An emergency rescue point intelligent scheduling method and system for a dynamic risk scene

Abstract

The present application relates to the technical field of intelligent traffic and emergency management, in particular to an emergency rescue point intelligent scheduling method and system for dynamic risk scenarios. The method comprises the following steps: calculating basic evaluation indexes and emergency indexes according to monitoring data; performing weight distribution on the basic evaluation indexes through an analytic hierarchy process; obtaining a total danger grade score in combination with the basic evaluation indexes, the emergency indexes and the weight distribution result; matching a coverage radius of an emergency rescue point by using the total danger grade score based on a rescue resource capability coefficient and a road traffic condition coefficient, and obtaining an optimal layout scheme through the coverage radius. The present application makes the rescue coverage radius self-adaptively adjustable according to scenarios by coupling risk, capability and road conditions, and further optimizes site selection by using an intelligent algorithm, thereby significantly improving the response time, coverage rationality and overall scheduling efficiency of the emergency rescue network.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation and emergency management technology, specifically to an intelligent dispatching method and system for emergency rescue points in dynamic risk scenarios. Background Technology

[0002] In traffic scenarios such as highways and urban roads, emergencies such as traffic accidents and severe weather are dynamic and uncertain, placing extremely high demands on the timeliness of emergency rescue. Existing emergency rescue point layouts are mostly based on static historical data or experience, with fixed coverage radii, making it difficult to adapt to real-time changes in risk levels and road conditions. When risk scenarios change dynamically, problems such as insufficient rescue resource coverage or response delays often occur.

[0003] While some existing research attempts to incorporate traffic flow and weather data for risk assessment, these methods typically fail to systematically couple road infrastructure conditions, real-time emergency indicators, and the capabilities of rescue resources themselves. Furthermore, there is a lack of effective quantitative models and optimization algorithms for dynamically adjusting the coverage and layout of rescue points based on risk assessment results, leading to insufficient scientific rigor and adaptability in dispatching schemes.

[0004] Therefore, there is an urgent need for a method that can integrate dynamic risk indicators, rescue capabilities, and road conditions, and achieve dynamic matching and intelligent optimization of the coverage radius and layout of emergency rescue points. Summary of the Invention

[0005] To address the shortcomings of existing methods and the needs of practical applications, this invention provides an intelligent scheduling method for emergency rescue points in dynamic risk scenarios, comprising the following steps: Basic assessment indicators and emergency indicators are calculated based on monitoring data; the basic assessment indicators are weighted using the analytic hierarchy process (AHP); the total hazard score is obtained by combining the basic assessment indicators, the emergency indicators, and the weighted results; based on the rescue resource capacity coefficient and the road traffic condition coefficient, the coverage radius of emergency rescue points is matched using the total hazard score, and the optimal layout scheme is obtained through the coverage radius.

[0006] Optionally, the basic assessment indicators include: road driving speed risk coefficient, road traffic volume risk coefficient, vehicle driving speed variance risk coefficient, road length risk coefficient, and road cross and longitudinal slope risk coefficient.

[0007] Optionally, the emergency indicator satisfies: Within 10 minutes of triggering If the decrease is greater than 30%, the bonus score will increase by 3. Indicates the actual average driving speed; when triggered within 30 minutes If the increase is greater than 50%, the bonus score will increase by 2. Indicates the actual traffic volume; when triggered or Furthermore, real-time rain and snow conditions are available, adding 3 bonus points. Indicates the longitudinal slope. The cross slope is indicated; when multiple triggering conditions are met simultaneously, the additional score is calculated by accumulating the scores according to each triggering condition.

[0008] Optionally, the weight allocation of the basic evaluation indicators using the analytic hierarchy process includes the following steps: Based on the analytic hierarchy process, benchmark weights are determined for the basic evaluation indicators; historical monitoring data is used to correct the benchmark weights to obtain the final weight allocation results.

[0009] Optionally, the total danger level score is obtained by combining the basic assessment indicators, the emergency indicators, and the weight allocation results, satisfying the following: in, Indicates the total risk level score. This represents the score of the i-th basic evaluation index. This indicates the weight allocation result. This indicates bonus points for emergency indicators.

[0010] Optionally, the rescue resource capacity coefficient satisfies: When the comprehensive score of vehicle performance, personnel skills, and equipment configuration is 90-100 points, the rescue resource capability coefficient corresponds linearly to 1.2-1.3; when the comprehensive score of vehicle performance, personnel skills, and equipment configuration is 70-90 points, the rescue resource capability coefficient corresponds linearly to 1.0-1.2; when the comprehensive score of vehicle performance, personnel skills, and equipment configuration is 50-70 points, the rescue resource capability coefficient corresponds linearly to 0.9-1.0. And satisfy: in, Indicates the coefficient of rescue resource capacity. It represents a comprehensive score based on vehicle performance, personnel skills, and equipment configuration.

[0011] Optionally, the road traffic condition coefficient satisfies: When the combined score of the number of lanes, emergency lane, and road surface condition is 90-100 points, the road traffic condition coefficient corresponds linearly to 1.1-1.2; when the combined score of the number of lanes, emergency lane, and road surface condition is 70-90 points, the road traffic condition coefficient corresponds linearly to 1.0-1.1; when the combined score of the number of lanes, emergency lane, and road surface condition is 50-70 points, the road traffic condition coefficient corresponds linearly to 0.8-1.0. And satisfy: in, This represents the road traffic condition coefficient. This represents a comprehensive score based on the number of lanes, emergency lanes, and road surface conditions.

[0012] Optionally, the matching of the coverage radius of emergency rescue points using the total hazard level score satisfies: in, Indicates the coverage radius. Indicates the reference coverage radius. Indicates the coefficient of rescue resource capacity. This represents the road traffic condition coefficient.

[0013] Optionally, obtaining the optimal layout scheme through the coverage radius includes the following steps: A fitness function is constructed based on coverage completeness, deployment flexibility, and operating costs; based on the coverage radius and the fitness function, an optimal layout scheme is obtained through a genetic algorithm.

[0014] This invention calculates basic risk indicators and triggered emergency indicators through real-time monitoring data. It employs the analytic hierarchy process (AHP) and combines it with historical data to scientifically weight multi-dimensional basic indicators, thereby comprehensively calculating a total score that accurately represents the on-site risk level. Furthermore, it introduces rescue resource capacity coefficients and road traffic condition coefficients as correction factors, allowing the theoretical coverage radius of rescue points to dynamically adjust according to their own capabilities and road conditions. Finally, based on the dynamic coverage radius of each point, it uses optimization techniques such as genetic algorithms to construct a fitness function with multiple objectives such as coverage and cost, solving for the globally optimal rescue point layout scheme. This achieves a fundamental shift from static experience-based layout to dynamic data-driven approach, significantly improving the real-time performance and accuracy of risk assessment. Secondly, by systematically coupling the three key elements of risk, capability, and road conditions, the coverage and response strategies of the rescue network can adaptively optimize with changing scenarios, solving the problems of resource mismatch and response delays. While ensuring high coverage and deployment flexibility, it effectively controls operating costs, thereby comprehensively improving the overall effectiveness, reliability, and timeliness of the emergency rescue system.

[0015] Secondly, to efficiently execute the intelligent scheduling method for emergency rescue points in dynamic risk scenarios provided by this invention, this invention also provides an intelligent scheduling system for emergency rescue points in dynamic risk scenarios, comprising: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory includes program instructions used for the intelligent scheduling method for emergency rescue points in dynamic risk scenarios. The intelligent scheduling system for emergency rescue points in dynamic risk scenarios provided by this invention has a compact structure and stable performance, and can stably execute the intelligent scheduling method for emergency rescue points in dynamic risk scenarios provided by this invention, further enhancing the overall applicability and practical application capability of this invention. Attached Figure Description

[0016] Figure 1 A flowchart of an intelligent dispatching method for emergency rescue points in dynamic risk scenarios provided by an embodiment of the present invention; Figure 2 This is a framework diagram of an intelligent dispatch system for emergency rescue points in dynamic risk scenarios, provided as an embodiment of the present invention. Detailed Implementation

[0017] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0018] Throughout this specification, references to an embodiment, example, or illustration mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, phrases appearing in various places throughout the specification, such as "in one embodiment," "in an embodiment," "an example," or "an illustration," do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in any suitable combination and / or sub-combination in one or more embodiments or examples. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0019] Please see Figure 1 This invention provides an intelligent scheduling method for emergency rescue points in dynamic risk scenarios. In one embodiment, the method includes the following steps: S1. Calculate basic assessment indicators and emergency indicators based on monitoring data.

[0020] In this embodiment, the basic evaluation indicators include: road driving speed risk coefficient, road traffic volume risk coefficient, vehicle driving speed variance risk coefficient, road length risk coefficient, and road cross and longitudinal slope risk coefficient.

[0021] Furthermore, the actual average speed of the road segment is obtained through section speed measurement equipment (microwave radar, video detectors). Alternatively, monitoring data can be obtained through the floating car's GPS terminal.

[0022] The road driving speed risk coefficient satisfies: in, Indicates the risk factor of road driving speed. This indicates the design speed, such as 120 km / h on highways. 60 mountain roads .

[0023] The actual traffic volume is counted every 30 minutes using a loop detector, AI video counter, or infrared beam detector. The road traffic volume risk coefficient satisfies: in, This indicates the risk coefficient of road traffic volume. This indicates the designed traffic capacity, such as 4,000-6,000 vehicles per 30 minutes on a two-way four-lane highway.

[0024] Speed ​​measurements can be obtained every 30 minutes using multi-section video speed measurement or microwave radar. Instantaneous speeds of 50 valid vehicles (exclude (of parked vehicles), calculate average speed (That is, the speed value corresponding to the road driving speed score).

[0025] Furthermore, the velocity variance is calculated. Reflecting the degree of velocity dispersion, it satisfies: Then, calculate the variance risk coefficient of vehicle speed according to the variance range. ,satisfy: The actual length L of the road segment (along the road centerline, unit: km) is measured using a GIS map or summarized on-site using a laser / wheeled rangefinder. Based on the baseline, each increase Add 1 point to obtain the road length risk coefficient. The score range is 2-10 points. ,but .

[0026] Extracting longitudinal slope from design drawings , Hengpo Alternatively, the risk coefficients of the road's cross and longitudinal slopes can be measured in the field using an electronic slope meter or drone aerial surveying. ,satisfy: in, Indicates the longitudinal slope. This indicates the cross slope.

[0027] Furthermore, the aforementioned emergency indicators satisfy: Within 10 minutes of triggering If the decrease is greater than 30%, the bonus score will increase by 3. This indicates the actual average driving speed; When triggered within 30 minutes If the increase is greater than 50%, the bonus score will increase by 2. Indicates actual traffic volume; When triggered or Furthermore, real-time rain and snow conditions are available, adding 3 bonus points. Indicates the longitudinal slope. This indicates the cross slope.

[0028] When multiple triggering conditions are met simultaneously, the additional score is calculated by accumulating the scores according to each triggering condition.

[0029] S2. The basic evaluation indicators are weighted using the analytic hierarchy process (AHP).

[0030] In one embodiment, the weight allocation of the basic evaluation indicators using the analytic hierarchy process includes the following steps: S21. Based on the analytic hierarchy process, determine the benchmark weights for the basic evaluation indicators.

[0031] First, a hierarchical structure is constructed, defining a three-layer structure to lay the framework for weight calculation: Target Layer (A): Road Hazard Level Assessment (the core objective is to accurately quantify the degree of hazard). Criterion Layer (B): All basic indicators (denoted as B1, B2, ..., Bn, where n is the number of basic indicators); Solution Layer (C): Real-time monitoring data corresponding to each basic indicator.

[0032] Secondly, a judgment matrix is ​​constructed through expert scoring.

[0033] Domain experts were invited to conduct pairwise comparisons of the relative importance of all basic indicators at the criterion layer, using a 1-9 scaling method (the scaling method represents a gradient assignment from equally important to extremely important) to form... Order judgment matrix ,in This indicates the importance of index Bi relative to Bj, and satisfies... .

[0034] Then, subjective judgment bias is eliminated through consistency checks. The basic steps are as follows: (1) Calculate the largest eigenvalue of the judgment matrix. First, normalize each column of the judgment matrix (the normalized vector of the j-th column). Then calculate the product of the matrix and the normalized vector, and finally obtain the mean. The formula is: (2) Calculate the consistency index CI (3) Query the random consistency index RI (query the standard RI table based on the number of basic indicators n); (4) Calculate the consistency ratio CR If CR < 0.1, the judgment matrix passes the test and can be used to calculate the weights; If CR The expert scores need to be readjusted until the test is passed.

[0035] Finally, for the judgment matrix that passes the consistency test, the benchmark weights of each basic indicator are calculated using the eigenvalue method, with the following formula: In the formula, The benchmark weight of the i-th basic indicator satisfies , To determine the product of the elements in the i-th row of the matrix, n is the number of basic indices (corresponding to the number of square roots).

[0036] S22. Use historical monitoring data to correct the baseline weights and obtain the final weight allocation result.

[0037] Adjusting the baseline weights based on historical monitoring data to ensure they are suitable for real-world scenarios involves the following steps: First, calculate the indicator contribution rate. The indicator contribution rate reflects the actual impact of a certain indicator on the risk level score. It is calculated based on N valid assessment records within a preset period, using the following formula: In the formula, Let i be the score of the i-th indicator in the k-th record. The weighted score for the i-th indicator. Let be the periodic average of the weighted scores for the i-th indicator. The value range is 0-1.

[0038] Then, the weights after iterative optimization are calculated, and the weight correction coefficient is used to balance expert experience with real-time data. The formula is: In the formula, This is the weighting correction factor (with a value range of 0.6-0.8). Normalization is required to meet the following requirements. .

[0039] In some other embodiments, when an emergency indicator is triggered, the weight of the corresponding basic indicator is temporarily adjusted to amplify its impact, including the following steps: First, identify the relevant basic indicators. Based on the type of emergency indicator (such as a sudden drop in speed or a sudden increase in traffic volume), match the corresponding basic indicators (such as a sudden drop in speed related to road speed) and clarify the target indicators that need to be adjusted. Relevant basic indicators include: a sudden drop in speed related to the magnitude of the decrease in road speed; a sudden increase in traffic volume related to the actual traffic volume on the road; and for sloped road sections, rain and snow weather related to cross and longitudinal slopes, as well as real-time rain and snow weather conditions.

[0040] Secondly, calculate the temporary weights, which are increased by a preset fixed percentage, using the following formula: This is the preset weight increase amount; Non-correlated basic indicators: Weights are compressed proportionally to ensure that the sum of the weights of all indicators is still 1. The formula is: j is the sequence number of the non-correlated basic indicator. The total weight of non-related indicators; If multiple emergency indicators are triggered simultaneously, the weights of the corresponding basic indicators should be increased accordingly, while unrelated indicators should be compressed according to the above rules.

[0041] S3. Combine the basic assessment indicators, the emergency indicators, and the weight allocation results to obtain the total danger level score.

[0042] In this embodiment, the total danger level score is obtained by combining the basic assessment indicators, the emergency indicators, and the weight allocation results, satisfying the following: in, Indicates the total risk level score. This represents the score of the i-th basic evaluation index. This indicates the weight allocation result. This indicates bonus points for emergency indicators.

[0043] Furthermore, for routine scenarios (without urgent metric triggers): iteratively optimize the weights. The formula remains unchanged: Take 0.6-0.8, As the benchmark weight, Contribution to the indicator; For emergency scenarios (triggered by emergency indicators): temporarily adjust the weights. The formula remains the same, but with the increased weight of related indicators, it needs to be adapted to subsequent radius adjustments for congested / remote areas: ( (related indicators) (Non-correlated indicators, ensuring the sum is 1).

[0044] Scene division includes: Emergency scenarios: When emergency indicators are triggered (such as sudden drop in speed, surge in traffic, rain or snow, etc.); Routine scenario: No urgent indicators are triggered.

[0045] In emergency scenarios, adding emergency indicators will increase the total danger level score of the location, allowing mobile rescue points to be closer and enabling faster rescue operations in the event of an emergency.

[0046] S4. Based on the rescue resource capacity coefficient and road traffic condition coefficient, the coverage radius of the emergency rescue point is matched using the total danger level score, and the optimal layout scheme is obtained through the coverage radius.

[0047] In this embodiment, the rescue resource capacity coefficient satisfies: When the comprehensive score of vehicle performance, personnel skills, and equipment configuration is 90-100 points, the corresponding rescue resource capability coefficient is linearly 1.2-1.3. When the comprehensive score of vehicle performance, personnel skills, and equipment configuration is 70-90 points, the rescue resource capability coefficient corresponds linearly to 1.0-1.2. When the comprehensive score of vehicle performance, personnel skills, and equipment configuration is 50-70 points, the corresponding rescue resource capability coefficient is linearly 0.9-1.0. And satisfy: in, Indicates the coefficient of rescue resource capacity. It represents a comprehensive score based on vehicle performance, personnel skills, and equipment configuration.

[0048] The specific rules for calculating the comprehensive score based on vehicle performance, personnel skills, and equipment configuration are as follows: Single indicator rating and assignment (maximum score 100, rating according to emergency rescue industry standards): Excellent (90-100 points): Vehicles / personnel / equipment fully meet the national first-level emergency rescue standards, with no shortcomings; Good (70-89 points): Meets the Level 2 standard, core competencies are up to standard, but some non-core indicators are slightly below average; Medium (50-69 points): Meets the Level 3 standard, only meets basic rescue needs, and has shortcomings in core capabilities; Poor (<50 points): Does not meet the Level 3 standard and will not be included in the rescue point layout.

[0049] Overall score calculation: Overall score = (Vehicle performance score + Personnel skill score + Equipment configuration score) ÷ 3 The road traffic condition coefficient satisfies: When the combined score of the number of lanes, emergency lane, and road surface condition is 90-100 points, the road traffic condition coefficient corresponds linearly to 1.1-1.2. When the combined score of the number of lanes, emergency lane, and road surface condition is 70-90 points, the road traffic condition coefficient corresponds linearly to 1.0-1.1. When the combined score of the number of lanes, emergency lane, and road surface condition is 50-70 points, the road traffic condition coefficient corresponds linearly to 0.8-1.0. And satisfy: in, This represents the road traffic condition coefficient. This represents a comprehensive score based on the number of lanes, emergency lanes, and road surface conditions.

[0050] The calculation method for the comprehensive score of lane number, emergency lane, and road surface condition is as follows: 1. Number of lanes Advantages: Highways / expressways have ≥6 lanes in both directions, urban arterial roads have ≥4 lanes in both directions, and suburban / national / provincial highways have ≥3 lanes in both directions. There are no lane obstructions or reductions, which can meet the needs of rescue vehicles to pass or borrow lanes. Good: Expressways / expressways have 4 lanes in both directions, urban main roads have 3 lanes in both directions, and suburban / national / provincial highways have 2 lanes in both directions. The core lanes are not occupied, which basically meets the passage of rescue vehicles. Medium: The number of lanes on various types of roads is lower than the above-mentioned good standard, or there is a regular problem of lane occupation. Rescue vehicles need to give way / slow down when passing.

[0051] 2. Emergency lane Advantages: The emergency lane is continuous and uninterrupted throughout the entire road, with a width of ≥3.5 meters, free from occupation / blockage, seamlessly connected to rescue points / accident-prone sections, and clearly marked; Good: The core road section has continuous emergency lanes, with some breaks but emergency avoidance areas, no long-term occupation, and the signs are basically clear; In the middle section: emergency lanes are only provided in some sections / there are no dedicated emergency lanes, and temporary lane occupation occurs, requiring rescue vehicles to use the main road to pass.

[0052] 3. Road surface conditions Advantages: The road surface is free of potholes / cracks / subsidence, the damage rate is ≤1%, the coefficient of friction is ≥0.65, there are anti-skid / drainage measures in rainy and snowy weather, and rescue vehicles can pass at full speed; Good: The road surface is slightly damaged (damage rate 1%-3%), with no obvious potholes, a friction coefficient ≥0.55, and basically no water / ice accumulation in rainy or snowy weather. Rescue vehicles do not need to slow down significantly. Medium: Road surface damage rate >3%, obvious potholes / sinking, friction coefficient <0.55, prone to water / ice accumulation in rainy or snowy weather, rescue vehicles need to pass at low speed.

[0053] Furthermore, the rating rules are as follows: 1. Excellent (90-100): All three indicators are excellent, with no shortcomings, and the road conditions are fully adapted to the rapid passage of emergency rescue. 2. Good (70-90): Two excellent and one good / one excellent and two good, core traffic conditions meet the standards, some areas are slightly worse but do not affect rescue; 3. Medium (50-70): One grade is good and the rest are excellent, or all grades are good. There are obvious shortcomings in road traffic conditions, which affect the efficiency of rescue vehicle passage.

[0054] Furthermore, based on the road segment attributes and the total hazard score, the hazard level is classified, satisfying the following conditions: When the total danger level score is ≥14 and the duration of existence meets the requirements of ≥4 hours in resource-intensive areas and ≥5 hours in remote areas, it corresponds to Level I extremely dangerous. When 11 ≤ total hazard score < 14, and the duration meets the requirements of ≥ 3 hours in smooth scenarios and ≥ 4 hours in congested / remote areas, it corresponds to Level II high hazard. When 8 ≤ total hazard score < 11, and the duration of existence meets the general scenario requirement of ≥ 2 hours, it corresponds to Level III medium hazard; When 5 ≤ total hazard score < 8, and the duration meets the general scenario requirement of ≥ 1.5 hours, it corresponds to Level IV low hazard. When the total hazard score is less than 5, it corresponds to Level V safety.

[0055] Duration The core indicator is the number of deployed rescue resources (such as first aid stations, highway service area rescue points, and emergency material reserve points) within a 100km radius of the road section. Areas with a resource density of ≥0.5 per 100km are considered resource-intensive areas, while those with a density of ≥0.5 per 100km are considered remote areas. Using real-time traffic speed on road sections as the core indicator, the criteria for judging congestion status are: a speed of less than 20 km / h is considered a congested scenario, while the opposite is considered a smooth scenario. Furthermore, the baseline coverage radius is set according to the hazard level, as shown in Table 1: Table 1. Matching Table of Hazard Levels and Baseline Coverage Radius Furthermore, the matching of the coverage radius of emergency rescue points using the total hazard level score satisfies: in, Indicates the coverage radius. Indicates the reference coverage radius. Indicates the coefficient of rescue resource capacity. This represents the road traffic condition coefficient.

[0056] Furthermore, some embodiments also include additional constraints: Level I Remote Area: ≤6km (ensure response time ≤5 minutes); Level II congestion scenario: ≤8km (ensure response time ≤7 minutes); Level III / IV: ≤12km / 18km (avoid over-coverage).

[0057] In this embodiment, obtaining the optimal layout scheme through the coverage radius includes the following steps: First, an fitness function is built based on coverage completeness, deployment flexibility, and operating costs.

[0058] Specifically, an fitness function is constructed based on coverage completeness, deployment flexibility, and operational costs, satisfying the following: F represents the chromosome fitness value (the higher the value, the better the scheme). =0.4 (coverage completeness weight) =0.3 (Deployment flexibility weight) =0.3 (Operating cost weight), the weight is set based on the goal of prioritizing document coverage; Furthermore, The number of Level I-IV road risk points covered by the rescue points. This represents the total number of road risk points classified as Levels I-IV. The overlapping area covered by multiple rescue points. The total area covered by all rescue points; Gather at the selected rescue points. Selected rescue point The hub radiation index (out of 100), where m is the total number of candidate rescue points; The hub radiation index is essentially a comprehensive score of a rescue point's resource distribution efficiency, transportation accessibility, and infrastructure support. It differentiates the hub level of rescue points: high-scoring rescue points (e.g., ≥85 points) possess stronger resource allocation and multi-directional radiation capabilities, making them priority choices for layout optimization. Their scores should be based on resource distribution capacity, transportation accessibility, and infrastructure capabilities. Traffic accessibility (40 points): This assesses the connection between the rescue point and the road, the accessibility of the emergency lane, the travel time to the risk point, and the surrounding congestion. The better the connection and the shorter the travel time, the higher the score.

[0059] Resource aggregation and distribution capability (30 points): This assesses the efficiency of rescue resource dispatch, the ability to share equipment across different locations, the ability to coordinate with surrounding rescue points, and the personnel on-duty configuration. The more efficient the dispatch / coordination, the higher the score.

[0060] Infrastructure support (30 points): Evaluate the scale of the rescue site, the capacity for emergency material reserves, and basic supporting facilities such as power supply / communication. The more complete the hardware conditions, the higher the score.

[0061] The number of selected rescue points. The basic operating cost of a single rescue point is S, which represents the equipment sharing rate (the document requires S≥0.4, meaning a 40%-50% reduction in the number of rescue points). The equipment sharing rate (S) is a core quantitative indicator that measures the degree of coordinated reuse of rescue equipment among emergency rescue points. It is a key parameter for calculating operating costs and reflects the efficiency of sharing rescue equipment among multiple rescue points, rather than the exclusive configuration of a single rescue point. The requirement that S≥0.4 means that at least 40% of the core rescue equipment in the rescue network can be shared among various rescue points, achieving the cost control target of reducing the number of rescue points by 40%-50%.

[0062] in: Venue rental or depreciation costs, For depreciation and maintenance costs of rescue equipment, For the cost of rescue workers' salaries, For the cost of emergency supplies and consumables, This refers to daily energy consumption costs.

[0063] Secondly, based on the coverage radius and the fitness function, the optimal layout scheme is obtained through a genetic algorithm.

[0064] For candidate rescue point set (m is the number of candidate points), using a binary + real number hybrid encoding of length 2m, the encoding rules strictly match the requirement of prioritizing points with a hub radiation index ≥ 85: The first m bits (binary bits): , Indicates the selection of candidate points , Indicates that it is not selected; if Hub radiation index ,but The probability of initially assigning a value of 1 is increased to 80%; The last m digits (real digits): , indicating that it is selected Fine-tuning values ​​based on the original coordinates (range: ), ensuring that rescue points are aligned with actual road nodes (such as highway service area entrances and exits, and roadside emergency parking areas).

[0065] Furthermore, the population is initialized, and the population size is set to N=50 (to balance iteration efficiency and solution diversity). Randomly generate N chromosomes, satisfying two constraints during generation: ① The coverage ratio of roads of levels I-IV in the initial layout scheme is ≥90%; ② The selected hub point ( The proportion of initial solutions should be ≥60% to avoid deviation from the core requirements of complete coverage and flexible deployment.

[0066] The parameters of genetic operations (selection, crossover, mutation) are dynamically adjusted according to the road hazard level to meet the mechanism requirements of adding points within 30 minutes when the hazard level is upgraded and removing redundant points when the hazard level is downgraded. Selection operator: A weighted roulette wheel betting method is used. If the risk level is upgraded (e.g., the proportion of Level I risk points increases by ≥10%), then... Chromosomes with a coefficient ≥0.98 are assigned an additional 1.2 times the selection probability; Crossover operator: The basic crossover probability Pc = 0.7. If the risk level is downgraded (e.g., the proportion of Level I risk points decreases by ≥10%), then Pc drops to 0.5. Double-point crossover is used for binary bits, and arithmetic crossover is used for real bits. Mutation operator: The basic mutation probability Pm=0.05, and Pm rises to 0.1 when the iteration gets stuck in a local optimum (F change ≤1% for 3 consecutive generations); when there are new dangers around the rescue point, the corresponding coding bit is forcibly mutated (Pm=1).

[0067] In this embodiment, the chromosome selection probability formula satisfies: Let be the selection probability of the i-th chromosome. Let be the fitness value of the i-th chromosome. This is a risk level adjustment factor; when the danger escalates, Chromosomes ≥0.98 =1.2, the rest =1.

[0068] Furthermore, the iteration terminates and the optimal layout scheme is output when any of the following conditions are met: The number of iterations reached G=100 generations; The fitness value F changes by ≤1% for 5 consecutive generations, and the scheme satisfies ≥0.98 ≥0.85 ≤0.6; Two hours have passed since the last layout scheme was generated (meeting the requirement of generating a layout scheme every two hours).

[0069] Furthermore, the best resources are selected from the network of rescue points to achieve multi-point coordination.

[0070] Specifically, the decision-making factors are distance, resource matching degree, vehicle speed and trajectory matching degree, and route smoothness. The core and secondary factors are determined according to the rescue scenario (such as giving higher weight to route smoothness during morning / evening rush hours; giving higher weight to resource matching degree for hazardous materials / critical care rescue, etc.). The conditions of the rescue point are scored (0-10 points), and then the rescue quality score is quantitatively evaluated through the rescue quality score formula to obtain the optimal rescue point with the highest score from the rescue points near the accident section.

[0071] The rescue quality score meets the following requirements: in: The overall score for rescue quality; , , , These are the dynamic weighting coefficients for each indicator, with a weight sum of 1, and the proportions are adjusted according to the rescue scenario. Score the distance indicator; Score the resource matching degree; The vehicle speed and trajectory matching score is assigned. Score the route's smoothness; The method for obtaining the value is as follows: 1. Dynamic weights W1 / W2 / W3 / W4: allocated according to the rescue scenario, with a sum of 1.

[0072] (1) Hazardous chemicals / critical care rescue: W2 (resource matching degree) has the highest weight (e.g., 0.4), and priority is given to ensuring resource matching; (2) Morning / evening rush hour congestion: W4 (route smoothness) has the highest weight (e.g., 0.4), and priority is given to ensuring smooth traffic flow; (3) Accidents on remote road sections: W1 (distance) has the highest weight (e.g., 0.4), and priority is given to ensuring close-range response; (4) Routine traffic accidents: The four items are weighted equally (0.25 each).

[0073] 2. Score S for each basic indicator.

[0074] (1) Scoring is based on the actual driving distance / time from the rescue point to the accident site, with higher scores for shorter distances / shorter times; for example, ≤1km (≤2 minutes) gets 10 points, >5km (>10 minutes) gets 0 points, and scores are awarded linearly in between. (Specific values ​​are based on the actual rescue point.) (2) Scoring is based on the degree of matching between the rescue point's equipment / personnel and the type of accident. A perfect match earns 10 points, while a lack of core rescue resources earns 0 points. For example, if the accident involves vehicle demolition, a rescue point equipped with demolition tools earns 10 points, while a rescue point equipped with only first-aid supplies earns 3 points.

[0075] (3) Scoring is based on the real-time driving speed of the rescue vehicle (adhering to the road speed limit) and the rationality of the planned trajectory (no detours). Speed ​​matching + optimal trajectory earns 10 points, while excessively low speed / serious detours earn 0 points. The scoring also incorporates road driving speed and road traffic condition coefficients from the basic data.

[0076] (4) Scoring is based on real-time road traffic conditions from the rescue point to the accident site. 10 points are awarded for no congestion along the entire route, and 0 points are awarded for congestion along the entire route. The scoring also incorporates actual average driving speed and road traffic volume monitoring data from the basic data. For example, 10 points are awarded for a road section speed ≥60km / h, and 0 points are awarded for a speed <20km / h (congestion).

[0077] In this embodiment, the dispatching capability can also be improved through multi-rescue point coordination, including: ① Resource complementarity and synergy: Match resource complementarity points within 15km to ensure that both vehicles arrive at the same time (error ≤ 12 minutes). ② Coverage relay coordination: When the danger point is at the radius boundary, optimize the rendezvous point and trajectory to quickly move towards the target; ③ Green channel linkage: Connect with the transportation system to increase the speed of rescue vehicles by 30%-40% and ensure response time (Level I ≤ 3 minutes, Level IV ≤ 12 minutes).

[0078] Please see Figure 2 In this embodiment, to efficiently execute the intelligent scheduling method for emergency rescue points in dynamic risk scenarios provided by this invention, the present invention also provides an intelligent scheduling system for emergency rescue points in dynamic risk scenarios, comprising: an input device 1, an output device 2, a processor 3, and a memory 4. The input device 1, output device 2, processor 3, and memory 4 are interconnected. The memory 4 contains program instructions used to execute the steps of the intelligent scheduling method for emergency rescue points in dynamic risk scenarios. The intelligent scheduling system for emergency rescue points in dynamic risk scenarios of this invention has a compact structure and stable performance, and can stably execute the intelligent scheduling method for emergency rescue points in dynamic risk scenarios provided by this invention, further enhancing the overall applicability and practical application capability of this invention.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention.

Claims

1. An intelligent scheduling method for emergency rescue points in dynamic risk scenarios, characterized in that, Includes the following steps: Calculate basic assessment indicators and emergency indicators based on monitoring data; The weights of the basic evaluation indicators are assigned using the analytic hierarchy process. By combining the basic assessment indicators, the emergency indicators, and the weight allocation results, the total risk level score is obtained; Based on the rescue resource capacity coefficient and road traffic condition coefficient, the coverage radius of emergency rescue points is matched using the total hazard level score, and the optimal layout scheme is obtained through the coverage radius.

2. The intelligent dispatching method for emergency rescue points in dynamic risk scenarios according to claim 1, characterized in that, The basic assessment indicators include: road driving speed risk coefficient, road traffic volume risk coefficient, vehicle driving speed variance risk coefficient, road length risk coefficient, and road cross and longitudinal slope risk coefficient.

3. The intelligent dispatching method for emergency rescue points in dynamic risk scenarios according to claim 1, characterized in that, The aforementioned emergency indicators satisfy: Within 10 minutes of triggering If the decrease is greater than 30%, the bonus score will increase by 3. This indicates the actual average driving speed; When triggered within 30 minutes If the increase is greater than 50%, the bonus score will increase by 2. Indicates actual traffic volume; When triggered or Furthermore, real-time rain and snow conditions are available, adding 3 bonus points. Indicates the longitudinal slope. Indicates the cross slope; When multiple triggering conditions are met simultaneously, the additional score is calculated by accumulating the scores according to each triggering condition.

4. The intelligent dispatching method for emergency rescue points in dynamic risk scenarios according to claim 1, characterized in that, The weight allocation of the basic evaluation indicators using the analytic hierarchy process includes the following steps: Based on the analytic hierarchy process, benchmark weights are determined for the basic evaluation indicators. The baseline weights are adjusted using historical monitoring data to obtain the final weight allocation result.

5. The intelligent dispatching method for emergency rescue points in dynamic risk scenarios according to claim 1, characterized in that, The total hazard score is obtained by combining the basic assessment indicators, the emergency indicators, and the weight allocation results, satisfying the following: in, Indicates the total risk level score. This represents the score of the i-th basic evaluation index. This indicates the weight allocation result. This indicates bonus points for emergency indicators.

6. The intelligent dispatching method for emergency rescue points in dynamic risk scenarios according to claim 1, characterized in that, The rescue resource capacity coefficient satisfies: When the comprehensive score of vehicle performance, personnel skills, and equipment configuration is 90-100 points, the corresponding rescue resource capability coefficient is linearly 1.2-1.

3. When the comprehensive score of vehicle performance, personnel skills, and equipment configuration is 70-90 points, the rescue resource capability coefficient corresponds linearly to 1.0-1.

2. When the comprehensive score of vehicle performance, personnel skills, and equipment configuration is 50-70 points, the corresponding rescue resource capability coefficient is linearly 0.9-1.

0. And satisfy: in, Indicates the coefficient of rescue resource capacity. It represents a comprehensive score based on vehicle performance, personnel skills, and equipment configuration.

7. The intelligent dispatching method for emergency rescue points in dynamic risk scenarios according to claim 1, characterized in that, The road traffic condition coefficient satisfies: When the combined score of the number of lanes, emergency lane, and road surface condition is 90-100 points, the road traffic condition coefficient corresponds linearly to 1.1-1.

2. When the combined score of the number of lanes, emergency lane, and road surface condition is 70-90 points, the road traffic condition coefficient corresponds linearly to 1.0-1.

1. When the combined score of the number of lanes, emergency lane, and road surface condition is 50-70 points, the road traffic condition coefficient corresponds linearly to 0.8-1.

0. And satisfy: in, This represents the road traffic condition coefficient. This represents a comprehensive score based on the number of lanes, emergency lanes, and road surface conditions.

8. The intelligent dispatching method for emergency rescue points in dynamic risk scenarios according to claim 1, characterized in that, The coverage radius of emergency rescue points is matched using the total hazard level score, satisfying the following: in, Indicates the coverage radius. Indicates the reference coverage radius. Indicates the coefficient of rescue resource capacity. This represents the road traffic condition coefficient.

9. The intelligent dispatching method for emergency rescue points in dynamic risk scenarios according to claim 1, characterized in that, Obtaining the optimal layout scheme based on the coverage radius includes the following steps: Build an fitness function based on coverage completeness, deployment flexibility, and operating costs; Based on the coverage radius and the fitness function, the optimal layout scheme is obtained through a genetic algorithm.

10. An intelligent dispatch system for emergency rescue points in dynamic risk scenarios, characterized in that, The intelligent dispatch system for emergency rescue points in dynamic risk scenarios includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, which are used to execute the intelligent dispatch method for emergency rescue points in dynamic risk scenarios according to any one of claims 1-9.

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