CN114649085A - Emergency medical supplies management system based on big data - Google Patents
Emergency medical supplies management system based on big data Download PDFInfo
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Abstract
The invention relates to the technical field of medical supplies management and allocation, which is used for solving the problem that in the prior art, supplies are not allocated and allocated according to the distribution condition of hospital positions in an area, in particular to an emergency medical supplies management system based on big data, which comprises a medical big data platform, wherein the medical big data platform is in communication connection with an allocation management module, a supplies analysis module and an area management module; dividing the management area into a plurality of analysis areas i, comparing the abundance coefficient CPi with abundance thresholds CPmin and CPmax, and marking the analysis areas i as deficient areas, normal areas or abundant areas according to the comparison result; according to the invention, the stock analysis of the emergency medical articles is carried out on each analysis area through the material analysis module, and whether the material stock in the area meets the requirements or not is judged by combining the total number of people in the area and the number of people of old people in the area, so that the material allocation among the areas can be carried out according to the material stock condition.
Description
Technical Field
The invention relates to the technical field of medical supply management and allocation, in particular to an emergency medical supply management system based on big data.
Background
Medical supplies are a general term for articles used for treating wounds or diseases, the medical supplies can include household medical supplies, pet medical supplies and the like, emergency medical articles are rescue articles which are used for emergency when people have accidents, and the emergency medical articles can be classified into different categories according to different environments and different using objects.
The invention patent with the publication number of CN109545338A discloses a medical resource management method, a server and a system, which can reduce the medical resource allocation cost and improve the utilization rate of medical resources; however, the medical resource management method, the server and the system can only perform resource allocation analysis through the geographical position; however, the material allocation method is not scientific enough, and in practical application, the relevance between hospitals needs to be considered, for example, when two hospitals are in a very close distance, the emergency medical materials between the two hospitals can be mutually supplemented and allocated in a short time when an accident occurs, and a hospital independently existing in a certain area cannot obtain the material supply of an adjacent hospital in a short time, so that scientific material allocation needs to be performed according to the distribution condition of the hospital in the area.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide an emergency medical article management system based on big data in order to solve the problem that the prior art does not distribute and allocate materials according to the distribution condition of hospital positions in regions.
The purpose of the invention can be realized by the following technical scheme: the emergency medical supply management system based on big data comprises a medical big data platform, wherein the medical big data platform is in communication connection with a deployment management module, a material analysis module and a region management module;
dividing the management area into a plurality of analysis areas i, i =1, 2, …, n is a positive integer, performing stock analysis on the emergency medical supplies in the analysis areas i by a material analysis module to obtain a sufficiency coefficient CPi, comparing the sufficiency coefficient CPi with sufficiency thresholds CPmin and CPmax, and marking the analysis areas i as a deficient area, a normal area or a rich area according to the comparison result;
when the number of the deficient areas is not one, the material analysis module generates inter-area allocation signals, and the material analysis module sends the deficient areas, the rich areas and the inter-area allocation signals to the allocation management module;
the material analysis module sends the normal region to the region management module through the medical big data platform, the region management module is used for carrying out stock analysis on the emergency medical articles of the regional hospital in the normal region: marking all hospitals such as the first-level hospitals and above in the normal area as analysis hospitals, performing correlation analysis on the analysis hospitals one by one to obtain a plurality of marking positions, inputting the marking positions into a hospital distribution model to perform distribution analysis of the analysis hospitals in the normal area, and marking the analysis hospitals as first-level central hospitals, second-level central hospitals or third-level central hospitals according to distribution analysis results;
carrying out material quantity matching on an analysis hospital and marking the analysis hospital as a deficient hospital, a normal hospital or a rich hospital according to a material quantity matching result;
when the number of the deficient hospitals is not one, the material analysis module generates a regional allocation signal, and the material analysis module sends the deficient hospitals, the rich hospitals and the regional allocation signal to the allocation management module;
the allocation management module is used for performing inter-regional allocation analysis on the deficient region and performing intra-regional allocation analysis on the deficient hospital.
In a preferred embodiment of the present invention, the inventory analysis of the emergency medical supplies in the management area by the material analysis module comprises: the total number of emergency medical items in analysis area i is labeled JYi, the total number of enrolled people in analysis area i is labeled RZi, the number of enrolled people in analysis area i between sixty and eighty years of age is labeled LSi, and the abundance coefficients CPi are calculated from JYi, RZi and LSi.
As a preferred embodiment of the present invention, the process of comparing the abundance coefficients CPi of the analysis area i with the abundance thresholds CPmin, CPmax comprises:
if the abundance coefficient CPi of the analysis area i is less than or equal to CPmin, indicating that the inventory of the emergency medical article corresponding to the analysis area i is unqualified, and marking the corresponding analysis area i as a deficient area;
if CPmin is less than CPi and less than CPmax, the inventory of the emergency medical articles corresponding to the analysis area i is qualified, and the corresponding analysis area i is marked as a normal area;
if the abundance coefficient CPi of the analysis area i is larger than or equal to CPmax, the inventory of the emergency medical articles corresponding to the analysis area i is qualified, and the corresponding analysis area i is marked as a rich area.
As a preferred embodiment of the present invention, the association analysis process includes: setting a distance threshold value r1, wherein r1 is a positive integer and has a unit of kilometer, taking the position of an analysis hospital as the center of a circle, drawing a circle by r1, marking the area surrounded by the obtained circular curve as a correlation area, acquiring the number of analysis hospitals in the correlation area, and if the number of the analysis hospitals in the correlation area is one, marking the analysis hospitals as independent hospitals and marking the positions of the independent hospitals as marking positions; if the number of the analysis hospitals in the association area is not one, all the analysis hospitals in the association area are marked as an association set, and the midpoint of the connection line of the two analysis hospital positions with the minimum straight line distance in the association set is marked as a mark position.
As a preferred embodiment of the present invention, the hospital distribution model comprises: marking the number of the marked positions as m, drawing a circle by taking the marked positions as the circle center and r1 as the radius, marking the obtained m circular curves as diffusion curves, setting a distance threshold value r2, wherein r2 is a positive integer and has the unit of kilometer, r2 is more than r1, gradually increasing the radius value of the diffusion curve to r2 with r1, marking the process of increasing the radius value of the diffusion curve as a diffusion process, judging that the diffusion process of the corresponding diffusion curve is terminated when two diffusion curves are intersected or the diffusion curve is intersected with the edge of a normal region in the diffusion process, and performing contact analysis on the diffusion curve when the diffusion process is terminated:
if the two diffusion curves are intersected, marking the analysis hospitals in the two diffusion curves as secondary central hospitals;
if the diffusion curve is intersected with the edge of the normal area, marking all analysis hospitals in the diffusion curve as three-level central hospitals;
if the radius value of the diffusion curve increases to r2 and the diffusion process is not terminated, the analysis hospitals within the diffusion curve are all labeled as primary central hospitals.
As a preferred embodiment of the present invention, the process of matching the quantity of materials for an analysis hospital includes: eighty percent of the total number of the emergency medical articles in the normal area is used for all the primary central hospitals, the secondary central hospitals and the tertiary central hospitals to be equally divided, twenty percent of the total number of the emergency medical articles in the normal area is used for all the primary central hospitals and the secondary central hospitals to be equally divided, and ten percent of the total number of the emergency medical articles in the normal area is used for all the primary central hospitals to be equally divided; obtaining material standard values WB of each analysis hospital after matching is completed, calculating to obtain material standard thresholds WBmin and WBmax through the material standard values, wherein WBmin is a minimum material standard threshold, WBmax is a maximum material standard threshold, marking the stock quantity of the materials of the analysis hospital as a stock value KC, and comparing the stock value KC with the material standard thresholds WBmin and WBmax:
if KC is less than or equal to WBmin, marking the corresponding analysis hospital as a deficient hospital;
if WBmin is less than KC and less than WBmax, marking the corresponding analysis hospital as a normal hospital;
if KC is larger than or equal to WBmax, the corresponding analysis hospital is marked as a rich hospital.
As a preferred embodiment of the present invention, after receiving the inter-region allocation signal, the allocation management module marks the deficient region as a deficient object and marks the enriched region as an enriched object; after receiving the regional allocation signal, the allocation management module marks the deficient hospitals as deficient objects and marks the rich hospitals as rich objects.
As a preferred embodiment of the present invention, the specific process of the allocation management module for performing material allocation analysis includes: selecting a deficient object as a selected object, acquiring the priority coefficients of the rich objects and the selected object, marking the rich object with the maximum priority coefficient as a matched object and matching the matched object with the selected object, wherein the matched object provides material supply for the selected object;
after the matching of the selected object is completed, selecting the next deficient object as the selected object to perform the matching of the rich objects until all the deficient objects complete the matching of the rich objects;
and when the matching times of the rich objects are equal to two, the corresponding rich objects do not need to be subjected to material allocation, and the rich objects with the second priority coefficients are selected as matching objects to be matched in the matching process, so that the process is repeated.
As a preferred embodiment of the present invention, the process of acquiring the priority coefficient of the rich object includes:
marking the shortest distance between the rich object edge and the selected object edge as rich and deficient distance;
if the deployment management module receives the rich area and the deficient area, marking the difference value between the rich object abundant coefficient CPi and the maximum abundant threshold value CPmax as abundant difference, and marking the ratio of the abundant difference to the deficient distance as the priority coefficient of the rich object;
if the allocation management module receives rich hospitals and deficient hospitals, the difference value of the inventory value KC of rich objects and the maximum material standard threshold value WBmax is marked as the abundant difference, and the ratio of the abundant difference to the abundant distance is marked as the priority coefficient of the rich objects.
Compared with the prior art, the invention has the beneficial effects that:
1. the material analysis module carries out the stock analysis of first aid medical article to each analysis region, and whether the number of people who combines material stock, regional interior head count and regional interior old person meets the requirement to the material stock in the region judges to can carry out the material allotment between the region according to the material stock condition, guarantee that the material stock homoenergetic of each region can satisfy emergent requirement.
2. The regional management module can carry out the analysis to the goods and materials inventory of each hospital in the region, can regard as a set with a plurality of hospitals that the distance is close through correlation analysis, then carry out distribution analysis to the mark position and can be to each hospital, the central distribution condition of hospital set in the region, to the analysis hospital that is located the center and does not have close hospital, improve its goods and materials matching quantity to accomplish the scientific distribution of goods and materials, guarantee that the goods and materials inventory of each analysis hospital in the normal region can all satisfy the first aid demand.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the emergency medical supplies management system based on big data includes a medical big data platform, and the medical big data platform is communicatively connected with a deployment management module, a material analysis module, and an area management module.
The material analysis module can analyze the stock of the emergency medical supplies in the management area and perform inter-area material allocation analysis for each area.
Stock analysis process: dividing the management area into a plurality of analysis areas i, i =1, 2, …, n is a positive integer, marking the total number of emergency medical articles in the analysis area i as JYi, the total number of registered population in the analysis area i as RZi, the number of registered population in the analysis area i between sixties and eighties as LSi, and calculating the analysis area by the formula
Obtaining the abundance coefficient CPi of the analysis area i, wherein alpha 1, alpha 2 and alpha 3 are all proportionality coefficients, and alpha 1 is more than alpha 2 and more than alpha 3 is more than 1; the abundance coefficient CPi is a numerical value reflecting the abundance degree of the emergency medical material inventory in the analysis region i, and the higher the numerical value of the abundance coefficient CPi is, the more abundant the emergency medical material inventory in the analysis region i is, the more medical treatment is passedThe data platform acquires the abundance thresholds CPmin and CPmax, wherein CPmin is the minimum abundance threshold, CPmax is the maximum abundance threshold, and the abundance coefficient CPi of the analysis area i is compared with the abundance thresholds CPmin and CPmax: if the abundance coefficient CPi of the analysis area i is less than or equal to CPmin, indicating that the inventory of the emergency medical article corresponding to the analysis area i is unqualified, and marking the corresponding analysis area i as a deficient area; if CPmin is less than CPi and less than CPmax, the inventory of the emergency medical articles corresponding to the analysis area i is qualified, and the corresponding analysis area i is marked as a normal area; if the abundance coefficient CPi of the analysis area i is larger than or equal to CPmax, the inventory of the emergency medical articles corresponding to the analysis area i is qualified, and the corresponding analysis area i is marked as a rich area.
When the number of the deficient areas is not one, the material analysis module generates an inter-area allocation signal, and the material analysis module sends the deficient areas, the rich areas and the inter-area allocation signal to the allocation management module.
The allocation management module receives the allocation signal between the areas and then performs material allocation analysis on the deficient area, the material allocation between the areas indicates that the material inventory between the analysis area and the analysis area is qualified by mutually allocating the material, and the material allocation process of the deficient area comprises the following steps: selecting a deficient area as a selected area, marking the nearest distance between the edge of the rich area and the edge of the selected area as a rich-deficient distance, marking the difference value between the abundance coefficient CPi of the rich area and the maximum abundance threshold CPmax as an abundance difference, marking the ratio of the abundance difference to the abundance distance as a priority coefficient of the rich area, analyzing the matching priority of the rich area and the selected area according to the abundant condition of material inventory and the convenience of material transportation, matching the rich area according to the value of the priority coefficient, marking the rich area with the largest priority coefficient as a matching area and matching the selected area, and providing material supply for the selected area by the matched matching area; after the matching of the selected area is completed, the next deficient area is selected as the selected area to match the rich area, and it should be noted that when the matching times of the rich area is equal to two, the corresponding rich area does not need to allocate materials, and the rich area can provide material supply for at most two deficient areas.
It should be noted that after all the deficient areas are matched, inventory analysis of the emergency medical supplies needs to be performed again through the material analysis module, if the number of the deficient areas is zero, it is indicated that the material inventory of all the analysis areas can meet the requirements after matching, and all the analysis areas are marked as normal areas; if the number of the deficient areas is not zero, the situation that the matched analysis areas with the material inventory not meeting the requirements exist, all the analysis areas except the deficient areas are marked as normal areas, a material analysis module generates an external aid signal and sends the external aid signal and the deficient areas to a mobile phone terminal of a manager through a medical big data platform, and the manager receives the external aid signal and then allocates the materials in the areas to the deficient areas to supplement medical materials.
The material analysis module sends the normal region to the regional management module through the medical big data platform, and the regional management module is used for carrying out stock analysis on the first aid medical goods of regional hospital to the normal region: marking all hospitals such as first-class first in the normal region and above as analysis hospitals, carrying out association analysis on the analysis hospitals one by one, analyzing whether the analysis hospitals have analysis hospitals with close distances or not by the purpose of association analysis, when the analysis hospitals with close distances exist, the actual emergency pressure of the corresponding analysis hospitals is smaller, and when the analysis hospitals without close distances do not exist, the actual emergency pressure of the corresponding analysis hospitals is larger, so that materials need to be inclined to the analysis hospitals independently existing in a certain region, and the process of association analysis comprises the following steps: setting a distance threshold value r1, wherein r1 is a positive integer and has a unit of kilometer, taking the position of an analysis hospital as the center of a circle, drawing a circle by r1, marking the area surrounded by the obtained circular curve as a correlation area, acquiring the number of analysis hospitals in the correlation area, and if the number of the analysis hospitals in the correlation area is one, marking the analysis hospitals as independent hospitals and marking the positions of the independent hospitals as marking positions; if the number of the analysis hospitals in the association area is not one, all the analysis hospitals in the association area are marked as an association set, and the midpoint of the connection line of the two analysis hospital positions with the minimum straight line distance in the association set is marked as a mark position.
Inputting the marked positions into a hospital distribution model for analyzing distribution analysis of the hospital in a normal area: marking the number of the marked positions as m, drawing a circle by taking the marked positions as the circle center and r1 as the radius, marking the obtained m circular curves as diffusion curves, setting a distance threshold value r2, wherein r2 is a positive integer and the unit is kilometer, r2 is more than r1, gradually increasing the radius value of the diffusion curves to r2 by r1, marking the process of increasing the radius value of the diffusion curves as a diffusion process, judging that the diffusion process of the corresponding diffusion curves is terminated when two diffusion curves are intersected or the diffusion curves are intersected with the edge of a normal area in the diffusion process, and performing contact analysis on the diffusion curves when the diffusion process is terminated: if the two diffusion curves are intersected, marking the analysis hospitals in the two diffusion curves as secondary central hospitals; if the diffusion curve is intersected with the edge of the normal area, marking all analysis hospitals in the diffusion curve as three-level central hospitals; if the radius value of the diffusion curve increases to r2 and the diffusion process is not terminated, the analysis hospitals within the diffusion curve are all labeled as primary central hospitals.
Matching the quantity of materials for the analysis hospital: eighty percent of the total number of the emergency medical articles in the normal area is used for all the primary central hospitals, the secondary central hospitals and the tertiary central hospitals to be equally divided, twenty percent of the total number of the emergency medical articles in the normal area is used for all the primary central hospitals and the secondary central hospitals to be equally divided, and ten percent of the total number of the emergency medical articles in the normal area is used for all the primary central hospitals to be equally divided; obtaining material standard values WB of each analysis hospital after matching is completed, obtaining material standard thresholds WBmin and WBmax through material standard value calculation, wherein WBmin is a minimum material standard threshold, WBmin =0.85 xWB, WBmax is a maximum material standard threshold, WBmax =1.15 xWB, marking the stock quantity of the materials of the analysis hospital as a stock value KC, and comparing the stock value KC with the material standard thresholds WBmin and WBmax: if KC is less than or equal to WBmin, marking the corresponding analysis hospital as a deficient hospital; if WBmin is less than KC and less than WBmax, marking the corresponding analysis hospital as a normal hospital; if KC is larger than or equal to WBmax, marking the corresponding analysis hospital as a rich hospital.
When the number of the deficient hospitals is not one, the material analysis module generates a regional internal allocation signal, and the material analysis module sends the deficient hospitals, the rich hospitals and the regional internal allocation signal to the allocation management module.
Allotment management module receives and carries out material allotment analysis to the deficient hospital after the regional allotment signal, and the material allotment between the region indicates that all analysis hospital material inventory in the normal region is qualified through the mutual allotment of accessible material between abundant hospital and the deficient hospital, and the material allotment process of the deficient hospital includes: selecting a deficient hospital as a selected hospital, marking the nearest distance between the edge of the rich hospital and the edge of the selected hospital as a rich distance, marking the difference value between the inventory value KC of the rich hospital and the maximum material standard threshold WBmax as a rich difference, marking the ratio of the rich difference to the rich difference as a priority coefficient of the rich hospital, analyzing the matching priority of the rich hospital and the selected hospital by combining the rich condition of material inventory and the convenience of material transportation, matching the rich hospital through the numerical value of the priority coefficient, marking the rich hospital with the maximum priority coefficient as a matching hospital and matching the selected hospital when the numerical value of the priority coefficient is higher, and providing material supply for the selected hospital by the matched matching hospital; after the matching of the selected hospital is completed, the next deficient hospital is selected as the selected hospital to carry out the matching of the enriched hospital, and it needs to be noted that when the matching times of the enriched hospital is equal to two, the corresponding enriched hospital does not carry out material allocation any more, and the enriched hospital can provide material supply for the two deficient hospitals at most.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: formula (II)
(ii) a ByA person skilled in the art collects multiple groups of sample data and sets corresponding abundance coefficients for each group of sample data; substituting the set abundance coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1, alpha 2 and alpha 3 which are respectively 3.65, 2.47 and 2.32;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and regarding the size of the coefficient, the size depends on the number of sample data and a corresponding abundant coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameters and the quantized numerical values is not influenced, for example, the abundance coefficient is in direct proportion to the numerical value of the inventory total amount;
when the system is used, the material analysis module analyzes the stock of emergency medical supplies in a management area and performs inter-area material allocation analysis for each area, the material analysis module sends a normal area to the area management module through the medical big data platform, the area management module is used for performing stock analysis on the emergency medical supplies in the area hospitals in the normal area, and the allocation management module is used for performing inter-area allocation analysis on the deficient area and performing intra-area allocation analysis on the deficient hospital.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. The emergency medical supply management system based on big data comprises a medical big data platform, and is characterized in that the medical big data platform is in communication connection with a deployment management module, a material analysis module and a region management module;
dividing the management area into a plurality of analysis areas i, i =1, 2, …, n is a positive integer, performing stock analysis on the emergency medical supplies in the analysis areas i by a material analysis module to obtain a sufficiency coefficient CPi, comparing the sufficiency coefficient CPi with sufficiency thresholds CPmin and CPmax, and marking the analysis areas i as a deficient area, a normal area or a rich area according to the comparison result;
when the number of the deficient areas is not one, the material analysis module generates inter-area allocation signals, and the material analysis module sends the deficient areas, the rich areas and the inter-area allocation signals to the allocation management module;
the material analysis module sends the normal region to the regional management module through the medical big data platform, and the regional management module is used for carrying out stock analysis on the first aid medical goods of regional hospital to the normal region: marking all hospitals such as the first-level hospitals and above in the normal area as analysis hospitals, performing correlation analysis on the analysis hospitals one by one to obtain a plurality of marking positions, inputting the marking positions into a hospital distribution model to perform distribution analysis of the analysis hospitals in the normal area, and marking the analysis hospitals as first-level central hospitals, second-level central hospitals or third-level central hospitals according to distribution analysis results;
carrying out material quantity matching on an analysis hospital and marking the analysis hospital as a deficient hospital, a normal hospital or a rich hospital according to a material quantity matching result;
when the number of the deficient hospitals is not one, the material analysis module generates a regional allocation signal, and the material analysis module sends the deficient hospitals, the rich hospitals and the regional allocation signal to the allocation management module;
the allocation management module is used for performing inter-regional allocation analysis on the deficient region and performing intra-regional allocation analysis on the deficient hospital.
2. The big data based emergency medical supplies management system of claim 1, wherein the inventory analysis of the emergency medical supplies in the management area by the supplies analysis module comprises: the total number of emergency medical items in the analysis area i is marked as JYi, the total number of registered people in the analysis area i is marked as RZi, the number of registered people in the analysis area i with the age range between sixty and eighty years is marked as LSi, and the filling coefficient CPi is obtained by calculating JYi, RZi and LSi.
3. The big-data based emergency medical product management system of claim 1, wherein the process of analyzing the abundance coefficients CPi of the area i to compare with abundance thresholds CPmin, CPmax comprises:
if the abundance coefficient CPi of the analysis area i is less than or equal to CPmin, indicating that the inventory of the emergency medical article corresponding to the analysis area i is unqualified, and marking the corresponding analysis area i as a deficient area;
if CPmin is less than CPi and less than CPmax, the inventory of the emergency medical articles corresponding to the analysis area i is qualified, and the corresponding analysis area i is marked as a normal area;
if the abundance coefficient CPi of the analysis area i is larger than or equal to CPmax, the inventory of the emergency medical articles corresponding to the analysis area i is qualified, and the corresponding analysis area i is marked as a rich area.
4. The big-data based emergency medical supply management system of claim 1, wherein the process of correlation analysis comprises: setting a distance threshold value r1, wherein r1 is a positive integer and has a unit of kilometer, taking the position of an analysis hospital as the center of a circle, drawing a circle by r1, marking the area surrounded by the obtained circular curve as a correlation area, acquiring the number of analysis hospitals in the correlation area, and if the number of the analysis hospitals in the correlation area is one, marking the analysis hospitals as independent hospitals and marking the positions of the independent hospitals as marking positions; if the number of the analysis hospitals in the association area is not one, all the analysis hospitals in the association area are marked as an association set, and the midpoint of the connection line of the two analysis hospital positions with the minimum straight line distance in the association set is marked as a mark position.
5. The big-data based emergency medical supplies management system of claim 4, wherein the hospital distribution model comprises a distribution analysis process for analyzing the distribution of the hospital in a normal area: marking the number of the marked positions as m, drawing a circle by taking the marked positions as the circle center and r1 as the radius, marking the obtained m circular curves as diffusion curves, setting a distance threshold value r2, wherein r2 is a positive integer and the unit is kilometer, r2 is more than r1, gradually increasing the radius value of the diffusion curves to r2 by r1, marking the process of increasing the radius value of the diffusion curves as a diffusion process, judging that the diffusion process of the corresponding diffusion curves is terminated when two diffusion curves are intersected or the diffusion curves are intersected with the edge of a normal area in the diffusion process, and performing contact analysis on the diffusion curves when the diffusion process is terminated:
if the two diffusion curves are intersected, marking the analysis hospitals in the two diffusion curves as secondary central hospitals;
if the diffusion curve is intersected with the edge of the normal area, marking all analysis hospitals in the diffusion curve as three-level central hospitals;
if the radius value of the diffusion curve increases to r2 and the diffusion process is not terminated, the analysis hospitals within the diffusion curve are all labeled as primary central hospitals.
6. The big data-based emergency medical supply management system according to claim 5, wherein the process of matching the quantity of materials to the analysis hospital comprises: eighty percent of the total number of the emergency medical articles in the normal area is used for all the primary central hospitals, the secondary central hospitals and the tertiary central hospitals to be equally divided, twenty percent of the total number of the emergency medical articles in the normal area is used for all the primary central hospitals and the secondary central hospitals to be equally divided, and ten percent of the total number of the emergency medical articles in the normal area is used for all the primary central hospitals to be equally divided; obtaining material standard values WB of each analysis hospital after matching is completed, calculating to obtain material standard thresholds WBmin and WBmax through the material standard values, wherein WBmin is a minimum material standard threshold, WBmax is a maximum material standard threshold, marking the stock quantity of the materials of the analysis hospital as a stock value KC, and comparing the stock value KC with the material standard thresholds WBmin and WBmax:
if KC is less than or equal to WBmin, marking the corresponding analysis hospital as a deficient hospital;
if WBmin is less than KC and less than WBmax, marking the corresponding analysis hospital as a normal hospital;
if KC is larger than or equal to WBmax, the corresponding analysis hospital is marked as a rich hospital.
7. The big data based emergency medical supplies management system of claim 1, wherein the deployment management module marks the starved area as starved and the rich area as rich after receiving the inter-area deployment signal; after receiving the regional allocation signal, the allocation management module marks the deficient hospitals as deficient objects and marks the rich hospitals as rich objects.
8. The big data based emergency medical supplies management system of claim 1, wherein the specific process of the allocation management module for performing the material allocation analysis comprises: selecting a deficient object as a selected object, acquiring the priority coefficients of the rich objects and the selected object, marking the rich object with the maximum priority coefficient as a matched object and matching the matched object with the selected object, wherein the matched object provides material supply for the selected object;
after the matching of the selected object is completed, selecting the next deficient object as the selected object to perform the matching of the rich objects until all deficient objects complete the matching of the rich objects;
and when the matching times of the rich objects are equal to two, the corresponding rich objects do not need to be subjected to material allocation, and the rich objects with the second priority coefficients are selected as matching objects to be matched in the matching process, so that the process is repeated.
9. The big-data based emergency medical supply management system of claim 8, wherein the rich-object priority coefficient acquisition process comprises:
marking the shortest distance between the rich object edge and the selected object edge as rich and deficient distance;
if the deployment management module receives the rich area and the deficient area, marking the difference value between the rich object abundance coefficient CPi and the maximum abundance threshold CPmax as abundance difference, and marking the ratio of the abundance difference to the abundance distance as the priority coefficient of the rich object;
if the allocation management module receives rich hospitals and deficient hospitals, the difference value of the inventory value KC of rich objects and the maximum material standard threshold value WBmax is marked as the abundant difference, and the ratio of the abundant difference to the abundant distance is marked as the priority coefficient of the rich objects.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115099760A (en) * | 2022-08-29 | 2022-09-23 | 广东中科凯泽信息科技有限公司 | Intelligent dangerous article detection and early warning method based on computer machine vision |
| CN115469176A (en) * | 2022-07-22 | 2022-12-13 | 国网河南省电力公司新乡供电公司 | Digital twin power grid risk assessment system based on data model |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104616093A (en) * | 2014-12-23 | 2015-05-13 | 北京航天控制仪器研究所 | Earthquake disaster rescue command and dispatch system and method |
| CN104750984A (en) * | 2015-03-20 | 2015-07-01 | 华南理工大学 | Method for selecting emergency medical care reaction path |
| CN106502999A (en) * | 2015-09-03 | 2017-03-15 | 阿里巴巴集团控股有限公司 | Evaluation and querying method and device of the article to the distribution that sends with charge free a little |
| US20170173262A1 (en) * | 2017-03-01 | 2017-06-22 | François Paul VELTZ | Medical systems, devices and methods |
| CN108206049A (en) * | 2017-12-29 | 2018-06-26 | 广东工业大学 | The distribution method and system of a kind of medical resource |
| CN110147870A (en) * | 2019-04-08 | 2019-08-20 | 合肥工业大学 | A kind of optimization method distributed for multiple no-manned plane task after earthquake disaster |
| CN111739618A (en) * | 2020-04-30 | 2020-10-02 | 卫宁健康科技集团股份有限公司 | Scheduling method, system, electronic device and storage medium for disaster site |
| CN112530559A (en) * | 2020-11-09 | 2021-03-19 | 河北工程大学 | Intelligent medical material allocation system for sudden public health event |
| CN113297785A (en) * | 2021-04-27 | 2021-08-24 | 河南工业大学 | Medical material emergency dispatching optimization method based on firework optimization algorithm |
| CN113689941A (en) * | 2021-09-16 | 2021-11-23 | 曜立科技(北京)有限公司 | Medical data treatment system with intelligent supervision |
| CN214897311U (en) * | 2021-03-12 | 2021-11-26 | 西安翻译学院 | Economic trade information bulletin device |
-
2022
- 2022-01-20 CN CN202210063784.7A patent/CN114649085B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104616093A (en) * | 2014-12-23 | 2015-05-13 | 北京航天控制仪器研究所 | Earthquake disaster rescue command and dispatch system and method |
| CN104750984A (en) * | 2015-03-20 | 2015-07-01 | 华南理工大学 | Method for selecting emergency medical care reaction path |
| CN106502999A (en) * | 2015-09-03 | 2017-03-15 | 阿里巴巴集团控股有限公司 | Evaluation and querying method and device of the article to the distribution that sends with charge free a little |
| US20170173262A1 (en) * | 2017-03-01 | 2017-06-22 | François Paul VELTZ | Medical systems, devices and methods |
| CN108206049A (en) * | 2017-12-29 | 2018-06-26 | 广东工业大学 | The distribution method and system of a kind of medical resource |
| CN110147870A (en) * | 2019-04-08 | 2019-08-20 | 合肥工业大学 | A kind of optimization method distributed for multiple no-manned plane task after earthquake disaster |
| CN111739618A (en) * | 2020-04-30 | 2020-10-02 | 卫宁健康科技集团股份有限公司 | Scheduling method, system, electronic device and storage medium for disaster site |
| CN112530559A (en) * | 2020-11-09 | 2021-03-19 | 河北工程大学 | Intelligent medical material allocation system for sudden public health event |
| CN214897311U (en) * | 2021-03-12 | 2021-11-26 | 西安翻译学院 | Economic trade information bulletin device |
| CN113297785A (en) * | 2021-04-27 | 2021-08-24 | 河南工业大学 | Medical material emergency dispatching optimization method based on firework optimization algorithm |
| CN113689941A (en) * | 2021-09-16 | 2021-11-23 | 曜立科技(北京)有限公司 | Medical data treatment system with intelligent supervision |
Non-Patent Citations (3)
| Title |
|---|
| F FIEDRICH 等: "Optimized resource allocation for emergency response after earthquake disasters", 《SAFETY SCIENCE》, vol. 35, no. 1, pages 41 - 57 * |
| 刘红: "一种物流仓储管理系统的设计与实现", 《科技视界》, no. 36, pages 228 * |
| 陈莹: "医院床位信息管理和分配系统的设计与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》, no. 1, pages 138 - 483 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115469176A (en) * | 2022-07-22 | 2022-12-13 | 国网河南省电力公司新乡供电公司 | Digital twin power grid risk assessment system based on data model |
| CN115099760A (en) * | 2022-08-29 | 2022-09-23 | 广东中科凯泽信息科技有限公司 | Intelligent dangerous article detection and early warning method based on computer machine vision |
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