length of n and the width of m mark points, wherein the map [ i, j ] represents the requirement weight of the mark points [ i, j ], i is more than or equal to 1 and less than or equal to n, and j is more than or equal to 1 and less than or equal to m; the distance between [ i, j ] and the index point represented by [ i +1, j ] is 1 km, and the distance between [ i, j ] and the index point represented by [ i, j +1] is 1 km. The boundary of the area covered by the mark point matrix exceeds 5 kilometers of the urban marginal cell or hospital.
Step 2-2, initializing h to 1;
step 2-3. extract the row of the h-th person in the demographic structure raw data table (i.e., table 1), pr ═ PTB [ h ].
Step 2-4. initialize weight 1
Step 2-5. if pr.age <12 or pr.age >60, weight is 5;
step 2-6. if 22< pr.age <40 and pr.sex equals "woman", then weight is 5;
step 2-7, calling a weight distribution algorithm to distribute the weight to the mark points near the cell where the h-th personal member is located, namely dist (map, weight, pr.i, pr.j)
Step 2-8.h ═ h +1, then jump to step 2-3.
Step 2-9, initializing h to 1;
and 2-10, taking the line of the h-th medical institution in the original data table (namely the table 2) of the superior medical institution, wherein hr is HTB [ h ].
Step 2-11. initialize weight 0-hr
Step 2-12, calling a weight distribution algorithm to distribute the weight to the mark points near the h hospital, namely dist (map, weight, hr.i, hr, j);
and step 3: constructing a maximum vertex set V according to the medical demand map [ n, m ];
step 3-1, initializing a vertex set V as an empty set;
step 3-2, initializing i to 1;
3-3, if i is greater than n, skipping to 3-9;
step 3-4. initializing j to 1;
3-5, if j is larger than m, skipping to the step 3-8;
and 3-6, if the weights of the 8 mark points of map [ i-1, j-1], map [ i-1, j ], map [ i-1, j +1], map [ i, j-1], map [ i, j +1], map [ i +1, j-1], map [ i +1, j ], map [ i +1, j +1] are all smaller than the weight of map [ i, j ], adding V ═ i, j ] into the vertex set V. If the 8 marker points exceed the boundary of the weight matrix, the default weight of the marker point exceeding the boundary is less than map [ i, j ];
step 3-7, j is j +1, and the step 3-4 is skipped;
step 3-8, i is i +1, and skipping to step 3-2;
and 3-9, finishing.
And 4, step 4: the deployment location is calculated.
Step 4-1, initializing a deployment position set S as an empty set;
step 4-2, constructing a medical requirement map [ n, m ] through the step 2;
4-3, obtaining a maximum value vertex set V of the medical requirement map [ n, m ] through the step 3;
step 4-4, selecting the vertex V with the minimum weight in the maximum value vertex set V as [ i, j ]
Step 4-5, calling the function add _ st (map, i, j) of the step 5 to add the vertex v with the minimum weight into the deployment position set S
4-6, if the weights of all the mark points in the map [ n, m ] are not 0, skipping to the step 4-3;
step 4-7, ending, the deployment position set S is a mark point set needing to deploy the medical station;
step 5, constructing a pruned landmark weighting function add _ st (map, st _ i, st _ j), specifically as follows:
step 5-1, initializing tl as the maximum annual access load L accepted by a station, wherein cur _ diff is 0, and level _ weight is 0;
step 5-2 initializes that i is 1 and level _ V is empty.
5-3, if i is greater than n, jumping to the step 5-11;
step 5-4, initializing j to 1;
step 5-5. if j > m, jump to step 5-10
Step 5-6.diff _ i ═ abs (i-st _ i) and diff _ j ═ abs (j-st _ j), where abs (x) denotes the absolute value of x
Step 5-7.max _ diff ═ max (diff _ i, diff _ j), where max (x, y) denotes taking the larger of x and y
Step 5-8. if max _ diff is cur _ diff, [ i, j ] is added to level _ V, level _ weight + ═ map [ i, j ]
Step 5-9.j + +, jump to step 5-5
Step 5-10.i + +, jump to step 5-3
And 5-11, if the level _ weight is less than tl, setting the mark point V in the level _ V as [ a, b ] and map [ a, b ] as 0. tl-level _ weight, jump to step 5-13.
Step 5-12, setting any mark point V in level _ V as [ a, b ], and setting map [ a, b ] as map [ a, b ] tl/level _ weight, wherein tl is 0;
and 5-13, if tl >0, jumping to the step 5-2.
And 5-14, ending.
The invention has the following beneficial effects:
the invention solves the problem of deployment planning of medical sites by adopting a data fusion and coverage algorithm, comprehensively considers the influence of population structures and the distribution of superior medical institutions, converts the influence into the coverage problem of a demand matrix, and provides a distribution scheme for obtaining the sites by calculating the coverage algorithm. The following can be effectively balanced: the number of the stations, the load balance of the stations and the average moving distance of the patient greatly improve the operating efficiency of the urban medical system.
Detailed Description
The present invention will be further described with reference to the following examples.
The two key points of the invention are: 1. how to fuse three different attributes of data, demographic and high-level hospital data, into location-related quantified medical needs; 2. how to select a deployment position to cover the requirements according to the position-related medical requirement data obtained after fusion.
The basic idea of data fusion is: and setting a mark point every 1 kilometer by 1 kilometer in the city map to form a mark point matrix network. And modifying the demand weight of each candidate point according to a specific algorithm by using the two types of data respectively, and finally obtaining a mark point matrix network with the demand weight, wherein the mark point matrix network is called a demand map. Since a person's medical needs can be satisfied by sites at different distances, the closer the site is, the better, so a person's needs will be distributed to the vicinity centered on his location, and likewise, the medical needs satisfied by a superior medical institution will be distributed to the vicinity centered on his location.
The basic idea of coverage is: finding out all maximum points (the demand weight is greater than the mark points of all adjacent points) in the demand map, deploying a station at the maximum point with the minimum weight, then clearing 0 the mark point weight of the service area of the station, then recalculating all the maximum points of the demand map, and then finding out the maximum point deployment station with the minimum weight. The present invention employs square area coverage because of the problem of overlap of circular coverage.
And establishing a connecting line between each maximum value point and the maximum value point nearest to the maximum value point, wherein the connecting line is called an edge. The maxima points and edges form a graph G (V, E), where V is the set of maxima points and E is the set of edges. Sites are deployed on the edge of graph G using greedy to cover the demand.
Table 1: demographic structure raw data table PTB
Table 2: superior medical institution raw data table HTB
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
parameter definition:
max _ diff: represents the greater of diff _ j and diff _ i;
level _ weight: representing the total weight to be distributed of the layer where the current mark point [ i, j ] is located;
level _ pcount: indicating how many mark points are on the layer where the current mark point [ i, j ] is;
pr: information representing current people in the original data table of the population structure;
PTB: a table of raw data representing a population structure;
age is: indicates the age of the person currently being treated;
hr: indicating the currently processed superior medical institution information;
count: number of outpatients of the superior medical institution hr;
step 1: weight distribution dist (weight, c _ i, c _ j)
Distributing half weight value weight to mark points [ c _ i, c _ j ], distributing one quarter weight value to 8 mark points adjacent to the mark points [ c _ i, c _ j ], distributing one eighth weight value to 16 mark points outside the 8 mark points, and so on. When the weight value which is not distributed is less than 1% of the initial weight, the weight value is directly discarded. The concrete implementation is as follows:
step 1-1. initializing i ═ 1;
1-2, if i is greater than n, jumping to the step 1-13;
step 1-3. initialize j-1
1-4, if j is larger than m, jumping to the step 1-12;
step 1-5.diff _ i ═ abs (i-c _ i) and diff _ j ═ abs (j-c _ j), where abs (x) denotes the absolute value of x
Step 1-6.max _ diff ═ max (diff _ i, diff _ j), where max (x, y) denotes taking the larger of x and y
1-7. if max _ diff >5, jump to step 1-11.// the point [ i, j ] is outside the 5 th layer centered at [ c _ i, c _ j ], the remaining total weight to be distributed is less than 1% of the initial value, so no processing is required
Step 1-8.level _ weight ═ weight [ ((0.5) ^ (max _ diff + 1)); // the weights to be distributed for that layer
Step 1-9.level _ pcount ═ max _ diff +1 ^2- (max _ diff) ^2, the number of mark points of this layer;
and 1-10, if the calculated map [ i, j ] is less than 0, reassigning the map [ i, j ] to be 0.
Step 1-11.j is j +1, and skipping to step 1-4;
step 1-12.i + +, skipping to step 1-2;
and 1-13, finishing.
The implementation procedure of the dist (weight, c _ i, c _ j) is as follows:
weight:
[c_i,c_j]:
diff_i＝abs(i-c_i)；diff_j；level_weight；level_pcount
pr＝PTB[h]
pr.age
dist(map,weight,pr.i,pr.j)
hr.count
level_weight＝0；level_V；
step 2, fusing the human mouth data and the hospital data to obtain a medical requirement map, which comprises the following specific steps:
step 2-1, initializing a medical requirement map [ n, m ] to show that the city map is covered by a matrix with the length of n and the width of m mark points, wherein the map [ i, j ] represents the requirement weight of the mark points [ i, j ], i is more than or equal to 1 and less than or equal to n, and j is more than or equal to 1 and less than or equal to m; the distance between [ i, j ] and the index point represented by [ i +1, j ] is 1 km, and the distance between [ i, j ] and the index point represented by [ i, j +1] is 1 km. The boundary of the area covered by the mark point matrix exceeds 5 kilometers of the urban marginal cell or hospital.
Step 2-2, initializing h to 1;
step 2-3. extract the row of the h-th person in the demographic structure raw data table (i.e., table 1), pr ═ PTB [ h ].
Step 2-4. initialize weight 1
Step 2-5. if pr.age <12 or pr.age >60, weight is 5;
step 2-6. if 22< pr.age <40 and pr.sex equals "woman", then weight is 5;
step 2-7, calling a weight distribution algorithm to distribute the weight to the mark points near the cell where the h-th personal member is located, namely dist (map, weight, pr.i, pr.j)
Step 2-8.h ═ h +1, then jump to step 2-3.
Step 2-9, initializing h to 1;
and 2-10, taking the line of the h-th medical institution in the original data table (namely the table 2) of the superior medical institution, wherein hr is HTB [ h ].
Step 2-11. initialize weight 0-hr
Step 2-12, calling a weight distribution algorithm to distribute the weight to the mark points near the h hospital, namely dist (map, weight, hr.i, hr, j);
and step 3: constructing a maximum vertex set V according to the medical demand map [ n, m ];
step 3-1, initializing a vertex set V as an empty set;
step 3-2, initializing i to 1;
3-3, if i is greater than n, skipping to 3-9;
step 3-4. initializing j to 1;
3-5, if j is larger than m, skipping to the step 3-8;
and 3-6, if the weights of the 8 mark points of map [ i-1, j-1], map [ i-1, j ], map [ i-1, j +1], map [ i, j-1], map [ i, j +1], map [ i +1, j-1], map [ i +1, j ], map [ i +1, j +1] are all smaller than the weight of map [ i, j ], adding V ═ i, j ] into the vertex set V. If the 8 marker points exceed the boundary of the weight matrix, the default weight of the marker point exceeding the boundary is less than map [ i, j ];
step 3-7, j is j +1, and the step 3-4 is skipped;
step 3-8, i is i +1, and skipping to step 3-2;
and 3-9, finishing.
And 4, step 4: the deployment location is calculated.
Step 4-1, initializing a deployment position set S as an empty set;
step 4-2, constructing a medical requirement map [ n, m ] through the step 2;
4-3, obtaining a maximum value vertex set V of the medical requirement map [ n, m ] through the step 3;
step 4-4, selecting the vertex V with the minimum weight in the maximum value vertex set V as [ i, j ]
Step 4-5, calling the function add _ st (map, i, j) of the step 5 to add the vertex v with the minimum weight into the deployment position set S
4-6, if the weights of all the mark points in the map [ n, m ] are not 0, skipping to the step 4-3;
step 4-7, ending, the deployment position set S is a mark point set needing to deploy the medical station;
step 5, constructing a pruned landmark weighting function add _ st (map, st _ i, st _ j), specifically as follows:
step 5-1, initializing tl as the maximum annual access load L accepted by a station, wherein cur _ diff is 0, and level _ weight is 0;
step 5-2 initializes that i is 1 and level _ V is empty.
5-3, if i is greater than n, jumping to the step 5-11;
step 5-4, initializing j to 1;
step 5-5. if j > m, jump to step 5-10
Step 5-6.diff _ i ═ abs (i-st _ i) and diff _ j ═ abs (j-st _ j), where abs (x) denotes the absolute value of x
Step 5-7.max _ diff ═ max (diff _ i, diff _ j), where max (x, y) denotes taking the larger of x and y
Step 5-8. if max _ diff is cur _ diff, [ i, j ] is added to level _ V, level _ weight + ═ map [ i, j ]
Step 5-9.j + +, jump to step 5-5
Step 5-10.i + +, jump to step 5-3
And 5-11, if the level _ weight is less than tl, setting the mark point V in the level _ V as [ a, b ] and map [ a, b ] as 0. tl-level _ weight, jump to step 5-13.
Step 5-12, setting any mark point V in level _ V as [ a, b ], and setting map [ a, b ] as map [ a, b ] tl/level _ weight, wherein tl is 0;
and 5-13, if tl >0, jumping to the step 5-2.
And 5-14, ending.
Claims (1)
1. A community medical site deployment method based on data fusion is characterized by comprising the following steps:
parameter definition:
max _ diff: represents the greater of diff _ j and diff _ i;
level _ weight: representing the total weight to be distributed of the layer where the current mark point [ i, j ] is located;
level _ pcount: indicating how many mark points are on the layer where the current mark point [ i, j ] is;
pr: information representing current people in the original data table of the population structure;
PTB: a table of raw data representing a population structure;
age is: indicates the age of the person currently being treated;
hr: indicating the currently processed superior medical institution information;
count: number of outpatients of the superior medical institution hr;
step 1: constructing weight distribution dist (weight, c _ i, c _ j);
distributing half weight value weight to mark points [ c _ i, c _ j ], distributing one quarter weight value to 8 mark points adjacent to the mark points [ c _ i, c _ j ], distributing one eighth weight value to 16 mark points outside the 8 mark points, and so on; when the weight value which is not distributed is less than 1% of the initial weight, directly discarding; the concrete implementation is as follows:
step 1-1, initializing i to be 1;
1-2, if i is larger than n, skipping to 1-13;
1-3. initializing j to 1
1-4, if j is larger than m, skipping to step 1-12;
step 1-5, making diff _ i equal to the absolute value of (i-c _ i), and making diff _ j equal to the absolute value of (j-c _ j);
step 1-6, making max _ diff equal to the larger of diff _ i and diff _ j;
1-7, if max _ diff is larger than 5, jumping to the step 1-11;
step 1-8, let level _ weight equal weight ((0.5) ^ (max _ diff + 1));
step 1-9, making level _ pcount equal to (max _ diff +1) ^2- (max _ diff) ^ 2;
step 1-10, enabling map [ i, j ] to be equal to map [ i, j ] + level _ weight/level _ pcount, and if the calculated map [ i, j ] is smaller than 0, reassigning the map [ i, j ] to be 0;
step 1-11, increasing j by 1, and then jumping to step 1-4;
step 1-12, increasing i by 1, and then jumping to step 1-2;
1-13, finishing;
step 2, fusing the human mouth data and the hospital data to obtain a medical requirement map, which comprises the following specific steps:
step 2-1, initializing a medical requirement map [ n, m ] to show that the medical requirement map is covered by a matrix with n length and m width mark points, wherein the map [ i, j ] represents the requirement weight of the mark points [ i, j ], i is more than or equal to 1 and less than or equal to n, and j is more than or equal to 1 and less than or equal to m; the distance between [ i, j ] and the mark point represented by [ i +1, j ] is 1 kilometer, and the distance between [ i, j ] and the mark point represented by [ i, j +1] is 1 kilometer; the boundary of the area covered by the mark point matrix exceeds 5 kilometers of the urban marginal cell or hospital;
step 2-2, initializing h to be 1;
step 2-3, making pr equal to PTB [ h ], namely extracting the row of the h-th personal member in the population structure original data table;
step 2-4, initializing the weight of the h-th person to be 1;
step 2-5. if pr.age is less than 12 or pr.age is greater than 60, let weight equal to 5;
step 2-6. if pr.age is greater than 22 and less than 40, and pr.sex is equal to "woman", let weight equal to 5;
step 2-7, distributing weight to a mark point nearby a cell where the h-th personal member is located, namely dist (weight, pr.i, pr.j), by using a weight distribution algorithm in the step 1;
step 2-8, if unprocessed people still exist in the population structure original data table, increasing h by 1 and then jumping to step 2-3;
step 2-9, initializing h ^ 1;
step 2-10, making hr equal to HTB (h ^), namely, taking a row of the h ^ th medical institution in the original data table of the superior medical institution; HTB represents an original data table of a superior medical institution;
step 2-11. initialize weight to 0-hr
Step 2-12, using the weight distribution algorithm of step 1 to distribute the weight to the mark points near the h ^ hospital, namely dist (weight, hr.i, hr, j);
and step 3: constructing a maximum vertex set V according to the medical demand map [ n, m ];
step 3-1, initializing a vertex set V as an empty set;
step 3-2, initializing i to be 1;
3-3, if i is larger than n, skipping to 3-9;
step 3-4, initializing j to be 1;
3-5, if j is larger than m, skipping to 3-8;
step 3-6, if the weights of the 8 mark points of map [ i-1, j-1], map [ i-1, j ], map [ i-1, j +1], map [ i, j-1], map [ i, j +1], map [ i +1, j-1], map [ i +1, j ], map [ i +1, j +1] are all smaller than map [ i, j ], adding V ═ i, j ] into the vertex set V; if the 8 marker points exceed the boundary of the weight matrix, the default weight of the marker point exceeding the boundary is less than map [ i, j ];
step 3-7, increasing j by 1, and skipping to step 3-4;
step 3-8, increasing 1 to i, and skipping to step 3-2;
step 3-9, finishing;
and 4, step 4: calculating a deployment position;
step 4-1, initializing a deployment position set S as an empty set;
step 4-2, constructing a medical requirement map [ n, m ] through the step 2;
4-3, obtaining a maximum value vertex set V of the medical requirement map [ n, m ] through the step 3;
step 4-4, selecting the vertex V with the minimum weight in the maximum value vertex set V as [ i, j ]
Step 4-5, calling the function add _ st (map, i, j) of the step 5 to add the vertex v with the minimum weight into the deployment position set S
4-6, if the weights of all the mark points in the map [ n, m ] are not 0, skipping to the step 4-3;
step 4-7, ending, the deployment position set S is a mark point set needing to deploy the medical station;
step 5, constructing a pruned landmark weighting function add _ st (map, st _ i, st _ j), specifically as follows:
step 5-1, initializing tl as the maximum annual access load L accepted by a site, initializing cur _ diff as 0, and initializing level _ weight as 0;
step 5-2, initializing i to be 1 and initializing level _ V to be null;
5-3, if i is larger than n, jumping to the step 5-11;
step 5-4, initializing j to be 1;
step 5-5, if j is larger than m, jumping to step 5-10
Step 5-6, making diff _ i equal to the absolute value of (i-st _ i) and diff _ j equal to the absolute value of (j-st _ j);
step 5-7, making max _ diff equal to the larger of diff _ i and diff _ j;
step 5-8, if max _ diff and cur _ diff are equal, adding [ i, j ] into level _ V, and simultaneously setting level _ weight as level _ weight + map [ i, j ];
step 5-9, increasing j by 1, and skipping to step 5-5
Step 5-10, increasing i by 1, and skipping to step 5-3
Step 5-11, if level _ weight is smaller than tl, setting map [ a, b ] as 0 for any mark point V in level _ V ═ a, b ]; setting tl as tl-level _ weight; then jumping to the step 5-13;
step 5-12, setting map [ a, b ] as map [ a, b ] tl/level _ weight for any mark point V in level _ V, and simultaneously setting tl as 0;
step 5-13, if tl is larger than 0, skipping to step 5-2;
and 5-14, ending.
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| CN107391956A (en) * | 2017-09-01 | 2017-11-24 | 复旦大学 | A kind of extracting method of the medical resource spatial layout feature based on polynary isomeric data |
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| CN103999117A (en) * | 2011-12-22 | 2014-08-20 | 英特尔公司 | Methods and apparatus for providing assistance services for large crowds |
| CN105243114A (en) * | 2015-09-25 | 2016-01-13 | 中国农业银行股份有限公司 | Siting analysis method and apparatus |
| CN106643783A (en) * | 2016-12-28 | 2017-05-10 | 国网天津市电力公司东丽供电分公司 | Shortest path Thiessen polygon-based electric vehicle charging station searching method |
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