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 PDF

Info

Publication number
CN107945857A
CN107945857A CN201711307048.7A CN201711307048A CN107945857A CN 107945857 A CN107945857 A CN 107945857A CN 201711307048 A CN201711307048 A CN 201711307048A CN 107945857 A CN107945857 A CN 107945857A
Authority
CN
China
Prior art keywords
weight
map
diff
level
jumps
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201711307048.7A
Other languages
Chinese (zh)
Other versions
CN107945857B (en
Inventor
朱旭东
张吕峥
余小益
方宝林
高春蓉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Bsoft Co Ltd
Original Assignee
Bsoft Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Bsoft Co Ltd filed Critical Bsoft Co Ltd
Priority to CN201711307048.7A priority Critical patent/CN107945857B/en
Publication of CN107945857A publication Critical patent/CN107945857A/en
Application granted granted Critical
Publication of CN107945857B publication Critical patent/CN107945857B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

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

A kind of community medicine site deployment method based on data fusion
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)

  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=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 the absolute value of x
    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, 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=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；
    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.count
    Step 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 V
    The 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 5
    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 the absolute value of x
    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, 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 PTB
    Table 2：Higher level medical institutions raw data table HTB
CN201711307048.7A 2017-12-11 2017-12-11 Community medical site deployment method based on data fusion Active CN107945857B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711307048.7A CN107945857B (en) 2017-12-11 2017-12-11 Community medical site deployment method based on data fusion

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711307048.7A CN107945857B (en) 2017-12-11 2017-12-11 Community medical site deployment method based on data fusion

Publications (2)

Publication Number Publication Date
CN107945857A true CN107945857A (en) 2018-04-20
CN107945857B CN107945857B (en) 2021-01-05

Family

ID=61946418

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711307048.7A Active CN107945857B (en) 2017-12-11 2017-12-11 Community medical site deployment method based on data fusion

Country Status (1)

Country Link
CN (1) CN107945857B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111385753A (en) * 2019-10-24 2020-07-07 南京瑞栖智能交通技术产业研究院有限公司 Medical facility accessibility evaluation method based on mobile phone signaling data

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020058517A1 (en) * 2000-11-13 2002-05-16 Toshihiko Furukawa Informing system
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
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

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020058517A1 (en) * 2000-11-13 2002-05-16 Toshihiko Furukawa Informing system
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
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

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111385753A (en) * 2019-10-24 2020-07-07 南京瑞栖智能交通技术产业研究院有限公司 Medical facility accessibility evaluation method based on mobile phone signaling data
CN111385753B (en) * 2019-10-24 2022-01-04 南京瑞栖智能交通技术产业研究院有限公司 Medical facility accessibility evaluation method based on mobile phone signaling data

Also Published As

Publication number Publication date
CN107945857B (en) 2021-01-05

Similar Documents

Publication Publication Date Title
CN107194128B (en) Multi-mode public transport network design method based on central radiation type network framework
Castella et al. Analysis of village accessibility and its impact on land use dynamics in a mountainous province of northern Vietnam
Dao et al. Spatio-temporal location modeling in a 3D indoor environment: the case of AEDs as emergency medical devices
CN109102868A (en) The site selecting method of medical emergency facility after a kind of shake
Zarkeshzadeh et al. A novel hybrid method for improving ambulance dispatching response time through a simulation study
CN107391956A (en) A kind of extracting method of the medical resource spatial layout feature based on polynary isomeric data
CN107945857A (en) A kind of community medicine site deployment method based on data fusion
Brown et al. Walking and proximity to the urban growth boundary and central business district
Xiao et al. Average strategy fictitious play with application to road pricing
CN115759669A (en) Urban blue-green ecological network construction method based on composite function
Timony et al. Promising quantities, disappointing distribution. Investigating the presence of French-speaking physicians in Ontario’s rural Francophone communities
Huang et al. Livability assessment of 101,630 communities in China’s major cities: A remote sensing perspective
Ryan The feasibility of medical unmanned aerial systems in suburban areas
Xiao et al. Exploring non-linear built environment effects on urban vibrancy under COVID-19: The case of Hong Kong
Villani et al. Socialising on a Skywalk: How Hong Kong’s elevated walkways become public open space
He et al. Geospatial analysis and research on social and spatial inequality of compulsory education: A case study of Hangzhou, China
O’Connor et al. Air transport geographies of the Asia-Pacific
CN106529560B (en) A kind of recognition methods of the urban function region of the comprehensive density of population and landscape structure
Sun et al. Inequitable job accessibility across educational and hukou groups in beijing
CN117473594A (en) Machine room deployment method and device, electronic equipment and storage medium
Yue et al. Evaluating the accessibility to healthcare facilities under the Chinese hierarchical diagnosis and treatment system
Villani et al. Patterns of Stationary Activities in the Elevated Pedestrian Networks of High-Density Asian Cities: The case of Mong Kok, Hong Kong
CN110826753B (en) Resource processing method, device and equipment thereof
Tanser The application of geographical information systems to infectious diseases and health systems in Africa.
Yi et al. Optimize the Planning of Ambulance Standby Points by Using Getis-Ord Gi* Based on Historical Emergency Data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB02 Change of applicant information

Address after: 310051 Fifth Floor of Pioneering Intelligence Building, No. 92 Yueda Lane, Changhe Street, Binjiang District, Hangzhou City, Zhejiang Province

Applicant after: Pioneering Huikang Technology Co.,Ltd.

Address before: 310013 33rd Floor, Pioneering Building, 199 Wensan Road, Xihu District, Hangzhou City, Zhejiang Province

Applicant before: B-SOFT Ltd.

CB02 Change of applicant information
GR01 Patent grant
GR01 Patent grant