A highway emergency response system

Abstract

This invention belongs to the technical field of highway management systems, and particularly relates to an emergency response system for highway emergencies. It includes a highway network emergency response database, a highway emergency element basic data processing module, and a highway emergency resource support module. The highway network emergency response database includes a data server, a data integration unit, and a data sharing and conversion unit. The highway emergency element basic data processing module includes a basic data processing unit and a highway emergency network repair unit. This application is used for establishing, improving, and screening multiple emergency response plans for highway emergencies. It is suitable for the rapid optimization and generation of associated routes and nodes of emergencies, advance planning and configuration, or optimization of the allocation of existing emergency response resources, improving the coverage of emergency response resources, and optimizing the effectiveness of emergency response.

Description

Technical Field

[0001] This invention belongs to the technical field of highway management systems, and in particular relates to an emergency response system for highway emergencies. Background Technology

[0002] Thanks to the development of key technologies such as satellite remote sensing, GIS data acquisition, and data collection terminals, systems and methods for regional traffic control, accident early warning, and intervention have emerged. Various traffic data acquisition networks and information update and push systems have been widely applied to all aspects of urban traffic management. However, in practical applications, due to the large number of opposing or unidirectional lines in the traffic network, and the significant data and network discrepancies or even overlaps between different lines and nodes, while this phenomenon is beneficial for providing more accurate and complete basic data in daily traffic control and data maintenance, it limits the response efficiency of emergency systems and affects the rationality of emergency response plans during intervention and handling of emergencies. Because emergencies and the resulting traffic system states are highly variable and subject to complex and continuous changes, coupled with the unknown impacts of different environments and event attributes, current emergency response systems still largely rely on experts or temporary response teams for emergency handling. This means that the execution of existing emergency response plans is potentially influenced by the personal experience and cognitive biases of decision-makers within the decision-making team, which may affect the rationality of the emergency response plans. Summary of the Invention

[0003] The purpose of this invention is to provide a highway emergency response system that can optimize and repair the existing highway topology network, and more scientifically utilize effective node and line attribute data to serve emergency response plans, so as to improve and perfect the emergency response capability of highway emergencies more efficiently and scientifically, and optimize the response speed and performance of the emergency response system.

[0004] To achieve the above objectives, the present invention adopts the following technical solution.

[0005] An emergency response system for highway emergencies includes a highway network emergency response database, a basic data processing module for highway emergency elements, and a highway emergency resource support module.

[0006] The highway network emergency response database includes a data server, a data integration unit, and a data sharing and conversion unit.

[0007] The data integration unit establishes a database application service interface within the data server based on the WebService framework, registers with the data server using the WSDL network service description tool, and collects data from the database inside the data server using the SAAP protocol based on the data requests sent to the data server.

[0008] The data sharing and conversion unit establishes a data sharing and conversion model for heterogeneous databases based on data mapping. The heterogeneous databases include at least commonly used distributed databases, B / S architecture databases, and relational databases. Data mapping includes mapping according to the data exchange format between data paths to enable mutual conversion between different types of databases.

[0009] The basic data processing module for highway emergency elements includes a basic data processing unit and a highway emergency response network repair unit.

[0010] The basic data processing unit consists of fixed or mobile data ports within the highway emergency response network and data analysis tools. Based on these ports, the basic data processing unit collects highway status information, highway operational status information, and regional meteorological status information. The data analysis tools filter the raw data based on pre-defined highway emergency event attribute elements, removing invalid and redundant data, and then classify the filtered data. The highway status information includes at least the distribution and usage status information of highway emergency response equipment or facilities, and the allocation and node-based response resources for emergency response. The highway operational status information includes at least the highway traffic flow and road load information, and road congestion information within the time domain related to the emergency response. The regional meteorological status information includes at least regional wind direction and speed information, visibility information, temperature and air pressure information, and monitoring information for specific hazardous substance leaks.

[0011] The highway emergency response network repair unit includes a topology repair component configured at the output end of the urban highway network. This topology repair component uses a topology search tool to determine all nodes within the network and the lines connecting them to a given topology obtained during emergency response to a sudden event, based on a GIS topology map of the regional traffic network. It then acquires and optimizes the topology according to the following steps:

[0012] A. Traverse all nodes in the network topology to search for single-line nodes that are connected to only one line, and search for first-level lines in the network topology with single-line nodes as endpoints.

[0013] B. Traverse all first-level routes and record all intermediate nodes traversed by the routes; obtain all secondary routes with non-single-line nodes as endpoints.

[0014] C. Traverse the secondary paths. If a path contains only two nodes, continue searching the next secondary path.

[0015] D. If the secondary route contains more than two nodes, then based on the distance and connection relationship between each network node, analyze and judge all nodes traversed by the secondary route to determine the node type. The types include ordinary nodes, weak nodes, and associated nodes. Specific steps include:

[0016] d1. Delineate the search area with any node of the secondary route as the center and the search range dmax as the radius, and count the total number of other routes passing through the search area f1, where dmax is the maximum distance that causes the traffic data obtained by two closely spaced nodes in different routes to overlap.

[0017] d2. If f1 = 0, then the node is considered a normal node, and return to step c to continue searching for the next secondary path;

[0018] d3. If f1 = 1, then sequentially determine whether the distance between the two endpoints of the route passing through the search area and the node is greater than dmin, where dmin is the minimum distance that allows one of the traffic data obtained by two nodes in different routes to completely contain the other. If it is greater than dmin, then the node is considered a normal node; if it is less than or equal to dmin, then the node is considered a weak node.

[0019] d4. If f1 > 1, calculate the distance between the endpoint of the line passing through the search area and the node, and count the number of distances greater than dmin, where dmin is the minimum distance that ensures that the traffic data obtained by two nodes in different lines completely contain the other. If the number of distances greater than dmin is more than 1, the node is considered an associated node; if the number of distances greater than dmin is 1, the node is considered a weak node; otherwise, the node is considered a normal node.

[0020] E. Count the types of all nodes, and sequentially retrieve the number and type of nodes on each line. Based on this, optimize the network lines. The specific steps are as follows:

[0021] e1. Determine if there are weak nodes in the line. If there are no weak nodes in the line, keep the line without optimization. If there are weak nodes, continue to the next step.

[0022] e2. Divide the existing line into two ends, which are either associated nodes or ordinary nodes, and then divide the line into two primary lines. Determine the type of the nodes at both ends of the primary line. If the two ends are an ordinary node and an associated node, then do not optimize the primary line. If both ends are associated nodes, then continue to the next step.

[0023] e3. Further determine whether there are weak nodes in the primary line and count the number of weak nodes in the primary line; if the number of weak nodes in the primary line is 0 or exceeds the bidirectional judgment threshold Smax, then the primary line will not be optimized; otherwise, proceed to the next step.

[0024] e4. Building upon e3, further processing is performed based on the number of weak nodes in a primary circuit, specifically including:

[0025] If there is a weak node in a certain primary line L1, then compare the direction of the line between the weak node and the two endpoints of L1 to see if it is consistent with the main direction of line L1. If the directions are completely consistent, locate the endpoint of the nearest adjacent line L2 and replace the weak node with this endpoint. It should be noted that this replacement process only applies to line L1. When judging the adjacent line L2, L1 should be regarded as the original structure.

[0026] If a weak node exists in a primary route L3 and the number of weak nodes does not exceed the bidirectional judgment threshold Smax, it indicates that there are a large number of intersections or near intersections between route L3 and one or more adjacent routes. The distance is close enough, but they are not considered as intersecting routes in the road system. Generally, they are intersections with limited directions and diverging routes in specific areas, but they do not affect the smooth flow between routes during emergency handling. To improve emergency efficiency, all weak nodes of primary route L3 are analyzed in sequence. Specifically: determine the attributes of any node M2 ​​that is closest to weak node M1. If node M2 ​​is located in primary route L3, delete weak node M1. If node M2 ​​is located in the adjacent primary route L4, merge nodes M1 and M2, and treat the merged node as an associated node. Update primary routes L3 and L4 based on this. Repeat the above steps until all weak nodes in the primary route have been processed and output.

[0027] Further improvements or enhancements to the aforementioned highway emergency response system include an emergency response resource allocation unit in the highway emergency element basic data processing module. This unit allocates response resources to route nodes based on the specific conditions of the routes in the highway network. After a route or node issues an emergency warning, the unit schedules and uses emergency response resources among different nodes as needed to ensure that it can respond to potential or already occurring emergencies.

[0028] Further improvements or enhancements to the aforementioned highway emergency response system include an emergency response resource allocation unit that allocates emergency response resources based on the highway topology and network node information to enhance the ability to respond to emergencies. The specific steps are as follows:

[0029] The highway network is defined as the network space Road(M, L), where C is the set of highway network nodes and L is the set of routes connecting the nodes. The allocation of highway emergency response resources is described as the demand weight of node resources, which is determined based on factors such as the probability of occurrence of sudden events at the node, the importance of the node in the highway network, and the cost of resource allocation.

[0030] Assuming the emergency response resources allocated to node i are within the required response time period, the furthest distance from node i to effectively respond is D; d ij Let be the shortest path length from node i to other nodes j; then 'a' represents the emergency response resource capacity of node i for other nodes i. ij Represented as:

[0031]

[0032] Where a ij =1 indicates that it has the ability to handle, a ij =0 indicates a lack of capacity to handle the situation;

[0033] Define the disposal variable P for emergency response resource allocation at highway nodes. i Q j The meaning is as follows:

[0034]

[0035]

[0036] In the aforementioned network space Road(M, L), Q j The emergency response needs of any node with a value of 1 have been met; it is required that each node be within the influence range of a node with emergency response resources. The solution model is expressed as:

[0037]

[0038] Where c i To determine the cost of configuring a unit of emergency response resources at node i, and for ease of processing, let c... i =1; that is The aforementioned solution model is considered as a satisfaction rate of the emergency response resource coverage requirements of the nodes. Therefore, the aforementioned solution model can be expressed as:

[0039]

[0040] Among them W j Let be the emergency response resource demand weight for node j, and q be the total number of nodes covered by emergency response resources; because a ij Z j ,Xi The variables are all 0 or 1, resulting in the final solution model:

[0041]

[0042] Further improvements or enhancements to the aforementioned highway emergency response system also include a highway emergency response plan support module.

[0043] The emergency response plan support module for highway emergencies consists of an emergency response plan decision-making unit and a plan optimization unit.

[0044] When a highway emergency occurs, the emergency response decision-making unit generates an emergency response resource allocation plan based on the highway topology and network node emergency response resource allocation information. It flexibly allocates some of the allocated emergency response resources to the corresponding nodes of the emergency, forming different emergency response plans.

[0045] The optimal solution optimization unit outputs the best solution based on the actual influencing factors of highway emergencies. Specific steps include:

[0046] S1. The emergency response plan decision-making unit provides the y-th evaluation index T of the response plan based on the characteristics of the highway network structure and the influence of various factors. y The allowable reference points are y = 1, 2...Y; a weighted average is performed based on the allowable reference points given by all decision output units to obtain the reference values ​​of all evaluation indicators.

[0047] S2. Using the reference values ​​obtained in step S1 as the standard, eliminate schemes from the disposal decision schemes where a certain evaluation indicator is lower than its corresponding reference value, forming L alternative disposal schemes R. l l = 1, 2, 3...L;

[0048] Assume that the k-th decision output unit considers the l-th alternative solution R. l The propensity weight is T kl Then, the tendency weight matrix of the decision output unit for the alternative treatment plan is taken as...

[0049]

[0050] S3. Assume that the tendency weight of the u-th evaluation index given by the k-th decision output unit is S. klu Based on the weighted average method, the expected weights of the influence tendencies of all decision output units on the u-th evaluation index are obtained. Based on the expected influence tendency weights given by all decision output units, the influence tendency weights given by each decision output unit are corrected. Then, using methods such as the entropy weight method and ANP multi-attribute decision network, the corrected influence tendency weight V for each decision output unit based on the expected influence tendency weights is obtained. ku u = 1, 2, 3...U;

[0051] S3. Considering the differences in emphasis among different evaluation experts or systems, and the varying emphases of decision-making output units on different evaluation indicators, which is the reason for the differences in disposal plans, to optimize the impact of the decision-making output unit's decision-making tendency on the actual situation, the priority of the final decision plan is updated and optimized based on the above. Assume that the tendency weight of the k-th decision-making output unit's influence tendency on the u-th evaluation indicator after the adjustment is V. ku The final evaluation values ​​of the alternative disposal plans are obtained by updating the calculation based on the revised impact tendency weights and the evaluation scores of the disposal indicators. The best alternative disposal plans are then ranked and selected based on the evaluation results.

[0052] To further improve or refine the aforementioned highway emergency response system, the data server establishes a data index structure based on the following method: According to the data characteristics required for highway emergency response and the requirements of regional transportation system data applications, based on real-time traffic data, necessary information is extracted to establish a data dictionary table for storing element information such as class, class field, class projection, and class range, so as to enable systematic and rapid location and extraction of necessary data information; based on the SDE spatial data model, a data structure referencing various types of traffic data stored in the database is established for the map layers in the existing transportation system GIS data, and the attributes of the data layers and internal system symbols are determined. This process utilizes an SDE spatial data engine and ArcGIS services for establishment.

[0053] Further improvements or enhancements to the aforementioned emergency response system for highway emergencies include the data sharing and conversion unit, which is used to integrate multiple databases with similar structures or data to establish an integrated database. Specifically, this means querying each database according to the required target data, extracting valid data, removing duplicate data, and storing the data to obtain the integrated database.

[0054] Its beneficial effects are as follows:

[0055] The highway emergency response system proposed in this application is based on existing road management technologies such as traffic route network topology systems and GIS information collection systems. It is used to establish, improve and screen multiple emergency response plans for highway emergencies. It is suitable for the rapid optimization and generation of related routes and nodes of emergencies, advance planning and configuration or optimization of the allocation of existing emergency response resources, improving the coverage of emergency response resources and optimizing the effectiveness of emergency response. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the structure and functions of the emergency response database for the highway network;

[0057] Figure 2 This is a schematic diagram of the data processing steps used by this system for emergency response to road network conflicts. Detailed Implementation

[0058] The emergency response system for highway emergencies in this application is based on various data interfaces provided by existing highway network topology systems, GIS road geographic information systems, and other regional road management systems. Its purpose is to improve and process data on the basis of the aforementioned systems, and to establish and improve targeted emergency response plans for highway emergencies.

[0059] Specifically, such as Figure 1 As shown, the highway emergency response system of this application includes a highway network emergency response database, a highway emergency element basic data processing module, and a highway emergency resource support module.

[0060] The highway network emergency response database mainly consists of several parts, including a data server, a data integration unit, and a data sharing and conversion unit. These parts are divided based on their different functional requirements and levels. In actual implementation, the data server, data integration unit, and data sharing and conversion unit may be implemented by different processing tools configured in the same data center station or even located on the same server or PC.

[0061] The data integration unit establishes a database application service interface within the data server based on the WebService framework, registers with the data server using the WSDL network service description tool, and collects data from the database inside the data server using the SAAP protocol based on the data requests sent to the data server.

[0062] Thanks to the current mature and advanced traffic control and information collection systems, most of the basic data required by the highway emergency response system of this application can be directly extracted from existing road traffic control systems. Furthermore, the storage and utilization methods of data within these systems are similar, and their database structures are alike. Given the complex road system structure involving massive amounts of data, the structure and storage methods of this data directly affect the efficiency of data processing and utilization, as well as the hardware and software costs of system operation. Based on the foregoing, this application considers that the underlying structure of the highway system will still be planned and designed based on the actual geographical characteristics. Based on the widespread application of GIS data services within the existing transportation system and combined with the characteristics of GIS data, a multi-regional, multi-dimensional data storage structure will be established. This will ensure that the system's basic data structure is compatible with the underlying structure and characteristics of the required source data, reducing the difficulty of data fusion and extraction, and improving data processing efficiency. Specifically, the establishment scheme is as follows:

[0063] 1a. Based on the characteristics of the data required for emergency response to highway incidents and the requirements for data application in regional transportation systems (mainly referring to compliance with relevant national and departmental system data specifications and necessary data standards), on the basis of existing basic traffic data, necessary information is extracted to establish a data dictionary table for storing element information such as class, class field, class projection, and class range, so as to enable systematic and rapid location and extraction of necessary data information; based on this scheme, data formats suitable for rapid extraction and combination can be generated and established, so as to improve the efficiency of data management and retrieval during the early warning and response of incidents.

[0064] 1b. Currently, most road traffic management systems are equipped with output nodes or interfaces suitable for various data types. However, in order to improve the internal and external compatibility of the large system and expand data throughput, it is often difficult to effectively classify and intervene in advance. This leads to the need for further processing by various data application functional units. In the process of emergency response, there is a large amount of non-full data generated, transmitted and used. This data and information are often only used and processed for a short time in the process of local efficient scheduling and control. On the one hand, there is the urgency of emergency response, and on the other hand, temporary local scheduling often cannot call up a large number of system resources. To address this issue, the data integration unit of this application establishes a data structure that references various types of traffic data stored in the database for the map layering in the existing traffic system GIS data based on SDE spatial data, and determines the attributes of the data layer and the internal symbols of the system.

[0065] The above structure and specific implementation process are mainly based on the built-in functional units of tools and systems such as ArcSDE data pathway and ArcGIS services. The corresponding technologies and hardware and software structures are very mature and widely used. They are one of the commonly used basic technologies in data management systems, and will not be elaborated here.

[0066] To reduce the cost of redevelopment, improve system compatibility, and avoid the problem of difficulty in data acquisition due to differences in database structure design corresponding to different data sources, a data sharing and conversion unit is also set up in the data service module.

[0067] The data sharing and conversion unit is mainly used to establish a unified and efficient database structure to reduce the total number of attributes of data within the system and reduce the system burden. Specifically, this unit establishes a data sharing and conversion model for heterogeneous databases based on data mapping. The heterogeneous databases include at least commonly used distributed databases, B / S architecture databases, and relational databases. The data mapping includes mapping according to the data exchange format between data paths so that different types of databases can be converted to each other.

[0068] To avoid the complexity and redundancy of shared models caused by mapping and transformation between large amounts of data with similar structures, multiple databases with similar structures or data can be integrated to form an integrated database. In practice, each database can be queried according to the required target data, valid data can be extracted, duplicate data can be removed, and the integrated database can be obtained after storage.

[0069] The basic data processing module for highway emergency elements mainly includes a basic data processing unit and a highway emergency response network repair unit.

[0070] The basic data processing unit consists of fixed or mobile data ports within the highway emergency response network and data analysis tools. The basic data processing unit collects highway status information, highway operational status information, and regional meteorological status information based on the aforementioned ports. The data analysis tools filter the raw data based on pre-defined highway emergency event attribute elements, removing invalid and redundant data, and then classify the filtered data. The highway status information includes at least the distribution and usage status information of highway emergency response equipment or facilities, emergency response resource allocation, and node response resource information.

[0071] The highway operation status information includes at least the highway traffic volume and road load information, and road congestion information within the emergency response time-related time domain; the regional meteorological status information includes at least the regional wind direction and speed information, visibility information, temperature and air pressure information, and monitoring information for specific hazardous substance leaks.

[0072] The highway emergency response network repair unit includes a topology repair component configured at the output end of the urban highway network. This topology repair component uses a topology search tool to determine all nodes within the network and the lines connecting them to a given topology obtained during emergency response to a sudden event, based on a GIS topology map of the regional traffic network. It then acquires and optimizes the topology according to the following steps:

[0073] In real life, the influencing factors of highway emergencies and the scope of control and adjustment required in responding to emergencies vary greatly depending on the nature and severity of the emergency. During emergency response, data management and system control needs may be involved at different levels, in different regions, and with varying degrees of granularity. In existing road data systems, the data structure and the corresponding information categories and quantities of traffic data vary significantly from macro-level regional traffic planning data to smaller regional road routes and more micro-level local traffic systems. In responding to highway emergencies, data and resource scheduling of one or more traffic systems with different regional levels or levels of granularity are often involved. Furthermore, the data of these traffic systems may have completely different topologies due to differences in their target objectives and focuses.

[0074] In establishing an emergency response plan for highway incidents, to achieve a more accurate and efficient response, it is necessary to quickly retrieve and locate specific traffic route topologies and data network structures within the aforementioned multi-layered data systems. This is to facilitate the targeted acquisition of specific data information for the required routes or regions. While each of these systems possesses a relatively clear topology and hierarchical network in its corresponding region or level, it lacks the capability for more refined and multi-dimensional topology and data support. Furthermore, conflicts between system topologies directly affect the formation and effectiveness of emergency response plan data, leading to the ineffective collection of necessary data and the existence of unnecessary duplicate data. To address these problems and drawbacks, and to construct a system structure that meets the requirements of the emergency response plan of this application, this application also provides data processing steps for road network conflicts in emergency response, specifically including:

[0075] 1. For a given topology obtained during emergency response to a sudden event, determine all nodes within the network and the lines connecting those nodes based on the GIS topology map of the regional transportation network.

[0076] 2. For example Figure 2 As shown, the routes are obtained in the following order, and the route topology is optimized.

[0077] A. Traverse all nodes in the network topology to search for single-line nodes that are connected to only one line, and search for first-level lines in the network topology with single-line nodes as endpoints.

[0078] B. Traverse all first-level routes and record all intermediate nodes traversed by the routes; obtain all secondary routes with non-single-line nodes as endpoints.

[0079] C. Traverse the secondary paths. If a path contains only two nodes, continue searching the next secondary path.

[0080] D. If the secondary route contains more than two nodes, then based on the distance and connection relationship between each network node, analyze and judge all nodes traversed by the secondary route to determine the node type. The types include ordinary nodes, weak nodes, and associated nodes. Specific steps include:

[0081] d5. Delineate the search area with any node of the secondary route as the center and the search range dmax as the radius, and count the total number f1 of other routes passing through the search area, where dmax is the maximum distance that causes the traffic data obtained by two closely spaced nodes in different routes to overlap.

[0082] d6. If f1 = 0, then the node is considered a normal node, and return to step c to continue searching for the next secondary path;

[0083] d7. If f1 = 1, then sequentially determine whether the distance between the two endpoints of the route passing through the search area and the node is greater than dmin, where dmin is the minimum distance that allows one of the traffic data obtained by two nodes in different routes to completely contain the other. If it is greater than dmin, then the node is considered a normal node; if it is less than or equal to dmin, then the node is considered a weak node.

[0084] d8. If f1 > 1, calculate the distance between the endpoint of the line passing through the search area and the node, and count the number of distances greater than dmin, where dmin is the minimum distance that ensures that the traffic data obtained by two nodes in different lines completely contain the other. If the number of distances greater than dmin exceeds 1, the node is considered an associated node. If the number of distances greater than dmin is 1, the node is considered a weak node. Otherwise, the node is considered a normal node.

[0085] E. Count the types of all nodes, and sequentially retrieve the number and type of nodes on each line. Based on this, optimize the network lines. The specific steps are as follows:

[0086] e5. Determine if there are weak nodes in the line. If there are no weak nodes in the line, keep the line without optimization. If there are weak nodes, continue to the next step.

[0087] e6. Divide the existing line into two ends, which are either associated nodes or ordinary nodes, and then divide the line into two primary lines. Determine the type of the nodes at both ends of the primary line. If the two ends are an ordinary node and an associated node, then do not optimize the primary line. If both ends are associated nodes, then continue to the next step.

[0088] e7. Further determine whether there are weak nodes in the primary line and count the number of weak nodes in the primary line; if the number of weak nodes in the primary line is 0 or exceeds the bidirectional judgment threshold Smax, then the primary line will not be optimized; otherwise, proceed to the next step.

[0089] If the number of weak nodes is 0, it means that the route is a relatively independent and complete route and does not need to be optimized. If the number exceeds a certain threshold, it means that there are a certain number of overlapping nodes with adjacent routes. In this case, they are likely two bidirectional routes that are close to each other and need to maintain the original route parameters. The bidirectional judgment threshold Smax is determined based on the actual traffic system structure parameters of the area to be judged or given based on historical experience data.

[0090] e8. Based on e3, further processing is performed according to the number of weak nodes in a primary line, specifically including:

[0091] If there is a weak node in a certain primary line L1, then compare whether the direction of the line between the weak node and the two endpoints of L1 is consistent with the main direction of line L1 (the direction of the line connecting the two endpoints). If the directions are completely consistent, locate the endpoint of the nearest adjacent line L2 to the weak node and replace the weak node with this endpoint. It should be noted that this replacement process only applies to line L1. When judging the adjacent line L2, L1 should be regarded as the original structure.

[0092] If a weak node exists in a primary route L3 and the number of weak nodes does not exceed the bidirectional judgment threshold Smax, it indicates that there are a large number of intersections or close intersections between route L3 and one or more adjacent routes. The distance is close enough, but they are not considered as intersecting routes in the road system. Generally, they are intersections with limited directions and diverging routes in specific areas, but they do not affect the smoothness between routes during emergency handling. To improve emergency efficiency, all weak nodes of primary route L3 are analyzed in sequence. Specifically: determine the attributes of any node M2 ​​that is closest to weak node M1. If node M2 ​​is located in primary route L3, delete weak node M1. If node M2 ​​is located in the adjacent primary route L4, merge nodes M1 and M2, and treat the merged node as an associated node. Update primary routes L3 and L4 based on this. Repeat the above steps until all weak nodes in the primary route have been processed and output.

[0093] In the process of detecting, warning and handling emergencies on highways, scientific and efficient rapid management of emergency response resources in the region can improve the ability to prevent highway emergencies and ensure the efficiency of emergency response. It is also an important foundation for the implementation of emergency response plans.

[0094] In practice, emergency response resources are typically allocated to nodes along the highway network based on the specific conditions of each route. After a route or node issues an early warning of an emergency, these resources are scheduled and used among different nodes as needed to ensure the ability to respond to potential or already occurring emergencies. As a complex network structure, the highway system can determine its topology and network node information based on the aforementioned modules and schemes. By analyzing the highway network structure attributes, route status, and real-time traffic flow, the trend of emergencies occurring in the highway network can be judged and analyzed to determine the probability of nodes facing emergencies. Based on this probability, emergency response resources can be allocated in advance to improve the ability to respond to emergencies.

[0095] First, to simplify the problem, considering the topological characteristics of the highway network, the highway network can be defined as the network space Road(M, L), where C is the set of highway network nodes and L is the set of lines connecting the nodes. The allocation of highway emergency response resources can be described as the demand weight of node resources. The demand weight is determined based on factors such as the probability of occurrence of sudden events at the node, the importance of the node in the highway network, and the cost of resource allocation.

[0096] Assuming the emergency response resources allocated to node i are within the required response time period, the furthest distance from node i that can be effectively responded to is D; d ij Let be the shortest path length from node i to other nodes j; then 'a' represents the emergency response resource capacity of node i for other nodes i. ij It can be represented as:

[0097]

[0098] Where a ij =1 indicates that it has the ability to handle, a ij =0 indicates a lack of capacity to handle the situation;

[0099] Define the disposal variable P for emergency response resource allocation at highway nodes. i Q j The meaning is as follows:

[0100]

[0101]

[0102] In the aforementioned network space Road(M, L), Q j The emergency response needs of any node with a value of 1 can be met. However, in reality, emergency response resources are limited, while the number of highway nodes is enormous. It's impossible to guarantee that all nodes receive effective coverage from emergency response resources, while other nodes experience resource over-allocation. Therefore, choosing the optimal solution transforms into finding the minimum set of resource-allocated nodes, requiring that each node be within the influence range of a node with available emergency response resources. The solution model can be expressed as:

[0103]

[0104] Where c i To determine the cost of configuring a unit of emergency response resources at node i, and for ease of processing, c can be set... i =1; that is The aforementioned solution model can be viewed as a satisfaction rate of the emergency response resource coverage requirements of nodes. Therefore, the aforementioned solution model can be expressed as:

[0105]

[0106] Among them W j Let be the emergency response resource demand weight for node j, and q be the total number of nodes covered by emergency response resources; because a ij Z j ,X i The variables are all 0 or 1, resulting in the final solution model:

[0107]

[0108] Using the aforementioned schemes, we can obtain several resource allocation schemes for emergency response to highway emergencies. When an emergency occurs, some of the allocated emergency response resources can be flexibly deployed to the corresponding nodes of the emergency to form different emergency response schemes. Since the occurrence of emergencies is difficult to predict and the time requirements for responding to emergencies are high, there is often a great deal of uncertainty in the selection of emergency response schemes. The information available for reference is limited. In the actual emergency response process, it often relies heavily on the experience of on-site personnel and experts. Under extreme decision-making time and strong emergency pressure, it may lead to problems such as seemingly scientific and reasonable schemes, but ultimately affecting the effectiveness of the implementation of the response scheme and the unsatisfactory response capabilities.

[0109] To address this issue, this application includes an emergency response plan support module for highway emergencies. This module optimizes the output of response plans, avoids decision-making errors caused by irrational factors, and strives to determine the optimal response plan based on the actual influencing factors of highway emergencies. Specifically, it executes the following steps:

[0110] S1. The emergency response plan decision-making unit provides the y-th evaluation index T of the response plan based on the characteristics of the highway network structure and the influence of various factors. y The allowable reference points are y = 1, 2...Y; a weighted average is performed based on the allowable reference points given by all decision outputs to obtain the reference values ​​of all evaluation indicators.

[0111] S2. Using the reference values ​​obtained in step S1 as the standard, eliminate schemes from the disposal decision schemes where a certain evaluation indicator is lower than its corresponding reference value, forming L alternative disposal schemes R. l l = 1, 2, 3...L;

[0112] Assume that the k-th decision output is for the l-th alternative solution R l The propensity weight is T kl Then, the bias weight matrix of the decision output end for the alternative solutions can be obtained as follows:

[0113]

[0114] S3. Assume that the tendency weight of the u-th evaluation index given by the k-th decision output is S. klu Based on the weighted average method, we can obtain the expected weight S of the influence tendency of all decision outputs on the u-th evaluation index. klu Based on the expected influence tendency weights given by all decision outputs, the influence tendency weights given by each decision output are corrected. Using methods such as the entropy weight method and ANP multi-attribute decision networks, the corrected influence tendency weights V for each decision output based on the expected influence tendency weights are obtained. ku u = 1, 2, 3...U;

[0115] S3. Considering the differences in emphasis among different evaluation experts or systems, the decision-making output may have varying emphases on different evaluation indicators, which is also the reason for the differences in the disposal schemes. To optimize the impact of the decision-making output's bias on the actual situation, the priority of the final decision scheme is updated and optimized based on the above. Assume that the bias weight of the k-th decision output's bias towards the u-th evaluation indicator after the adjustment is V. ku The final evaluation values ​​of the alternative disposal plans are obtained by updating the calculation based on the revised impact tendency weights and the evaluation scores of the disposal indicators. The best alternative disposal plans are then ranked and selected based on the evaluation results.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A highway emergency incident handling system, characterized by, The expressway network emergency disposal database, an expressway emergency event element basic data processing module, and an expressway emergency resource support module are included. The expressway network emergency disposal database includes a data server, a data integration unit, and a data sharing conversion unit. The data integration unit establishes a database application service interface in the data server based on a WebService framework, registers with the data server using a WSDL network service description tool, collects data from the database inside the data server using a SAAP protocol according to a data request sent to the data server, and establishes a sharing conversion model of heterogeneous database data based on data mapping. The data sharing conversion unit establishes a sharing conversion model of heterogeneous database data based on data mapping, wherein the heterogeneous database at least includes a commonly used distributed database, a B / S system database, and a relational database, and the data mapping includes mapping according to data exchange formats between databases to enable mutual conversion between different types of databases. The expressway emergency event element basic data processing module includes a basic data processing unit and an expressway emergency disposal network repair unit. The basic data processing unit is composed of fixed or mobile data ports and data analysis tools in the expressway emergency disposal network, and collects expressway state information, expressway running state information, and regional meteorological state information based on the aforementioned ports; the data analysis tools filter invalid and redundant data based on previously prepared expressway emergency event element attributes, and classify the filtered data; the expressway state information at least includes distribution and use state information of expressway emergency event disposal equipment or facilities, emergency event disposal resource allocation and node disposal resource information; the expressway running state information at least includes expressway traffic flow and road load information in the time domain associated with emergency disposal time, and road congestion information; and the regional meteorological state information at least includes regional wind direction and speed information, visibility information, temperature and pressure information, and monitoring information for specific harmful substance leakage. The expressway emergency disposal network repair unit includes a topology repair component arranged at the output end of the urban expressway network, which uses a topology search tool to determine all nodes contained in the network and the lines connected to the nodes according to the GIS topological graph of the regional traffic network for a certain topological line obtained in the emergency event disposal process, and acquires and optimizes the topological line according to the following steps: A. Search for single-line nodes connected to only one line by traversing all nodes of the network topology, and search for first-level lines with the single-line nodes as end points from the network topology; B. Traverse all first-level lines, record all intermediate nodes passed by the lines, and acquire secondary lines with non-single-line nodes as end points; C. Traverse the secondary lines, and if the line only contains two nodes, continue to search for the next secondary line. D. If the secondary line contains more than two nodes, analyze and determine the type of all nodes through which the secondary line passes according to the distance of each network node and the connection relationship thereof, the type including a normal node, a weak node, and a correlation node, and the specific steps including: d1. Taking any node of the secondary line as the center and the search range dmax as the radius to define a search area, and counting the total number f1 of other lines passing through the search area, wherein dmax is the maximum distance at which the traffic data obtained by two nodes in different lines are repeated; d2. If f1=0, the node is considered as a normal node, and the next secondary line is searched in step c; d3. If f1=1, the distance between the two end points of the line passing through the search area and the node is determined in turn, and if the distance is greater than dmin, the node is considered as a normal node, wherein dmin is the minimum distance at which the traffic data obtained by two nodes in different lines are completely contained in each other; if the distance is less than or equal to dmin, the node is considered as a weak node; d4. If f1>1, the distance between the end points of the line passing through the search area and the node is calculated, and the number of distances greater than dmin is counted, wherein dmin is the minimum distance at which the traffic data obtained by two nodes in different lines are completely contained in each other; if the number of distances greater than dmin is more than 1, the node is considered as a correlation node, if the number of distances greater than dmin is 1, the node is considered as a weak node, otherwise, the node is considered as a normal node; E. The types of all nodes are counted, and the number and type of each node on each line are searched in turn, and the network line is optimized according to the number and type, and the specific steps are: e1. It is determined whether there is a weak node in the line, if there is no weak node in the line, the line is retained without optimization, if there is a weak node, the next step is continued; e2. The existing line is divided into two sides with the correlation node as the end point of the line, and the end nodes on both sides are correlation nodes or normal nodes to a primary line; it is determined whether the types of the two end nodes of the primary line are a normal node and a correlation node, if so, the primary line is not optimized; if the two end nodes are both correlation nodes, the next step is continued; e3. It is further determined whether there is a weak node in the primary line, and the number of weak nodes in the primary line is counted; if the number of weak nodes in the primary line is 0 or exceeds the bidirectional judgment threshold Smax, the primary line is not optimized, otherwise, the next step is executed; e4. On the basis of e3, the number of weak nodes in the primary line is further processed, including: if there is one weak node in a certain primary line L1, the directions of the lines between the weak node and the two end points of L1 are compared with the main direction of the line L1, if the directions are completely consistent, the end point of the adjacent line L2 closest to the weak node is located, and the end point is used to replace the weak node, it should be noted that the replacement process is only for L1 line, and when judging the adjacent line L2, L1 should be regarded as the original structure; If there is a weak node in a primary line L3 and the number of weak nodes does not exceed the bidirectional judgment threshold Smax, it indicates that there is a large amount of crossing or near-crossing structure between the line L3 and one or more adjacent lines, the distance is close enough, but it is not considered as a crossing line in the road system, at this time, all weak nodes of the primary line L3 are analyzed in turn, specifically: the attribute of any node M2 closest to the weak node M1 is determined, if the node M2 is located in the primary line L3, the weak node M1 is deleted, if the node M2 is located in the adjacent primary line L4, the nodes M1 and M2 are merged, and the merged node is regarded as an associated node, on this basis, the primary lines L3 and L4 are updated, and the above steps are cycled until all weak nodes in the primary line are processed and output.

2. The highway emergency handling system according to claim 1, characterized by, The expressway emergency element basic data processing module further comprises an emergency disposal resource allocation unit, which allocates disposal resources in line nodes according to the specific situation of the lines in the expressway network. After the line or node issues an emergency warning information, the emergency disposal resources are scheduled and used between different nodes as needed to ensure that the emergency disposal resources can respond to the emergency that can occur or has occurred.

3. The highway emergency handling system according to claim 2, wherein The emergency disposal resource allocation unit is further configured to allocate emergency disposal resources based on the expressway topology structure and network node information to improve the ability to respond to emergencies, and specifically performs the following steps: The expressway network is defined as a network space Road(M, L), where M is a set of expressway network nodes, and L is a set of lines connecting the nodes; the allocation of expressway emergency disposal resources is described as the demand weight of node resources, and the demand weight is determined according to the occurrence probability of node emergencies, the importance of nodes in the expressway network, and the resource allocation cost factors; Suppose that the emergency disposal resource allocated in node i can effectively dispose of the farthest distance D from node i within the required disposal time period; d ij is the shortest path length from node i to other node j; the disposal capacity a of the emergency disposal resource located at node i for other node i ij is represented as: ； wherein a ij = 1 indicates a capability to handle, a ij = 0 indicates no capability to handle; Definition of the treatment variable P for the allocation of emergency treatment resources at a highway node i , Q j have the following meanings: In the foregoing network space Road(M, L), The emergency disposal needs of any node in the network space Road(M, L) are satisfied; it is required to ensure that each node should be within the influence range of a node with emergency disposal resources, and the model is solved as follows: wherein is the cost of configuring a unit of emergency disposal resource at node i, for ease of processing, let ; that is ; the aforementioned solving model is considered to meet the satisfaction rate of the node emergency disposal resource coverage demand, and the aforementioned solving model is expressed as: where q is the total number of nodes covered by the emergency handling resource; because are variables that are either 0 or 1, resulting in the final solution model: 。 4. The highway emergency handling system according to claim 1, wherein The data server establishes a data index structure based on the following method: according to the characteristics of data required for expressway emergency disposal and the requirements of regional traffic system data application, necessary information is extracted based on real-time traffic data to establish a data dictionary table for storing class, class field, class projection, and class range element information, so that necessary data information can be quickly located and extracted systematically; based on the SDE spatial data model, a data structure is established for referencing various traffic data stored in the database for the map layering in the existing traffic system GIS data, and the attributes of the data hierarchy and the internal symbols of the system are determined.

5. The highway emergency handling system according to claim 1, wherein The data sharing and conversion unit is further configured to integrate multiple databases with similar structures or similar data to establish an integrated database, specifically by querying each database according to the required target data, extracting valid data, removing duplicate data, and storing to obtain the integrated database. The data server establishes a data index structure based on the following method: according to the characteristics of data required for expressway emergency disposal and the requirements of regional traffic system data application, necessary information is extracted based on real-time traffic data to establish a data dictionary table for storing class, class field, class projection, and class range element information, so that necessary data information can be quickly located and extracted systematically; based on the SDE spatial data model, a data structure is established for referencing various traffic data stored in the database for the map layering in the existing traffic system GIS data, and the attributes of the data hierarchy and the internal symbols of the system are determined. The data sharing and conversion unit is further configured to integrate multiple databases with similar structures or similar data to establish an integrated database, specifically by querying each database according to the required target data, extracting valid data, removing duplicate data, and storing to obtain the integrated database.

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