CN107945857A - A kind of community medicine site deployment method based on data fusion - Google Patents
A kind of community medicine site deployment method based on data fusion Download PDFInfo
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Abstract
The present invention discloses a kind of community medicine site deployment method based on data fusion.The present invention forms an index point matrixing network to one index point of every 1 kilometer of * 1 kilometer of setting in city map.The demand weight of each candidate point is changed according to special algorithm, an index point matrixing network for carrying demand weight is finally obtained, is known as demand map;All maximum points are found out in demand map, a website is disposed in the maximum point of weight minimum, then by the website can coverage index point weight clear 0, then recalculate all maximum points of demand map, then find out weight minimum maximum point dispose website;Finally made untill the index point weight of whole demand map all returns 0 using the method for this iteration.The present invention being capable of active balance:Website quantity, the load balancing of website, the target of the aspect of patient's average moving distance three, greatly improve the operational efficiency of city medical system.
Description
Technical field
The present invention relates to a kind of community medicine site deployment method based on data fusion.
Background technology
Community medicine is the development trend of following medical services, has the time to diagnose the illness for saving sufferer, saves society's medical treatment
The various effect with traffic resource etc..
The deployment of community medicine website in city is usually disposed according to street or some big cells.Population is to pass
The unique foundation for community medicine site deployment of uniting.
However as the development of urbanization, not only the population trend in each region is uneven, and also there are following factor to influence society
The deployment effect of area's medical treatment website.
1. population structure.The region of the same size of population, the serious region of aging need more doctors than other regions
Docking station point.Natus, children and the high region of women of child-bearing age's ratio need more medical websites than other regions.
2. the distribution of high-grade hospital.In the region with high-grade hospital, crowd subtracts the demand of community medical institutions
Few, the region more remote apart from high-grade hospital is bigger to the demand of community medicine website.
The information of these two aspects is from separate sources, different type, the data of different objects, to the place of these two aspects data
Manage and merge, the medical website deployment scheme of optimization can be obtained.
In conclusion the method for the present invention carries out community medicine site deployment, it is intended to reaches following target:
The quantity of website is as few as possible, and the utilization rate of single website is high.Medical website needs to take substantial amounts of soil, manpower
With the resource such as medicine stock, improve the utilization rate of single website and reduce medical treatment as far as possible in the case of same service experience
Website quantity is main target;
The load balancing of each website.The website avoided is overstaffed and some websites does not have the medicable situation of sufferer.
Average latency of patient is used in the present invention to weigh the load of a website;
Less average arrival time.Crowd reaches the average time of medical website
The content of the invention
The present invention two key points be:1. how by population structure data and high-grade hospital data, these three are not belonged to together
Property data fusion be medical demand with the relevant quantization in position;2. how according to the position related medical need obtained after fusion
Data are sought, choose deployed position to cover these demands.
The basic thought of data fusion is:To one index point of every 1 kilometer of * 1 kilometer of setting in city map, one is formed
Index point matrixing network.Above-mentioned two classes data are changed to the demand weight of each candidate point according to special algorithm respectively, finally
The index point matrixing network for carrying demand weight to one, is known as demand map.Since the medical demand of a people can be by not
The website of same distance meets that nearer website is better, therefore a Man's Demands can be distributed to centered on his position
Close region, equally, the medical demand that a higher level medical institutions are met can be also distributed to centered on its position
Close region.
The basic thought of covering is:All maximum points are found out in demand map, and (demand weight is more than all adjacent
Point index point), weight minimum maximum point dispose a website, then by the website energy coverage index point
Weight clear 0, then recalculates all maximum points of demand map, then finds out the maximum point deployment station of weight minimum
Point ... is finally made untill the index point weight of whole demand map all returns 0 using the method for this iteration.Since circle covering is deposited
Overlapping the problem of, therefore the present invention is covered using square area.
By each maximum point with establishing line between its nearest maximum point, it is known as side.Maximum point and side structure
Into figure G (V, E), wherein V is the set of maximum point, and E is the set on side.Using greedy method website is disposed on the side of figure G
To be covered to demand.
Table 1:Population structure raw data table PTB
Table 2:Higher level medical institutions raw data table HTB
The technical solution adopted by the present invention to solve the technical problems includes the following steps:
Parameter definition:
max_diff:Represent the higher value in diff_j and diff_i;
level_weight:Represent the layer total weight to be distributed where current flag point [i, j];
level_pcount:Layer how many index point where current flag point [i, j] represented;
Pr:Represent information of the current persons in population structure raw data table;
PTB:Represent population structure raw data table;
pr.age:Represent the age of current processed personnel;
hr:Represent currently processed higher level medical institutions information;
hr.count:Represent the gop number of higher level medical institutions hr;
Step 1:Build the distribution dist (weight, c_i, c_j) of weight
The half of weighted value weight is distributed in index point [c_i, c_j], a quarter of weight is distributed in and indicates
8 adjacent index points of point [c_i, c_j], 1/8th of weight is distributed in 16 index points of above-mentioned 8 index point outer layers,
And so on.When the weighted value not being distributed is less than the 1% of initial weight, directly abandon.It is implemented as follows:
Step 1-1. initializes i=1;
If step 1-2. i>N, jumps to step 1-13;
Step 1-3. initializes j=1
If step 1-4. j>M, jumps to step 1-12;
Step 1-5.diff_i=abs (i-c_i), diff_j=abs (j-c_j), wherein abs (x) represent to take that x's is absolute
Value;
Step 1-6.max_diff=max (diff_i, diff_j), wherein max (x, y) represent to take larger in x and y
Value;
If step 1-7. max_diff>5, the step 1-11. // point [i, j] is jumped to centered on [c_i, c_j]
Beyond 5th layer, the total weight of residue of required distribution is less than the 1% of initial value, so need not handle
Step 1-8.level_weight=weight* ((0.5) ^ (max_diff+1));It is distributed required for // this layer
Weight
Step 1-9.level_pcount=(max_diff+1) ^2- (max_diff) ^2, the index point number of this layer;
Step 1-10.map [i, j]=map [i, j]+level_weight/level_pcount, if map after calculating
[i, j] is less than 0, then map [i, j] is assigned a value of 0 again.
Step 1-11.j=j+1, jumps to step 1-4;
Step 1-12.i++, jumps to step 1-2;
Step 1-13. terminates.
Step 2:Demographic data and hospital data are merged, obtain medical demand map, it is specific as follows:
Step 2-1. initialization medical demand map map [n, m], represent the city map by a length of n, width is m mark
The matrix of point is covered, and wherein map [i, j] represents the demand weight of index point [i, j], 1≤i≤n, 1≤j≤m;[i, j] with
The distance of index point represented by [i+1, j] is 1 kilometer, and the distance of [i, j] and the index point represented by [i, j+1] is 1 kilometer.
The zone boundary that mark dot matrix is covered, more than 5 kilometers of city most edge cell or hospital.
Step 2-2. initializes h=1;
The row where h-th of personnel in step 2-3. extraction population structure raw data tables (i.e. table 1), pr=PTB
[h]。
Step 2-4. initializes weight=1
If step 2-5. pr.age<12 or pr.age>60, then weight=5;
If step 2-6. 22<pr.age<40 and pr.sex is equal to " female ", then weight=5;
Indicate where weight is distributed to h-th of personnel by the Distribution Algorithm of step 2-7. calling weights near cell
Point, i.e. dist (map, weight, pr.i, pr.j)
Step 2-8.h=h+1, then branches to step 2-3.
Step 2-9. initializes h=1;
Step 2-10. takes in higher level medical institutions raw data table (i.e. table 2) h with the row where medical institutions, hr=
HTB[h]。
Step 2-11. initializes weight=0-hr.count
Weight is distributed to the index point near h-th of hospital, i.e. dist by the Distribution Algorithm of step 2-12 calling weights
(map,weight,hr.i,hr,j);
Step 3:Maximum vertex set V is built according to medical demand map map [n, m];
Step 3-1. initialization vertex set V is null set;
Step 3-2. initializes i=1;
If step 3-3. i>N, jumps to step 3-9;
Step 3-4. initializes j=1;
If step 3-5. j>M, jumps to step 3-8;
If step 3-6. 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], the weight of map [i+1, j+1] this 8 index points is respectively less than map [i, j], then by v
=[i, j] is added in vertex set V.If had in this 8 index points beyond weight matrix border, acquiescence is beyond border
The weight of this index point is less than map [i, j];
Step 3-7.j=j+1, jumps to step 3-4;
Step 3-8.i=i+1, jumps to step 3-2;
Step 3-9. terminates.
Step 4:Calculate deployed position.
Step 4-1. initialization deployment location sets S is null set;
Step 4-2. builds medical demand map map [n, m] by step 2;
Step 4-3. obtains the maximum vertex set V of medical demand map map [n, m] by step 3;
Vertex v=[i, the j] of step 4-4. weight selection minimums in maximum vertex set V
The vertex v of weight minimum is added deployed position collection by the function add_st (map, i, j) of step 4-5. invocation steps 5
Close S
If the weight of not all index point is all 0 in step 4-6. map [n, m], then step 4-3 is jumped to;
Step 4-7. terminates, then deployed position set S is exactly the mark point set for needing to dispose medical website;
Step 5. structure deletes index point weighting function add_st (map, st_i, st_j), specific as follows:
Step 5-1. initialization for one website of tl receptible most big year accesses load capacity L, cur_diff=0,
Level_weight=0;
Step 5-2 initializes i=1, and level_V is sky.
If step 5-3. i>N, jumps to step 5-11;
Step 5-4. initializes j=1;
If step 5-5. j>M, jumps to step 5-10
Step 5-6.diff_i=abs (i-st_i), diff_j=abs (j-st_j), wherein abs (x) represent to take that x's is exhausted
To value
Step 5-7.max_diff=max (diff_i, diff_j), wherein max (x, y) represent to take the higher value in x and y
If step 5-8. max_diff=cur_diff, [i, j] are added in level_V, level_weight+=
map[i,j]
Step 5-9.j++, jumps to step 5-5
Step 5-10.i++, jumps to step 5-3
If step 5-11. level_weight<Tl, to any of level_V index points v=[a, b], map [a, b]
It is arranged to 0.Tl-=level_weight, jumps to step 5-13.
For step 5-12. to any of level_V index points v=[a, b], map [a, b] is arranged to map [a, b] * tl/
Level_weight, tl=0;
If step 5-13. tl>0, jump to step 5-2.
Step 5-14. terminates.
The present invention has the beneficial effect that:
The present invention solves medical site deployment planning problem using data fusion and covering algorithm, considers population structure
With the influence of the distribution of higher level medical institutions, the covering problem of requirement matrix is converted into, and proposes a kind of covering algorithm to calculate
Obtain the distribution scheme of website.Can be with active balance:Website quantity, the load balancing of website, patient average moving distance tripartite
The target in face, greatly improves the operational efficiency of city medical system.
Embodiment
With reference to embodiment, the invention will be further described.
The present invention two key points be:1. how by population structure data and high-grade hospital data, these three are not belonged to together
Property data fusion be medical demand with the relevant quantization in position;2. how according to the position related medical need obtained after fusion
Data are sought, choose deployed position to cover these demands.
The basic thought of data fusion is:To one index point of every 1 kilometer of * 1 kilometer of setting in city map, one is formed
Index point matrixing network.Above-mentioned two classes data are changed to the demand weight of each candidate point according to special algorithm respectively, finally
The index point matrixing network for carrying demand weight to one, is known as demand map.Since the medical demand of a people can be by not
The website of same distance meets that nearer website is better, therefore a Man's Demands can be distributed to centered on his position
Close region, equally, the medical demand that a higher level medical institutions are met can be also distributed to centered on its position
Close region.
The basic thought of covering is:All maximum points are found out in demand map, and (demand weight is more than all adjacent
Point index point), weight minimum maximum point dispose a website, then by the website energy coverage index point
Weight clear 0, then recalculates all maximum points of demand map, then finds out the maximum point deployment station of weight minimum
Point ... is finally made untill the index point weight of whole demand map all returns 0 using the method for this iteration.Since circle covering is deposited
Overlapping the problem of, therefore the present invention is covered using square area.
By each maximum point with establishing line between its nearest maximum point, it is known as side.Maximum point and side structure
Into figure G (V, E), wherein V is the set of maximum point, and E is the set on side.Using greedy method website is disposed on the side of figure G
To be covered to demand.
Table 1:Population structure raw data table PTB
Table 2:Higher level medical institutions raw data table HTB
The technical solution adopted by the present invention to solve the technical problems includes the following steps:
Parameter definition:
max_diff:Represent the higher value in diff_j and diff_i;
level_weight:Represent the layer total weight to be distributed where current flag point [i, j];
level_pcount:Layer how many index point where current flag point [i, j] represented;
Pr:Represent information of the current persons in population structure raw data table;
PTB:Represent population structure raw data table;
pr.age:Represent the age of current processed personnel;
hr:Represent currently processed higher level medical institutions information;
hr.count:Represent the gop number of higher level medical institutions hr;
Step 1:Build the distribution dist (weight, c_i, c_j) of weight
The half of weighted value weight is distributed in index point [c_i, c_j], a quarter of weight is distributed in and indicates
8 adjacent index points of point [c_i, c_j], 1/8th of weight is distributed in 16 index points of above-mentioned 8 index point outer layers,
And so on.When the weighted value not being distributed is less than the 1% of initial weight, directly abandon.It is implemented as follows:
Step 1-1. initializes i=1;
If step 1-2. i>N, jumps to step 1-13;
Step 1-3. initializes j=1
If step 1-4. j>M, jumps to step 1-12;
Step 1-5.diff_i=abs (i-c_i), diff_j=abs (j-c_j), wherein abs (x) represent to take that x's is absolute
Value
Step 1-6.max_diff=max (diff_i, diff_j), wherein max (x, y) represent to take the higher value in x and y
If step 1-7. max_diff>5, the step 1-11. // point [i, j] is jumped to centered on [c_i, c_j]
Beyond 5th layer, the total weight of residue of required distribution is less than the 1% of initial value, so need not handle
Step 1-8.level_weight=weight* ((0.5) ^ (max_diff+1));It is distributed required for // this layer
Weight
Step 1-9.level_pcount=(max_diff+1) ^2- (max_diff) ^2, the index point number of this layer;
Step 1-10.map [i, j]=map [i, j]+level_weight/level_pcount, if map after calculating
[i, j] is less than 0, then map [i, j] is assigned a value of 0 again.
Step 1-11.j=j+1, jumps to step 1-4;
Step 1-12.i++, jumps to step 1-2;
Step 1-13. terminates.
The dist's (weight, c_i, c_j) realizes that program 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:Demographic data and hospital data are merged, obtain medical demand map, it is specific as follows:
Step 2-1. initialization medical demand map map [n, m], represent the city map by a length of n, width is m mark
The matrix of point is covered, and wherein map [i, j] represents the demand weight of index point [i, j], 1≤i≤n, 1≤j≤m;[i, j] with
The distance of index point represented by [i+1, j] is 1 kilometer, and the distance of [i, j] and the index point represented by [i, j+1] is 1 kilometer.
The zone boundary that mark dot matrix is covered, more than 5 kilometers of city most edge cell or hospital.
Step 2-2. initializes h=1;
The row where h-th of personnel in step 2-3. extraction population structure raw data tables (i.e. table 1), pr=PTB
[h]。
Step 2-4. initializes weight=1
If step 2-5. pr.age<12 or pr.age>60, then weight=5;
If step 2-6. 22<pr.age<40 and pr.sex is equal to " female ", then weight=5;
Indicate where weight is distributed to h-th of personnel by the Distribution Algorithm of step 2-7. calling weights near cell
Point, i.e. dist (map, weight, pr.i, pr.j)
Step 2-8.h=h+1, then branches to step 2-3.
Step 2-9. initializes h=1;
Step 2-10. takes in higher level medical institutions raw data table (i.e. table 2) h with the row where medical institutions, hr=
HTB[h]。
Step 2-11. initializes weight=0-hr.count
Weight is distributed to the index point near h-th of hospital, i.e. dist by the Distribution Algorithm of step 2-12 calling weights
(map,weight,hr.i,hr,j);
Step 3:Maximum vertex set V is built according to medical demand map map [n, m];
Step 3-1. initialization vertex set V is null set;
Step 3-2. initializes i=1;
If step 3-3. i>N, jumps to step 3-9;
Step 3-4. initializes j=1;
If step 3-5. j>M, jumps to step 3-8;
If step 3-6. 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], the weight of map [i+1, j+1] this 8 index points is respectively less than map [i, j], then by v
=[i, j] is added in vertex set V.If had in this 8 index points beyond weight matrix border, acquiescence is beyond border
The weight of this index point is less than map [i, j];
Step 3-7.j=j+1, jumps to step 3-4;
Step 3-8.i=i+1, jumps to step 3-2;
Step 3-9. terminates.
Step 4:Calculate deployed position.
Step 4-1. initialization deployment location sets S is null set;
Step 4-2. builds medical demand map map [n, m] by step 2;
Step 4-3. obtains the maximum vertex set V of medical demand map map [n, m] by step 3;
Vertex v=[i, the j] of step 4-4. weight selection minimums in maximum vertex set V
The vertex v of weight minimum is added deployed position collection by the function add_st (map, i, j) of step 4-5. invocation steps 5
Close S
If the weight of not all index point is all 0 in step 4-6. map [n, m], then step 4-3 is jumped to;
Step 4-7. terminates, then deployed position set S is exactly the mark point set for needing to dispose medical website;
Step 5. structure deletes index point weighting function add_st (map, st_i, st_j), specific as follows:
Step 5-1. initialization for one website of tl receptible most big year accesses load capacity L, cur_diff=0,
Level_weight=0;
Step 5-2 initializes i=1, and level_V is sky.
If step 5-3. i>N, jumps to step 5-11;
Step 5-4. initializes j=1;
If step 5-5. j>M, jumps to step 5-10
Step 5-6.diff_i=abs (i-st_i), diff_j=abs (j-st_j), wherein abs (x) represent to take that x's is exhausted
To value
Step 5-7.max_diff=max (diff_i, diff_j), wherein max (x, y) represent to take the higher value in x and y
If step 5-8. max_diff=cur_diff, [i, j] are added in level_V, level_weight+=
map[i,j]
Step 5-9.j++, jumps to step 5-5
Step 5-10.i++, jumps to step 5-3
If step 5-11. level_weight<Tl, to any of level_V index points v=[a, b], map [a, b]
It is arranged to 0.Tl-=level_weight, jumps to step 5-13.
For step 5-12. to any of level_V index points v=[a, b], map [a, b] is arranged to map [a, b] * tl/
Level_weight, tl=0;
If step 5-13. tl>0, jump to step 5-2.
Step 5-14. terminates.
Claims (1)
- A kind of 1. community medicine site deployment method based on data fusion, it is characterised in that include the following steps:Parameter definition:max_diff:Represent the higher value in diff_j and diff_i;level_weight:Represent the layer total weight to be distributed where current flag point [i, j];level_pcount:Layer how many index point where current flag point [i, j] represented;Pr:Represent information of the current persons in population structure raw data table;PTB:Represent population structure raw data table;pr.age:Represent the age of current processed personnel;hr:Represent currently processed higher level medical institutions information;hr.count:Represent the gop number of higher level medical institutions hr;Step 1:Build the distribution dist (weight, c_i, c_j) of weight;The half of weighted value weight is distributed in index point [c_i, c_j], a quarter of weight is distributed in and index point [c_ I, c_j] adjacent 8 index points, 1/8th of weight is distributed in 16 index points of above-mentioned 8 index point outer layers, with this Analogize;When the weighted value not being distributed is less than the 1% of initial weight, directly abandon;It is implemented as follows:Step 1-1. initializes i=1;If step 1-2. i>N, jumps to step 1-13;Step 1-3. initializes j=1If step 1-4. j>M, jumps to step 1-12;Step 1-5.diff_i=abs (i-c_i), diff_j=abs (j-c_j), wherein abs (x) represent to take the absolute value of xStep 1-6.max_diff=max (diff_i, diff_j), wherein max (x, y) represent to take the higher value in x and y;If step 1-7. max_diff>5, jump to step 1-11.;Step 1-8.level_weight=weight* ((0.5) ^ (max_diff+1));Step 1-9.level_pcount=(max_diff+1) ^2- (max_diff) ^2, the index point number of this layer;Step 1-10.map [i, j]=map [i, j]+level_weight/level_pcount, if map [i, j] after calculating Less than 0, then map [i, j] is assigned a value of 0 again;Step 1-11.j=j+1, jumps to step 1-4;Step 1-12.i++, jumps to step 1-2;Step 1-13. terminates;Step 2:Demographic data and hospital data are merged, obtain medical demand map, it is specific as follows:Step 2-1. initialization medical demand map map [n, m], represent the city map by a length of n, and width is m index point Matrix is covered, and wherein map [i, j] represents the demand weight of index point [i, j], 1≤i≤n, 1≤j≤m;[i, j] with [i+1, J] represented by the distance of index point be 1 kilometer, the distance of the index point represented by [i, j] and [i, j+1] is 1 kilometer;Mark The zone boundary that dot matrix is covered, more than 5 kilometers of city most edge cell or hospital;Step 2-2. initializes h=1;The row where h-th of personnel in step 2-3. extraction population structure raw data tables, population structure raw data table are Table 1, pr=PTB [h];Step 2-4. initializes weight=1If step 2-5. pr.age<12 or pr.age>60, then weight=5;If step 2-6. 22<pr.age<40 and pr.sex is equal to " female ", then weight=5;Step 2-7. calls the Distribution Algorithm of weight that weight is distributed to index point near h-th of personnel place cell, i.e., dist(map,weight,pr.i,pr.j)Step 2-8.h=h+1, then branches to step 2-3.Step 2-9. initializes h=1;Step 2-10. takes h in higher level medical institutions raw data table original with the row where medical institutions, higher level medical institutions Tables of data, that is, table 2, hr=HTB [h];Step 2-11. initializes weight=0-hr.countStep 2-12 calls the Distribution Algorithm of weight that weight is distributed to index point near h-th of hospital, i.e. dist (map, weight,hr.i,hr,j);Step 3:Maximum vertex set V is built according to medical demand map map [n, m];Step 3-1. initialization vertex set V is null set;Step 3-2. initializes i=1;If step 3-3. i>N, jumps to step 3-9;Step 3-4. initializes j=1;If step 3-5. j>M, jumps to step 3-8;If step 3-6. 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], the weight of map [i+1, j+1] this 8 index points is respectively less than map [i, j], then by v= [i, j] is added in vertex set V;Exceed this of border beyond weight matrix border, acquiescence if had in this 8 index points The weight of a index point is less than map [i, j];Step 3-7.j=j+1, jumps to step 3-4;Step 3-8.i=i+1, jumps to step 3-2;Step 3-9. terminates;Step 4:Calculate deployed position;Step 4-1. initialization deployment location sets S is null set;Step 4-2. builds medical demand map map [n, m] by step 2;Step 4-3. obtains the maximum vertex set V of medical demand map map [n, m] by step 3;Vertex v=[i, the j] of step 4-4. weight selection minimums in maximum vertex set VThe vertex v of weight minimum is added deployed position set S by the function add_st (map, i, j) of step 4-5. invocation steps 5If the weight of not all index point is all 0 in step 4-6. map [n, m], then step 4-3 is jumped to;Step 4-7. terminates, then deployed position set S is exactly the mark point set for needing to dispose medical website;Step 5. structure deletes index point weighting function add_st (map, st_i, st_j), specific as follows:Step 5-1. initialization for one website of tl receptible most big year accesses load capacity L, cur_diff=0, level_ Weight=0;Step 5-2 initializes i=1, and level_V is sky;If step 5-3. i>N, jumps to step 5-11;Step 5-4. initializes j=1;If step 5-5. j>M, jumps to step 5-10Step 5-6.diff_i=abs (i-st_i), diff_j=abs (j-st_j), wherein abs (x) represent to take the absolute value of xStep 5-7.max_diff=max (diff_i, diff_j), wherein max (x, y) represent to take the higher value in x and yIf step 5-8. max_diff=cur_diff, [i, j] are added in level_V, level_weight+=map [i,j]Step 5-9.j++, jumps to step 5-5Step 5-10.i++, jumps to step 5-3If step 5-11. level_weight<Tl, sets any of level_V index points v=[a, b], map [a, b] For 0;Tl-=level_weight, jumps to step 5-13.For step 5-12. to any of level_V index points v=[a, b], map [a, b] is arranged to map [a, b] * tl/level_ Weight, tl=0;If step 5-13. tl>0, jump to step 5-2;Step 5-14. terminates;Table 1:Population structure raw data table PTBTable 2:Higher level medical institutions raw data table HTB
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