CN115577900A - Emergency rescue scheduling recommendation method, system and storage medium - Google Patents
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Abstract
The invention provides an emergency rescue scheduling recommendation method, an emergency rescue scheduling recommendation system and a storage medium, wherein the method comprises the following steps: acquiring data, including: acquiring team data and team member personal information data; establishing a team recommendation model according to team data, and recommending an optimal rescue team according to disaster requirements based on the team recommendation model; establishing a team member recommendation model according to the personal information data, and recommending an optimal rescue team to screen optimal rescue personnel to go to a disaster point; and establishing a material transferring recommendation model, recommending rescue workers closest to the material point based on the material transferring recommendation model, taking materials from the material point, then, proceeding to a disaster point, and performing task feedback after disaster relief is completed. The invention solves the problems that the existing emergency rescue has low efficiency and is difficult to quickly form an optimal rescue scheme.
Description
Technical Field
The invention relates to the technical field of big data recommendation, in particular to an emergency rescue scheduling recommendation method, system and storage medium.
Background
With the rapid development of economic construction and urban construction in China, disaster accidents in the aspects of chemical engineering, traffic, buildings and the like frequently occur, and meanwhile, natural disaster accidents also have potential threats. Once a large disaster accident happens, great economic loss and casualties can be caused, and even severe influence can be generated. Therefore, the method has important practical significance and practical value for the research of the emergency rescue of the large disaster accident.
The existing rescue system greatly depends on professional fire rescue teams, but the number of the fire rescue teams is limited, and when the fire rescue teams are far away from an accident site, a lot of time is needed to arrive at the scene, so that the best rescue opportunity is lost. The civil rescue force is wide in distribution and can reach the site quickly, but the civil rescue is difficult to form a reasonable scheduling scheme, is difficult to aggregate quickly and has no feedback result.
Disclosure of Invention
The invention provides an emergency rescue scheduling recommendation method, system and storage medium, which are used for solving the problems that the existing emergency rescue is low in efficiency and an optimal rescue scheme is difficult to form quickly.
The invention provides an emergency rescue scheduling recommendation method, which comprises the following steps:
acquiring team data and team member personal information data;
establishing a team recommendation model according to team data, and recommending an optimal rescue team according to disaster requirements based on the team recommendation model;
establishing a team member recommendation model according to the personal information data, and recommending an optimal rescue team to screen optimal rescue personnel to go to a disaster point;
and establishing a material transferring recommendation model, recommending rescue workers closest to the material point based on the material transferring recommendation model, then taking materials from the material point, then sending to a disaster point, and performing task feedback after disaster relief is completed.
According to the emergency rescue scheduling recommendation method provided by the invention, the team data comprises the following steps: the location of the team base, the type of rescue the team excels in, and the material properties of the team base;
the team member personal information data includes: name, physical fitness evaluation, home address, common work address and rescue experience.
According to the emergency rescue scheduling recommendation method provided by the invention, a team recommendation model is established according to team data, and an optimal rescue team is recommended according to disaster demands based on the team recommendation model, and the method specifically comprises the following steps:
establishing a team portrait, and constructing the team portrait according to the team attributes;
establishing a response mechanism between a captain and a dispatcher through mobile application;
calculating the traffic capacity on the basis of the road distance, and dynamically adjusting the path planning based on the road network distance;
and calculating the time cost required by each response team member to reach the disaster occurrence point according to various attributes.
According to the emergency rescue scheduling recommendation method provided by the invention, the traffic capacity based on the road distance is calculated, and the path planning based on the road network distance is dynamically adjusted, and the method specifically comprises the following steps:
acquiring a dynamic factor and a static factor, and combining the dynamic factor and the static factor to output the road traffic capacity;
calculating an optimal passing path through an optimal path algorithm and sending the optimal passing path to the mobile application of each captain in real time;
and according to the planned optimal passing path, carrying out risk evaluation on the road passing capacity, and dynamically adjusting the path planning based on the road network distance.
According to the emergency rescue scheduling recommendation method provided by the invention, a team member recommendation model is established according to personal information data, and an optimal rescue team is recommended to screen optimal rescue personnel to go to a disaster point, and the method specifically comprises the following steps:
establishing a member portrait, and constructing the member portrait according to the personnel attributes;
the captain acquires the position information of the disaster occurrence point and the position information of the team member through mobile application;
determining the traffic capacity on the road basis according to the dynamic factors and the static factors, and dynamically adjusting the path planning based on the road network distance;
constructing a disaster matrix according to disaster conditions, and constructing a member matrix according to the member figures;
constructing a similarity matrix according to the disaster matrix and the team member matrix, and calculating the cosine similarity of each team member;
and ranking the calculated results of the team members according to the cosine similarity of the team members, and assembling the echelon rescue team.
According to the emergency rescue scheduling recommendation method provided by the invention, the building of the article-blending recommendation model, the recommending of the rescuers closest to the material point to go to the material point to take materials and then go to the disaster point based on the article-blending recommendation model, and the task feedback after the disaster relief is finished specifically comprise:
collecting material information and determining material attributes;
determining required equipment and quantity according to the response between the captain and the team member;
determining the traffic capacity on the road basis according to the dynamic factors and the static factors, and dynamically adjusting the path planning based on the road network distance;
and calculating the time cost required by each response team member to reach the disaster occurrence point, and planning an optimal material allocation path.
The invention also provides an emergency rescue scheduling recommendation system, which comprises:
a data acquisition module for acquiring data, comprising: team data and team member personal information data;
the team recommendation module is used for recommending an optimal rescue team according to disaster requirements;
the team member recommending module is used for screening the optimal rescue personnel to go to the disaster point according to the recommended optimal rescue team;
the material transferring recommendation module is used for recommending rescue workers closest to the material points to go to the material points to fetch materials and then go to disaster points;
and the result feedback module is used for performing task end feedback after disaster relief is finished.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the emergency rescue scheduling recommendation method.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the emergency rescue dispatch recommendation method as described in any of the above.
The invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the emergency rescue dispatch recommendation method as described in any one of the above.
According to the emergency rescue scheduling recommendation method, the emergency rescue scheduling recommendation system and the emergency rescue scheduling recommendation storage medium, an optimal rescue guidance scheme can be generated by establishing a team recommendation model, a team member recommendation model and a dispatching recommendation model, the most appropriate rescue team and rescue personnel are matched, and rescue goods are taken according to a planned optimal road network distance path and arrive at a disaster occurrence point for rescue. The best rescue scheme can be formed, the time is saved, and the emergency rescue efficiency is improved.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of an emergency rescue scheduling recommendation method according to the present invention;
fig. 2 is a second schematic flow chart of an emergency rescue scheduling recommendation method according to the present invention;
fig. 3 is a third schematic flow chart of an emergency rescue scheduling recommendation method according to the present invention;
fig. 4 is a fourth flowchart illustrating a method for scheduling and recommending emergency rescue according to the present invention;
FIG. 5 is a schematic diagram illustrating module connections of an emergency rescue dispatch recommendation system according to the present invention;
fig. 6 is a schematic structural diagram of an electronic device provided in the present invention.
Reference numerals:
110: a data acquisition module; 120: a team recommendation module; 130: a team member recommendation module; 140: a blending recommendation module; 150: a result feedback module; 610: a processor; 620: a communication interface; 630: a memory; 640: a communication bus.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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.
The emergency rescue dispatch recommendation method of the present invention is described below with reference to fig. 1 to 4, and the method includes:
s100, acquiring team data and team member personal information data;
s200, establishing a team recommendation model according to team data, and recommending an optimal rescue team according to disaster demands based on the team recommendation model;
s300, establishing a team member recommendation model according to the personal information data, and recommending an optimal rescue team to screen optimal rescue personnel to go to a disaster point;
s400, establishing a material mixing recommendation model, recommending rescue workers closest to a material point to go to the material point to take materials based on the material mixing recommendation model, then going to a disaster point, and performing task feedback after disaster relief is completed.
According to the invention, through establishing the recommendation model for teams, team members and supplies, resources can be integrated to form an optimal quick rescue scheme, appropriate rescue teams and rescue personnel are matched, and the required rescue supplies are taken quickly and arrive at disaster points for rescue, so that the time is saved and the rescue efficiency is improved.
Team data includes: team base locations, team great help types and team base material attributes;
the team member personal information data includes: name, physical fitness evaluation, home address, common work address and rescue experience.
According to the corresponding team data and the team member personal information data, the most suitable rescue team and rescue personnel can be matched according to different actual conditions, and an optimal rescue scheme is formed.
The method comprises the following steps of establishing a team recommendation model according to team data, recommending an optimal rescue team according to disaster demands based on the team recommendation model, and specifically comprising the following steps:
s201, establishing a team portrait, and establishing a team portrait according to the team attributes;
s202, establishing a response mechanism between a captain and a dispatcher through mobile application;
s203, calculating the traffic capacity on the basis of the road distance, and dynamically adjusting the path planning based on the road network distance;
and S204, calculating the time cost required by each response team member to reach the disaster occurrence point according to various attributes.
Calculating the traffic capacity on the basis of the road distance, and dynamically adjusting the path planning based on the road network distance, which specifically comprises the following steps:
acquiring a dynamic factor and a static factor, and combining the dynamic factor and the static factor to output the road traffic capacity;
calculating an optimal passing path through an optimal path algorithm and sending the optimal passing path to the mobile application of each captain in real time;
and according to the planned optimal passing path, carrying out risk assessment on the road passing capacity, and dynamically adjusting the path planning based on the road network distance.
The team portrait constructs a team portrait according to the team attributes, and the contents of the portrait include information such as the name of the team, the area where the team is good at rescue, and the position of the team.
The traffic capacity on the basis of the road distance is combined by static factors (terrain DEM data, high-precision road vector data and remote sensing earth surface coverage data) and dynamic factors (meteorological data), the traffic capacity of the road is output (weighted values are given to each road for risk assessment), and an optimal path algorithm Dijstra is used for calculating an optimal path and providing the optimal path to each captain APP in real time. The idea of the Dijstra algorithm is that the shortest path is found in each step, and all distances are updated in real time in each step, so that the shortest path is selected every time.
And performing risk assessment on the road traffic capacity on the basis of path planning. The traffic capacity of a road network changes, and is mainly influenced by road surface properties and weather conditions. According to the influence evaluation on the road traffic capacity, corresponding influence coefficients are set, influence factor lookup tables under different road surfaces and meteorological conditions are generated, and the road network distance-based path planning is dynamically adjusted by referring to the table 1.
Table 1 impact factor lookup table
When the time cost required for each response team member to reach the disaster occurrence point is calculated based on various attributes, the time cost is considered as a first priority factor in consideration of the fact that the threshold of the social rescue team itself is not high and professional rescues are all organized by fire fighting, and therefore, if the arrival times of the teams are the same, the team who is better at disaster rescue is considered.
According to personal information data, a team member recommendation model is established, an optimal rescue team is recommended to screen optimal rescue personnel to go to a disaster point, and the method specifically comprises the following steps:
s301, establishing a member portrait, and establishing the member portrait according to personnel attributes;
s302, the captain acquires the position information of the disaster occurrence point and the position information of the team member through mobile application;
s303, determining the traffic capacity on the road basis according to the dynamic factors and the static factors, and dynamically adjusting the path planning based on the road network distance;
s304, constructing a disaster matrix according to disaster conditions, and constructing a team member matrix according to the team member figure;
s305, constructing a similarity matrix according to the disaster matrix and the team member matrix, and calculating cosine similarity of each team member;
s306, ranking the calculated results of the team members according to the cosine similarity of the team members, and assembling the team members for rescue in a echelon.
And (3) constructing a member portrait according to personnel attributes, wherein portrait contents comprise names, ages, rescue skill systems, family addresses and common work address information.
The method comprises the steps of obtaining position information, when a disaster happens, enabling a team leader to carry out APP response on each team member through APPs, obtaining position information of the disaster happening point and the team member response point in real time, calculating according to time and working day which are not obtained, calculating according to the working day 9-00-18 by using a common working address, and calculating according to a family address at the rest time.
The traffic capacity on the basis of the road distance is obtained by combining static factors (terrain DEM data, high-precision road vector data and remote sensing earth surface coverage data) and dynamic factors (meteorological data), outputting the traffic capacity of the road (giving a weight value to each road for risk assessment), calculating an optimal traffic path by using an optimal path algorithm Dijstra and providing the optimal traffic path to a response team member APP in real time. The idea of the Dijstra algorithm is that the shortest path is found in each step, and all distances are updated in real time in each step, so that the shortest path is selected every time.
And carrying out risk assessment on the road traffic capacity on the basis of path planning. The traffic capacity of a road network changes, and is mainly influenced by road surface properties and weather conditions. According to the evaluation of the influence on the road traffic capacity, corresponding influence coefficients are set, and influence factor lookup tables under different road surfaces and meteorological conditions are generated to dynamically adjust the path planning based on the road network distance.
According to the disaster type, a team with rescue ability is matched, system configuration needs to be formed, and the time cost required for each response team member to reach a disaster occurrence point is calculated.
And constructing a disaster matrix according to disaster conditions, and constructing a team member matrix according to the team member figure.
First a matrix is constructed from the disaster, for example:
| kind of disaster | Cost of time | Experience of rescue | Skill requirements | The number of people in need | Physical quality |
| Fire hazard | 5 | 4 | 1、2、4 | 10 | 3 |
Wherein the time cost defines the skill number to rescue experience physical quality
5 minutes: 0-30min 1: fire-fighting 1: low 1, weaker
4 min-30 h 2, 2 medical treatment, 2 lower, 2 common
3 min: 1h-1.5h 3: logistics 3: good 3. Good
2: 1.5-2h 4: better
1 minute, more than 2 hours, 5: other 5: higher 5, good
From the team member representation, a matrix is constructed, for example:
and constructing a similarity matrix according to the disaster matrix and the team member matrix, and calculating by adopting cosine similarity.
For example:
since the social rescue team itself is not high in threshold and professional rescues are organized by fire fighting, time cost is divided by weight as a first priority factor.
Time cost: rescue experience: physical quality =7:2:1
Calculating cosine similarity
The cosine similarity of the demand to the team member 0000001 is
According to the method, the cosine similarity of each team member is 0000001=0.9863, 0000002=0.9914, 0000003=0.9935, 0000004=0.9996 and 0000005=0.9959
And ranking the calculated results of the team members according to the cosine similarity of the team members, and assembling a echelon rescue team.
And sorting the calculated results, dispatching the team members with the front rank according to the requirements, grouping and sorting according to the mastered skills, and respectively taking out the team members with the front rank according to a reasonable system to go to an accident scene for rescue.
If the team members need to go to the base to take goods and materials, the sequencing results need to be sequenced again according to the time cost of arriving at a goods and materials storage point, the team members with the top rank take the goods and materials, and the rest team members go to the site for rescue in advance.
After ranking in sequence, the team members serve as a team B and a team C to stand by at any time.
Establishing a material transferring recommendation model, recommending rescue workers closest to a material point to the material point and go to a disaster point after the materials are taken from the material point, and specifically comprising the following steps:
s401, collecting material information and determining material attributes;
s402, determining required equipment and quantity according to the response between the captain and the team member;
s403, determining the traffic capacity on the road basis according to the dynamic factors and the static factors, and dynamically adjusting the path planning based on the road network distance;
and S404, calculating the time cost required by each response team member to reach the disaster occurrence point, and planning an optimal material allocation path.
And describing all material information of each team according to the material attributes of the team.
For example:
the traffic capacity on the basis of the road distance is combined by static factors (terrain DEM data, high-precision road vector data and remote sensing earth surface coverage data) and dynamic factors (meteorological data), the traffic capacity of the road is output (weighted values are given to each road for risk assessment), and an optimal path algorithm Dijstra is used for calculating an optimal path and providing the optimal path to each captain APP in real time. The idea of the Dijstra algorithm is that the shortest path is found in each step, all distances are updated in real time in each step, and the shortest path is guaranteed to be selected in each step.
And carrying out risk assessment on the road traffic capacity on the basis of path planning. The traffic capacity of a road network changes, and is mainly influenced by road surface properties and weather conditions. According to the evaluation of the influence on the road traffic capacity, corresponding influence coefficients are set, and influence factor lookup tables under different road surfaces and meteorological conditions are generated to dynamically adjust the path planning based on the road network distance
In order to better understand real-time traffic conditions in a road network, a traffic model can be established, an empirical traffic model named as an idempotent model is used, and the traffic model can predict vehicle speed under traffic jam according to road section speed limit, length, number of lanes and number of vehicles under the conditions of limited road network capacity and uncertain traffic flow. The mathematical formula is the result of empirical curve fitting, and is shown in the following formula.
Power model:
in the formula: γ and ∈ are constants, γ =0.2261 and ∈ =0.01127. The model is a function T (d, c) for predicting the degree of traffic congestion based on two parameters, capacity (c) and total density (d) of the edges. The model outputs a coefficient of 0 to 1: t =1 shows that the vehicle can run smoothly without traffic jam; t =0 indicates a very congested state, and the reachability of the road is almost 0
The optimal passing path of the material dispatching vehicle can be recommended by combining the optimal path algorithm with the traffic model.
And calculating the time cost required by each response team member to reach the disaster occurrence point according to the distance. For example:
the selected team is determined according to the automatic recommendation (dispatcher) of the one-touch dispatcher-team, equipment scheduling is carried out from the team according to the demand, if the materials of the team are insufficient, the next team with the lowest time cost is recommended to be replenished, the optimal route is calculated through the Dijstra algorithm and the traffic model, and the material scheduling vehicle can directly run according to the given route.
According to the emergency rescue scheduling recommendation method, by establishing the team recommendation model, the team member recommendation model and the dispatching recommendation model, an optimal rescue guidance scheme can be generated, the most appropriate rescue team and rescue personnel are matched, and rescue goods and materials are taken according to the planned optimal road network distance path and are driven to disaster occurrence points for rescue. The optimal rescue scheme can be formed, the time is saved, and the emergency rescue efficiency is improved.
Referring to fig. 5, the present invention also discloses an emergency rescue scheduling recommendation system, which includes:
a data acquisition module 110 for acquiring data, comprising: team data and team member personal information data;
the team recommendation module 120 is used for recommending an optimal rescue team according to disaster requirements;
the team member recommending module 130 is used for screening the best rescue personnel to go to the disaster point according to the recommended best rescue team;
the article dispatching recommendation module 140 is used for recommending rescue workers closest to the article point to go to the article point to fetch articles and then go to the disaster point;
and the result feedback module 150 is used for performing task ending feedback after disaster relief is completed.
According to the emergency rescue scheduling recommendation system, the team recommendation model, the team member recommendation model and the dispatching recommendation model are established, an optimal rescue guidance scheme can be generated, the most appropriate rescue team and rescue personnel are matched, and rescue goods and materials are taken according to the planned optimal road network distance path and are driven to disaster occurrence points for rescue. The optimal rescue scheme can be formed, the time is saved, and the emergency rescue efficiency is improved.
Fig. 6 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 6: a processor (processor) 610, a communication Interface (Communications Interface) 620, a memory (memory) 630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. Processor 610 may invoke logic instructions in memory 630 to perform a rescue dispatch recommendation method comprising: acquiring team data and team member personal information data;
establishing a team recommendation model according to team data, and recommending an optimal rescue team according to disaster requirements based on the team recommendation model;
establishing a team member recommendation model according to the personal information data, and recommending an optimal rescue team to screen optimal rescue personnel to go to a disaster point;
and establishing a material transferring recommendation model, recommending rescue workers closest to the material point based on the material transferring recommendation model, then taking materials from the material point, then sending to a disaster point, and performing task feedback after disaster relief is completed.
In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer can execute the emergency rescue scheduling recommendation method provided by the above methods, and the method includes: acquiring team data and team member personal information data;
establishing a team recommendation model according to team data, and recommending an optimal rescue team according to disaster requirements based on the team recommendation model;
establishing a team member recommendation model according to personal information data, recommending an optimal rescue team, and screening optimal rescue personnel to go to a disaster point;
and establishing a material transferring recommendation model, recommending rescue workers closest to the material point based on the material transferring recommendation model, then taking materials from the material point, then sending to a disaster point, and performing task feedback after disaster relief is completed.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements a method for emergency rescue dispatch recommendation provided by the above methods, the method comprising: acquiring team data and team member personal information data;
establishing a team recommendation model according to team data, and recommending an optimal rescue team according to disaster requirements based on the team recommendation model;
establishing a team member recommendation model according to personal information data, recommending an optimal rescue team, and screening optimal rescue personnel to go to a disaster point;
and establishing a material transferring recommendation model, recommending rescue workers closest to the material point based on the material transferring recommendation model, taking materials from the material point, then, proceeding to a disaster point, and performing task feedback after disaster relief is completed.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An emergency rescue dispatch recommendation method, the method comprising:
acquiring team data and team member personal information data;
establishing a team recommendation model according to team data, and recommending an optimal rescue team according to disaster requirements based on the team recommendation model;
establishing a team member recommendation model according to the personal information data, and recommending an optimal rescue team to screen optimal rescue personnel to go to a disaster point;
and establishing a material transferring recommendation model, recommending rescue workers closest to the material point based on the material transferring recommendation model, then taking materials from the material point, then sending to a disaster point, and performing task feedback after disaster relief is completed.
2. The emergency rescue dispatch recommendation method of claim 1, wherein the team data comprises: the location of the team base, the type of rescue the team excels in, and the material properties of the team base;
the team member personal information data includes: name, physical fitness evaluation, home address, common work address and rescue experience.
3. The emergency rescue scheduling recommendation method according to claim 1, wherein the establishing of the team recommendation model according to team data, and the recommending of the optimal rescue team according to disaster needs based on the team recommendation model specifically comprise:
establishing a team portrait, and constructing the team portrait according to the team attributes;
establishing a response mechanism between a captain and a dispatcher through a mobile application;
calculating the traffic capacity on the basis of the road distance, and dynamically adjusting the path planning based on the road network distance;
and calculating the time cost required by each response team member to reach the disaster occurrence point according to various attributes.
4. The emergency rescue scheduling recommendation method according to claim 3, wherein the calculating of the traffic capacity based on the road distance and the dynamic adjustment of the path planning based on the road network distance specifically comprises:
acquiring a dynamic factor and a static factor, and combining the dynamic factor and the static factor to output the road traffic capacity;
calculating an optimal passing path through an optimal path algorithm and sending the optimal passing path to the mobile application of each captain in real time;
and according to the planned optimal passing path, carrying out risk assessment on the road passing capacity, and dynamically adjusting the path planning based on the road network distance.
5. The emergency rescue scheduling recommendation method according to claim 1, wherein the establishing of a team member recommendation model according to the personal information data and the recommending of the optimal rescue team for screening the optimal rescue personnel to go to a disaster point specifically comprise:
establishing a member portrait, and constructing the member portrait according to personnel attributes;
the captain acquires the position information of the disaster occurrence point and the position information of the team member through mobile application;
determining the traffic capacity on the road basis according to the dynamic factors and the static factors, and dynamically adjusting the path planning based on the road network distance;
constructing a disaster matrix according to disaster conditions, and constructing a member matrix according to the member figures;
constructing a similarity matrix according to the disaster matrix and the team member matrix, and calculating the cosine similarity of each team member;
and ranking the calculated results of the team members according to the cosine similarity of the team members, and assembling a echelon rescue team.
6. The emergency rescue scheduling recommendation method according to claim 1, wherein the building of a cargo allocation recommendation model, recommending that a rescue worker closest to a cargo point goes to the cargo point to pick up the cargo based on the cargo allocation recommendation model, then goes to a disaster point, and performing task feedback after disaster relief is completed, specifically comprises:
collecting material information and determining material attributes;
determining required equipment and quantity according to the response between the captain and the team member;
determining the traffic capacity on the road basis according to the dynamic factors and the static factors, and dynamically adjusting the path planning based on the road network distance;
and calculating the time cost required by each response team member to reach the disaster occurrence point, and planning an optimal material allocation path.
7. An emergency rescue dispatch recommendation system, the system comprising:
a data acquisition module for acquiring data, comprising: team data and team member personal information data;
the team recommendation module is used for establishing a team recommendation model according to team data and recommending an optimal rescue team according to disaster demands based on the team recommendation model;
the team member recommendation module is used for establishing a team member recommendation model according to the personal information data and recommending an optimal rescue team to screen optimal rescue personnel to go to a disaster point;
the object transferring recommendation module is used for establishing an object transferring recommendation model, recommending rescue workers closest to the object point based on the object transferring recommendation model, taking the object from the object point, and then, moving the object point to the disaster point;
and the result feedback module is used for performing task end feedback after disaster relief is completed.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the emergency rescue dispatch recommendation method of any one of claims 1 to 6.
9. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the emergency rescue dispatch recommendation method of any one of claims 1-6.
10. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the emergency rescue dispatch recommendation method of any of claims 1-6.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115796847A (en) * | 2023-02-10 | 2023-03-14 | 成都秦川物联网科技股份有限公司 | Intelligent gas maintenance personnel management method, internet of things system and medium |
| CN116957302A (en) * | 2023-09-20 | 2023-10-27 | 深圳市福生泰消防科技有限公司 | Fire emergency response method and system |
| CN117273348A (en) * | 2023-09-25 | 2023-12-22 | 北方工业大学 | Important emergency team dispatching system and method |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115796847A (en) * | 2023-02-10 | 2023-03-14 | 成都秦川物联网科技股份有限公司 | Intelligent gas maintenance personnel management method, internet of things system and medium |
| CN115796847B (en) * | 2023-02-10 | 2023-05-09 | 成都秦川物联网科技股份有限公司 | Intelligent gas maintenance personnel management method, internet of things system and medium |
| US11989700B2 (en) | 2023-02-10 | 2024-05-21 | Chengdu Qinchuan Iot Technology Co., Ltd. | Management methods and management internet of things systems for maintenance personnel of smart gas |
| CN116957302A (en) * | 2023-09-20 | 2023-10-27 | 深圳市福生泰消防科技有限公司 | Fire emergency response method and system |
| CN116957302B (en) * | 2023-09-20 | 2023-12-12 | 深圳市福生泰消防科技有限公司 | Fire emergency response method and system |
| CN117273348A (en) * | 2023-09-25 | 2023-12-22 | 北方工业大学 | Important emergency team dispatching system and method |
| CN117273348B (en) * | 2023-09-25 | 2024-03-05 | 北方工业大学 | Important emergency team dispatching system and method |
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