CN116046001A - Rescue path planning method and system based on intelligent fire fighting - Google Patents
Rescue path planning method and system based on intelligent fire fighting Download PDFInfo
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Abstract
The invention provides a rescue path planning method and system based on intelligent fire protection, which relate to the technical field of path planning, and are used for constructing an urban road topological graph, determining a plurality of adaptive rescue scheduling points based on rescue target information, acquiring a plurality of groups of feasible rescue paths, acquiring traffic flow to acquire a plurality of groups of road flow information, constructing an emergency traffic evaluation model to carry out emergency traffic evaluation on the road traffic information, acquiring an emergency traffic evaluation result, further carrying out path optimization to determine an optimal rescue path, solving the technical problems that the current planning method is insufficient in intelligence and incomplete in analysis flow, the accuracy of the planned path is insufficient due to lower reference data dimension, the real-time matching performance and the rescue timeliness of the road of the rescue path cannot be guaranteed, and the intelligent optimal rescue path planning is realized and the rescue timeliness is guaranteed by determining a plurality of feasible rescue paths and carrying out path optimization based on multidimensional influence factors.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a rescue path planning method and system based on intelligent fire fighting.
Background
Along with the rapid development of economy and urbanization, the crowd gathering degree is more and more dense, correspondingly, the possibility of fire occurrence among different activities is increased, the traffic travel diversity causes frequent occurrence of blocking conditions on roads, so that when the fire occurs, the timely police dispatch and rescue of a fire team are greatly hindered, the life and property safety of people is ensured to the maximum extent, the police dispatch time limit is compressed to the maximum extent, therefore, the route determination is carried out mainly through modes such as platform recommendation, map planning and the like, but due to the fact that the reference data dimension is less, the accuracy and the preference of the recommended route have certain deviation, and further improvement and optimization are required.
In the prior art, when a fire rescue path is planned, the current planning method is insufficient in intelligence and incomplete in analysis flow, and due to the fact that the reference data dimension is low, the planning path is insufficient in accuracy, and real-time road matching and rescue timeliness of the rescue path cannot be guaranteed.
Disclosure of Invention
The application provides a rescue path planning method and system based on intelligent fire control, which are used for solving the technical problems that when the fire control rescue path planning is carried out in the prior art, the current planning method is insufficient in intelligence, the analysis flow is incomplete, the planning path accuracy is insufficient due to lower reference data dimension, and the real-time road matching and rescue timeliness of the rescue path cannot be guaranteed.
In view of the above problems, the application provides a rescue path planning method and system based on intelligent fire protection.
In a first aspect, the present application provides a rescue path planning method based on intelligent fire protection, the method including:
the associated urban traffic management system invokes urban road distribution information to construct an urban road topological graph;
acquiring rescue target information;
determining a plurality of adaptive rescue scheduling points based on the rescue target information;
determining a plurality of groups of feasible rescue paths of the adaptive rescue scheduling points based on the urban road topological graph;
acquiring traffic flow of the plurality of groups of feasible rescue paths to acquire a plurality of groups of road flow information;
constructing an emergency traffic assessment model to carry out emergency traffic assessment on the multiple sets of road flow information, and obtaining an emergency traffic assessment result;
and carrying out traffic path optimization on the plurality of groups of feasible rescue paths based on the emergency traffic evaluation result, and determining an optimal rescue path.
In a second aspect, the present application provides an intelligent fire protection based rescue path planning system, the system comprising:
the topological graph construction module is used for calling urban road distribution information by the associated urban traffic management system to construct an urban road topological graph;
the information acquisition module is used for acquiring rescue target information;
the scheduling point determining module is used for determining a plurality of adaptive rescue scheduling points based on the rescue target information;
the path determining module is used for determining a plurality of groups of feasible rescue paths of the adaptive rescue scheduling points based on the urban road topological graph;
the traffic acquisition module is used for acquiring traffic flow of the plurality of groups of feasible rescue paths and acquiring a plurality of groups of road traffic information;
the traffic evaluation module is used for constructing an emergency traffic evaluation model to perform emergency traffic evaluation on the multiple groups of road flow information and obtain an emergency traffic evaluation result;
the path optimizing module is used for optimizing the traffic paths of the plurality of groups of feasible rescue paths based on the emergency traffic evaluation result and determining an optimal rescue path.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the rescue path planning method based on intelligent fire control, an associated urban traffic management system invokes urban road distribution information, builds an urban road topological graph, acquires rescue target information to determine a plurality of adaptive rescue scheduling points, determines a plurality of groups of feasible rescue paths of the adaptive rescue scheduling points based on the urban road topological graph, and acquires a plurality of groups of road flow information by traffic flow collection; an emergency passage evaluation model is constructed to perform emergency passage evaluation on the multiple sets of road flow information, an emergency passage evaluation result is obtained, then the multiple sets of feasible rescue paths are subjected to passage path optimization, and an optimal rescue path is determined, so that the technical problems that the current planning method is insufficient in intelligence and the analysis flow is incomplete, the real-time road matching and rescue timeliness of the rescue paths cannot be guaranteed due to the fact that the reference data dimension is low in the prior art are solved, the path optimization is performed based on multidimensional influence factors through determining the multiple feasible rescue paths, intelligent optimal rescue path planning is realized, and the rescue timeliness is guaranteed.
Drawings
Fig. 1 is a schematic flow chart of a rescue path planning method based on intelligent fire protection;
fig. 2 is a schematic diagram of an emergency passage evaluation model construction flow in a rescue path planning method based on intelligent fire protection;
fig. 3 is a schematic diagram of a path switching flow in a rescue path planning method based on intelligent fire protection;
fig. 4 is a schematic structural diagram of a rescue path planning system based on intelligent fire protection.
Reference numerals illustrate: the system comprises a topology diagram construction module 11, an information acquisition module 12, a scheduling point determination module 13, a path determination module 14, a flow acquisition module 15, a traffic evaluation module 16 and a path optimizing module 17.
Detailed Description
According to the rescue path planning method and system based on intelligent fire control, an urban road topological graph is built, a plurality of adaptive rescue scheduling points are determined based on rescue target information, a plurality of groups of feasible rescue paths are obtained, traffic flow collection is carried out, a plurality of groups of road flow information is obtained, an emergency traffic assessment model is built to carry out emergency traffic assessment on the road traffic information, an emergency traffic assessment result is obtained, and then path optimization is carried out to determine an optimal rescue path.
Example 1
As shown in fig. 1, the present application provides a rescue path planning method based on intelligent fire protection, which includes:
step S100: the associated urban traffic management system invokes urban road distribution information to construct an urban road topological graph;
specifically, along with the rapid development of economy and urbanization, the crowd gathering degree is more and more dense, correspondingly, the possibility of fire occurrence between different activities is increased, the road is always blocked due to the diversity of traffic traveling, the timely police-out rescue of a fire-fighting team is greatly hindered when the fire occurs, the life and property safety of people are guaranteed to the maximum extent, the time-out time limit is compressed, and therefore, the fire-out rescue path planning is extremely important.
Firstly, associating the urban traffic management system, integrating a main control platform for urban traffic management, storing complete traffic information, determining urban traffic distribution information based on the urban traffic management system, including multiple path directions, control sections and the like, constructing the urban road topology map by scaling, marking the urban road topology map based on road section control live, for example, carrying out subsequent traffic path analysis based on the urban road topology map, and providing theoretical support.
Step S200: acquiring rescue target information;
step S300: determining a plurality of adaptive rescue scheduling points based on the rescue target information;
specifically, a fire disaster occurrence point is taken as a rescue target, a position of the rescue target is positioned to determine a rescue position, fire disaster live information is acquired based on an information acquisition device, such as an image acquisition device, a smoke alarm device and the like, an analysis and evaluation are performed on an information acquisition result to determine a rescue range and a rescue grade, the rescue position, the rescue range and the rescue grade are taken as rescue target information, further, an area is defined based on the rescue position to determine a rescue scheduling area, a plurality of rescue scheduling points in the rescue scheduling area are screened and removed based on the rescue range and the rescue grade, a rescue scheduling point matched with the fire disaster live condition is determined to be taken as the plurality of adaptive rescue scheduling points, and the acquisition of the plurality of adaptive rescue scheduling points provides a basic basis for the subsequent rescue path planning and preference.
Further, the step S300 of determining a plurality of adaptive rescue scheduling points based on the rescue target information further includes:
step S310: dividing areas based on rescue positions to determine rescue scheduling areas;
step S320: determining a plurality of rescue scheduling points based on the rescue scheduling area;
step S330: and taking the rescue range and the rescue grade as screening standards, screening the plurality of rescue scheduling points, and obtaining the plurality of adaptive rescue scheduling points.
Specifically, the rescue target information is used for determining a rescue position, urban area division is carried out based on the rescue position, the rescue scheduling area is determined, a square circle of one kilometer is used as the rescue scheduling area, the rescue scheduling area can be adjusted according to the number of rescue scheduling points, for example, when the number of the rescue scheduling points in the rescue scheduling area is large, the area range can be properly reduced, planning efficiency is improved on the basis of guaranteeing preference, the rescue scheduling points in the rescue scheduling area are further positioned, the plurality of rescue scheduling points are obtained, fire influence degree is judged, the rescue range and the rescue grade are determined through fire live information acquisition, the rescue scheduling points which reach the rescue standard in the plurality of rescue scheduling points are determined as screening standards, the plurality of adaptive rescue scheduling points are obtained, and preferential analysis is carried out in the plurality of adaptive rescue scheduling points, so that invalid work is avoided.
Step S400: determining a plurality of groups of feasible rescue paths of the adaptive rescue scheduling points based on the urban road topological graph;
step S500: acquiring traffic flow of the plurality of groups of feasible rescue paths to acquire a plurality of groups of road flow information;
specifically, the plurality of adaptive rescue scheduling points are determined through rescue scheduling point analysis, the plurality of adaptive rescue scheduling points and the rescue target information are located in the urban road topological graph, a rescue target is used as a target node, the plurality of adaptive rescue scheduling points are respectively used as initial nodes, path connection between the initial nodes and the target node is performed based on the trend of a plurality of roads in the urban road topological graph, a plurality of planning paths possibly exist between a single initial node and the target node and are used as a set of feasible rescue paths, the plurality of sets of feasible rescue paths are obtained, and the plurality of sets of feasible rescue paths are all normal traffic paths excluding uncontrollable factors such as road construction and the like and are selected from the plurality of sets of feasible rescue paths.
Further, real-time monitoring image retrieval is carried out on the traffic sections corresponding to the multiple groups of feasible rescue paths based on road monitoring, a current time node is determined, information identification is carried out on the real-time monitoring images, traffic flow of the corresponding road sections is determined, road flow information is obtained, association corresponding identification is carried out on the road flow information, the time node and the rescue paths, information identification analysis is carried out directly and conveniently, and information disorder of multiple association paths is avoided.
Step S600: constructing an emergency traffic assessment model to carry out emergency traffic assessment on the multiple sets of road flow information, and obtaining an emergency traffic assessment result;
step S700: and carrying out traffic path optimization on the plurality of groups of feasible rescue paths based on the emergency traffic evaluation result, and determining an optimal rescue path.
The emergency passage assessment model is an auxiliary virtual tool for path passage assessment analysis, accuracy and objectivity of analysis results can be effectively guaranteed, the multiple sets of road flow information and the multiple sets of feasible rescue paths are input into the emergency passage assessment model, multiple passage section assessment results of all the feasible rescue paths are determined through model matching analysis, further assessment results are integrated to determine assessment results corresponding to the rescue paths and output the assessment results as the emergency passage assessment results, the emergency passage assessment results are in one-to-one correspondence with the multiple sets of feasible rescue paths, a rescue path fitness function is constructed, the shortest passage time limit is used as a function corresponding target, the passage time limit of each path in the multiple sets of feasible rescue paths is determined based on the emergency passage assessment results, the feasible rescue path corresponding to the smallest time limit is used as the optimal rescue path, rescue scheduling points are determined based on the optimal rescue path, fire rescue is timely carried out, and the life safety of the masses is guaranteed to the greatest extent.
Further, the step S600 of constructing an emergency traffic assessment model performs an emergency traffic assessment on the plurality of sets of road traffic information to obtain an emergency traffic assessment result, and the step S600 of the present application further includes:
step S610: constructing the emergency passage assessment model based on a machine learning algorithm;
step S620: and inputting the multiple sets of road flow information and the multiple sets of feasibility rescue paths into the emergency passage assessment model, and analyzing and outputting the emergency passage assessment result by using multi-layer data.
Specifically, the emergency traffic assessment model is an auxiliary decision tool for carrying out path traffic analysis, whether a planned path can normally pass or not is judged, a traffic jam condition exists, and preferably, the emergency traffic assessment model can be a multi-level network layer and comprises a data identification layer, a matching assessment layer and a result output layer, a regional traffic prediction module is embedded in the matching assessment layer, along with the transition of real-time traffic time and used for carrying out road traffic prediction of a real-time path traffic section, model analysis accuracy is improved, multiple groups of road traffic information and multiple groups of feasible rescue paths are input into the emergency traffic assessment model, two groups of data are identified and corresponding based on the data identification layer, an identification result is transmitted into the matching assessment layer, path matching and assessment prediction are carried out, a prediction result is obtained, integrated processing is carried out, the assessment result of each feasible path is determined, and the accuracy and objectivity of the assessment result can be effectively ensured by carrying out path analysis assessment through the construction model.
Further, as shown in fig. 2, the machine learning algorithm based construction of the emergency traffic assessment model, step S610 of the present application further includes:
step S611: constructing the emergency passage evaluation model framework;
step S612: performing time periodic division to determine a multi-level time division interval;
step S613: based on the urban traffic management system, historical traffic flow information is called according to the multi-level time division intervals, and a plurality of groups of historical road flow information are obtained;
step S614: carrying out traffic evaluation on the multiple groups of historical road flow information to obtain a historical traffic evaluation result;
step S615: and performing model optimization based on the historical traffic evaluation result to generate the emergency traffic evaluation model.
The emergency traffic assessment model framework is constructed and is a multi-level network layer, and comprises a data identification layer, a matching assessment layer and a result output layer, the quarterly, weather, workdays, holidays and the like are used as level division basis, time is periodically divided, the multi-level time division sections are obtained, the division basis is in close relation with road traffic, for example, the time nodes of the same road section are different in road traffic, the holidays are different from the road traffic of the workdays, a data retrieval time zone, namely, a time interval for historical data retrieval is further set, the multi-level time division sections are used as data retrieval standards based on the urban traffic management system, namely, a general control system for urban traffic management, historical traffic information retrieval is carried out based on the data retrieval time zone, time identification and road section identification are carried out on the retrieved traffic information, a plurality of sets of historical channel traffic information are generated, the plurality of sets of historical road traffic information are carried out passable assessment, traffic smoothness is judged, and a judgment result is used as the historical evaluation result.
Furthermore, the plurality of groups of history channel flow information are used as matching nodes, the history pass evaluation results are used as decision nodes, the history pass evaluation results and the decision nodes are correspondingly connected, the history pass evaluation results are input into the emergency pass evaluation model framework for model optimization, the constructed emergency pass evaluation model is obtained, the matching of the model output result and the actual pass condition can be effectively ensured, and the follow-up path planning accuracy is improved.
Further, step S620 of the present application further includes:
step S621: constructing an area traffic prediction module, wherein the area traffic prediction module is embedded in the emergency traffic assessment model;
step S622: determining a regional traffic time node based on the plurality of groups of feasibility rescue paths according to the regional traffic prediction module;
step S623: performing node flow matching on the regional transit time nodes to serve as a regional transit prediction result;
step S624: and acquiring the emergency traffic assessment result based on the regional traffic prediction result.
Specifically, the regional traffic prediction module is built, the regional traffic prediction module is embedded into a matching evaluation layer of the emergency traffic evaluation model, the regional traffic prediction module is a sub-evaluation layer, the current traffic time node and the traffic average speed can be used for evaluating and predicting a rescue path to be analyzed, the arrival time of each traffic section of the rescue path relative to the initial traffic time is determined, and the traffic prediction is carried out based on the arrival time node and the reference historical traffic flow.
Further, based on the regional traffic prediction module, the traffic time of the different traffic road sections is respectively determined for the multiple groups of feasible rescue paths, the regional traffic time nodes are obtained, the regional traffic time nodes are used as the matching nodes to perform node traffic flow matching, the flow matching result is determined, the regional traffic condition prediction is performed based on the decision nodes corresponding to the flow matching result, the regional traffic prediction result is obtained, the integration and summarization of the regional traffic prediction results are respectively performed for the multiple groups of feasible rescue paths, the emergency traffic evaluation result is generated, and the accuracy and the instantaneity of the prediction result can be effectively improved.
Further, the step S700 of determining an optimal rescue path based on the emergency pass evaluation result to perform pass path optimization on the plurality of sets of feasible rescue paths further includes:
step S710: constructing a rescue path fitness function by taking the minimum passing time limit as a response target;
step S720: determining a plurality of adaptation values according to the plurality of groups of feasible rescue paths based on the rescue path adaptation degree function;
step S730: performing correction and sequencing on the plurality of adaptation values to obtain an adaptation value sequence;
step S740: and determining an optimal adaptation value based on the adaptation value sequence, and carrying out reverse matching on the optimal adaptation value to obtain the optimal rescue path.
Specifically, based on the emergency passage evaluation model, performing evaluation analysis of the plurality of groups of feasible rescue paths, obtaining the emergency passage evaluation result, wherein the emergency passage evaluation result corresponds to the plurality of groups of feasible rescue paths one by one, further taking the minimum passage time limit as a corresponding target, constructing the rescue path fitness function, performing fitness analysis on the plurality of groups of feasible rescue paths to perform preferential selection, performing passage time limit analysis on the plurality of groups of feasible rescue paths based on the rescue path fitness function, determining the plurality of adaptation values, wherein the adaptation values are inversely proportional to the passage time limit, further, sequentially arranging the plurality of adaptation values from large to small, generating the adaptation value sequence, determining that the first term is the optimal adaptation value based on the adaptation value sequence, performing reverse matching on the rescue path, determining that the corresponding rescue path is the optimal rescue path, and ensuring that the optimal rescue path is the minimum in blocking possibility and the minimum passage time limit.
Further, as shown in fig. 3, step S800 further includes:
step S810: if the optimal rescue path has an emergency, generating a path switching instruction;
step S820: based on the path switching instruction, determining a real-time passing node of the optimal rescue path based on the urban road topological graph;
step S830: re-planning the path by taking the real-time passing node as a starting point to obtain a secondary planning path;
step S840: determining a secondary optimal path according to the emergency passage assessment model based on the secondary planning path;
step S850: determining a second rescue path according to the adaptive value sequence based on the plurality of groups of feasible rescue paths;
step S860: and checking the second rescue path and the secondary optimal path to obtain a path to be switched.
Specifically, when an emergency such as an activity, a rear-end collision and other sudden uncontrollable factors occur in the passing process of the optimal rescue path, the path switching instruction is generated to ensure the timeliness of rescue, along with the receiving of the path switching instruction, an implementation passing node is determined, the position of the implementation passing node is positioned in the urban road topological graph, the implementation passing node is used as an initial node, a rescue target is used as a passing endpoint, path planning is conducted again, a plurality of passable paths between the initial node and the passing endpoint are determined, the two-time planning path is used as the two-time planning path, further, the two-time planning path is subjected to predictive evaluation based on the emergency passing evaluation model, an evaluation result is obtained, path optimizing is carried out, the two-time optimal path is obtained, further, the adaptive value sequence of the plurality of groups of feasible rescue paths is called, the rescue path corresponding to the second adaptive value is used as the second rescue path, the two-time optimal path is compared with the second rescue path, and the time limit shortest passing path is determined to be the path to be subjected to rescue, and the situation that the rescue is uncontrollable in the passing process is avoided.
Example two
Based on the same inventive concept as the rescue path planning method based on intelligent fire protection in the foregoing embodiments, as shown in fig. 4, the present application provides a rescue path planning system based on intelligent fire protection, where the system includes:
the topological graph construction module 11 is used for calling urban road distribution information by the associated urban traffic management system to construct an urban road topological graph;
an information acquisition module 12, wherein the information acquisition module 12 is used for acquiring rescue target information;
a scheduling point determining module 13, wherein the scheduling point determining module 13 is configured to determine a plurality of adaptive rescue scheduling points based on the rescue target information;
a path determining module 14, where the path determining module 14 is configured to determine a plurality of sets of feasible rescue paths of the adaptive rescue scheduling points based on the urban road topology map;
the flow acquisition module 15 is used for acquiring traffic flow of the plurality of groups of feasible rescue paths and acquiring a plurality of groups of road flow information;
the traffic evaluation module 16 is used for constructing an emergency traffic evaluation model to perform emergency traffic evaluation on the multiple sets of road flow information, and an emergency traffic evaluation result is obtained;
the path optimizing module 17 is configured to perform traffic path optimizing on the multiple sets of feasible rescue paths based on the emergency traffic evaluation result, and determine an optimal rescue path.
Further, the system further comprises:
the area determining module is used for dividing the area based on the rescue position and determining a rescue scheduling area;
the rescue scheduling point determining module is used for determining a plurality of rescue scheduling points based on the rescue scheduling area;
the scheduling point screening module is used for screening the plurality of rescue scheduling points by taking the rescue range and the rescue grade as screening standards to obtain the plurality of adaptive rescue scheduling points.
Further, the system further comprises:
the model construction module is used for constructing the emergency passage assessment model based on a machine learning algorithm;
and the result output module is used for inputting the multiple sets of road flow information and the multiple sets of feasibility rescue paths into the emergency passage assessment model, and analyzing and outputting the emergency passage assessment result by using multiple layers of data.
Further, the system further comprises:
the framework construction module is used for constructing the emergency passage assessment model framework;
the interval determining module is used for carrying out time periodic division and determining multi-level time division intervals;
the information calling module is used for calling historical traffic flow information according to the multi-level time division interval based on the urban traffic management system to obtain multiple groups of historical road flow information;
the historical information evaluation module is used for carrying out traffic evaluation on the plurality of groups of historical road flow information to obtain a historical traffic evaluation result;
the model generation module is used for carrying out model optimization based on the historical traffic evaluation result and generating the emergency traffic evaluation model.
Further, the system further comprises:
the prediction module construction module is used for constructing a regional traffic prediction module, wherein the regional traffic prediction module is embedded in the emergency traffic assessment model;
the time node determining module is used for determining a regional traffic time node based on the plurality of groups of feasibility rescue paths according to the regional traffic prediction module;
the prediction result determining module is used for carrying out node flow matching on the regional transit time nodes and is used as a regional transit prediction result;
the traffic evaluation result acquisition module is used for acquiring the emergency traffic evaluation result based on the regional traffic prediction result.
Further, the system further comprises:
the function construction module is used for constructing a rescue path fitness function by taking the minimum passing time limit as a response target;
the adaptation value determining module is used for determining a plurality of adaptation values according to the plurality of groups of feasible rescue paths based on the rescue path adaptation degree function;
the sequence acquisition module is used for performing proofreading and sequencing on the plurality of adaptation values to acquire an adaptation value sequence;
and the optimal rescue path acquisition module is used for determining an optimal adaptation value based on the adaptation value sequence, and carrying out reverse matching on the optimal adaptation value to acquire the optimal rescue path.
Further, the system further comprises:
the instruction generation module is used for generating a path switching instruction if the optimal rescue path has an emergency;
the passing node determining module is used for determining real-time passing nodes of the optimal rescue path based on the path switching instruction and the urban road topological graph;
the path re-planning module is used for re-planning the path by taking the real-time passing node as a starting point to acquire a secondary planning path;
the secondary optimal path determining module is used for determining a secondary optimal path according to the emergency traffic assessment model based on the secondary planning path;
the second rescue path determining module is used for determining a second rescue path according to the adaptive value sequence based on the plurality of groups of feasible rescue paths;
and the path to be switched acquires the path to be switched, wherein the path to be switched acquires the second rescue path and the secondary optimal path by checking.
Through the foregoing detailed description of a rescue path planning method based on intelligent fire protection, those skilled in the art can clearly know a rescue path planning method and a rescue path planning system based on intelligent fire protection in this embodiment, and for the device disclosed in the embodiment, the description is relatively simple because it corresponds to the method disclosed in the embodiment, and relevant places refer to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The rescue path planning method based on intelligent fire fighting is characterized by comprising the following steps:
the associated urban traffic management system invokes urban road distribution information to construct an urban road topological graph;
acquiring rescue target information;
determining a plurality of adaptive rescue scheduling points based on the rescue target information;
determining a plurality of groups of feasible rescue paths of the adaptive rescue scheduling points based on the urban road topological graph;
acquiring traffic flow of the plurality of groups of feasible rescue paths to acquire a plurality of groups of road flow information;
constructing an emergency traffic assessment model to carry out emergency traffic assessment on the multiple sets of road flow information, and obtaining an emergency traffic assessment result;
and carrying out traffic path optimization on the plurality of groups of feasible rescue paths based on the emergency traffic evaluation result, and determining an optimal rescue path.
2. The method of claim 1, wherein the determining a plurality of adaptive rescue dispatch points based on the rescue target information comprises:
dividing areas based on rescue positions to determine rescue scheduling areas;
determining a plurality of rescue scheduling points based on the rescue scheduling area;
and taking the rescue range and the rescue grade as screening standards, screening the plurality of rescue scheduling points, and obtaining the plurality of adaptive rescue scheduling points.
3. The method of claim 1, wherein constructing the emergency pass assessment model performs an emergency pass assessment on the plurality of sets of road traffic information, and obtaining an emergency pass assessment result comprises:
constructing the emergency passage assessment model based on a machine learning algorithm;
and inputting the multiple sets of road flow information and the multiple sets of feasibility rescue paths into the emergency passage assessment model, and analyzing and outputting the emergency passage assessment result by using multi-layer data.
4. The method of claim 3, wherein the constructing the emergency pass assessment model based on a machine learning algorithm comprises:
constructing the emergency passage evaluation model framework;
performing time periodic division to determine a multi-level time division interval;
based on the urban traffic management system, historical traffic flow information is called according to the multi-level time division intervals, and a plurality of groups of historical road flow information are obtained;
carrying out traffic evaluation on the multiple groups of historical road flow information to obtain a historical traffic evaluation result;
and performing model optimization based on the historical traffic evaluation result to generate the emergency traffic evaluation model.
5. A method as claimed in claim 3, comprising:
constructing an area traffic prediction module, wherein the area traffic prediction module is embedded in the emergency traffic assessment model;
determining a regional traffic time node based on the plurality of groups of feasibility rescue paths according to the regional traffic prediction module;
performing node flow matching on the regional transit time nodes to serve as a regional transit prediction result;
and acquiring the emergency traffic assessment result based on the regional traffic prediction result.
6. The method of claim 1, wherein the optimizing the traffic path for the plurality of sets of viable rescue paths based on the emergency traffic assessment results, determining an optimal rescue path, comprises:
constructing a rescue path fitness function by taking the minimum passing time limit as a response target;
determining a plurality of adaptation values according to the plurality of groups of feasible rescue paths based on the rescue path adaptation degree function;
performing correction and sequencing on the plurality of adaptation values to obtain an adaptation value sequence;
and determining an optimal adaptation value based on the adaptation value sequence, and carrying out reverse matching on the optimal adaptation value to obtain the optimal rescue path.
7. The method as recited in claim 6, comprising:
if the optimal rescue path has an emergency, generating a path switching instruction;
based on the path switching instruction, determining a real-time passing node of the optimal rescue path based on the urban road topological graph;
re-planning the path by taking the real-time passing node as a starting point to obtain a secondary planning path;
determining a secondary optimal path according to the emergency passage assessment model based on the secondary planning path;
determining a second rescue path according to the adaptive value sequence based on the plurality of groups of feasible rescue paths;
and checking the second rescue path and the secondary optimal path to obtain a path to be switched.
8. Rescue path planning system based on intelligent fire protection, characterized in that it comprises:
the topological graph construction module is used for calling urban road distribution information by the associated urban traffic management system to construct an urban road topological graph;
the information acquisition module is used for acquiring rescue target information;
the scheduling point determining module is used for determining a plurality of adaptive rescue scheduling points based on the rescue target information;
the path determining module is used for determining a plurality of groups of feasible rescue paths of the adaptive rescue scheduling points based on the urban road topological graph;
the traffic acquisition module is used for acquiring traffic flow of the plurality of groups of feasible rescue paths and acquiring a plurality of groups of road traffic information;
the traffic evaluation module is used for constructing an emergency traffic evaluation model to perform emergency traffic evaluation on the multiple groups of road flow information and obtain an emergency traffic evaluation result;
the path optimizing module is used for optimizing the traffic paths of the plurality of groups of feasible rescue paths based on the emergency traffic evaluation result and determining an optimal rescue path.
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