Highway tunnel operation resilience assessment method and device, terminal and medium

Abstract

The application provides a highway tunnel operation resilience evaluation method, device, terminal and medium. The method comprises the following steps: acquiring multi-source data and adjacent tunnel pile number spacing data; processing the multi-source data to obtain an operation risk condition index and an operation guarantee capacity index; obtaining a static operation resilience margin index according to the operation risk condition index and the operation guarantee capacity index; obtaining a dynamic operation resilience measurement index according to the operation risk condition index, the operation guarantee capacity index and the adjacent tunnel pile number spacing data; and determining the operation resilience grade of the highway tunnel according to the static operation resilience margin index and the dynamic operation resilience measurement index. The method can be used to accurately grasp the highway tunnel operation safety performance, reduce the tunnel operation risk level and improve the tunnel operation guarantee capacity.

Description

Technical Field

[0001] This invention relates to the field of highway tunnel management technology, specifically to a method, device, terminal, and medium for assessing the operational resilience of highway tunnels. It is applicable to the graded management and control of safety risks during the operation of highway tunnels, the simulation of resilience status throughout the entire process, and the optimized allocation of operation and maintenance resources. Background Technology

[0002] Highway tunnels, characterized by semi-enclosed civil engineering structures and complex and diverse electromechanical facilities, exhibit greater vulnerability compared to other transportation infrastructure during operation. With increasing service life, highway tunnels now face more diverse types of risks, with existing risks accumulating and new risks constantly emerging; the risk structure is more complex, with various risks overlapping and transmitting within the enclosed space. Given that current technology cannot completely contain or avoid all risks and disasters, the industry is gradually introducing the "resilience concept," which accepts the inevitable reality that tunnels may be affected by unexpected events and proactively enhances the tunnel system's resistance, absorption, and recovery capabilities through various measures.

[0003] Currently, the evaluation technologies for highway tunnels in the industry mainly focus on the following three aspects: (1) Tunnel risk assessment: The current "Specification for Safety Evaluation of Highway Projects" (JTG B05-2015) mainly focuses on the design and construction risk assessment during the tunnel construction period. Although it mentions operational safety assessment, it mainly targets static design elements such as alignment indicators and has little to do with dynamic risks in the actual operation and management process. The proposed "Technical Specification for Highway Traffic Safety Risk Assessment" (JTG / T 222*) focuses on traffic safety risks, but its evaluation objects are concentrated on the highway network dimension and lack a refined assessment model for the specific operating environment inside individual tunnel projects (especially long tunnels).

[0004] (2) Physical and maintenance status assessment: The industry mainly uses the "Technical Condition Assessment Standard for Highway Tunnels" (JTG H12) to assess the physical and technical condition of civil structures and electromechanical facilities. In addition, although the "National Highway Network Key Bridge and Tunnel Monitoring and Evaluation Regulations" (T / CECSG:E41-04-2019) introduces a maintenance monitoring and evaluation mechanism, its assessment logic often focuses on sampling evaluation at the regional road network level, and its focus is more on evaluating the performance of maintenance units. Its main goal is not to reflect the real-time safety performance of the tunnel system itself when facing risks.

[0005] (3) Tunnel toughness assessment: With the rise of toughness theory, some technical solutions have attempted to apply it to the tunnel field. For example, the prior art document CN115439032A discloses "A Highway Tunnel Toughness Evaluation Model and Method", and the prior art document CN118195133A discloses "A Highway Tunnel Fire Resistance Toughness Assessment Method". However, most of the above-mentioned existing technologies are aimed at evaluating specific disaster scenarios (such as the latter only considering fire) or the post-disaster recovery stage, focusing on assessing the "safety toughness" of tunnels under extreme events, and failing to fully consider the dynamic relationship between "risk accumulation" and "guarantee capability" of tunnels in daily operation, that is, the "operational toughness" of tunnels.

[0006] While the aforementioned existing technologies have played a significant role in ensuring the safety of tunnel structures, they still have the following significant shortcomings when facing increasingly complex operating environments and the demands of building "resilient transportation": (1) Risk offensiveness and system defensiveness assessments are separated, lacking a quantitative model of "supply and demand coupling". Existing technologies usually treat "risk assessment" (demand side) and "facility assessment" (supply side) as two separate tasks, lacking a unified mathematical model to measure the surplus relationship between the two. In actual operation, if a high-risk tunnel has a very high level of emergency support capabilities (such as dual redundancy facilities and efficient rescue teams), its system resilience may still be within a safe range; conversely, a low-risk tunnel may be extremely vulnerable if management is paralyzed. Existing technologies cannot calculate the "safety resilience margin" of the system under current risk stress, making it impossible for managers to accurately judge the critical point when the system is close to "functional failure".

[0007] (2) Existing technologies often use a simple linear weighted summation model when assessing risks. However, in actual operation, there is a significant nonlinear coupling amplification effect between different types of risks (for example, rainstorms are not only an environmental risk, but can also induce traffic accidents and thus become traffic risks). Existing linear models assume that each risk exists independently and ignore the catastrophic energy generated by the superposition of cross-domain risks, resulting in an underestimation of the risk level under extremely complex working conditions.

[0008] (3) Static physical indicators are difficult to quantify dynamic “management soft power” and “technology empowerment”. The existing technical condition assessment (H12) system focuses on the physical condition of facilities (hard indicators) and ignores the “management efficiency” (soft indicators) of operation and maintenance units, such as system construction, emergency response timeliness and material reserve efficiency. At the same time, with the application of new technologies such as digital twins, intelligent monitoring and proactive prevention and control, traditional evaluation standards are difficult to cover the beneficial effects of these technologies on tunnel safety performance, resulting in conservative or distorted evaluation results that fail to reflect the value of “technology empowerment resilience”.

[0009] (4) Existing models are mostly limited to isolated evaluations from the perspective of a single tunnel, neglecting the network correlation of highway traffic flow. In sections where tunnels appear consecutively or in dense tunnel groups, the low guarantee capacity (weak link) of adjacent tunnels will have a significant "delay effect" on the recovery of the tunnel through traffic flow congestion backtracking and competition for emergency resources. Most existing evaluations ignore the constraint effect of this spatial neighborhood on the recovery rate.

[0010] (5) The lack of spatiotemporal quantification methods for the entire process of disaster evolution makes it difficult to distinguish between "current safety" and "recovery potential". Existing evaluation systems are mostly static evaluations. However, the core of tunnel resilience lies not only in "whether it is safe at this moment" (static margin), but also in the "recovery rate" (dynamic potential) after damage. Existing linear weighted or simple ratio models cannot construct a time evolution function of "performance degradation-absorption-recovery", which makes it impossible to distinguish between the two fundamentally different system states of "low risk but slow recovery (vulnerable)" and "high risk but fast recovery (robust)", resulting in a lack of depth in operation and maintenance decisions. Summary of the Invention

[0011] To address the shortcomings of existing technologies, this invention proposes a method, device, terminal, and medium for assessing the operational resilience of highway tunnels, aiming to accurately assess the performance of highway tunnel operations, reduce tunnel operational risks, and enhance tunnel operational support capabilities.

[0012] In a first aspect, embodiments of this application provide a method for assessing the operational resilience of highway tunnels, the method comprising: Acquire multi-source data and adjacent tunnel station spacing data; The multi-source data is processed to obtain the operational risk status index and the operational support capability index; Based on the operational risk status index and the operational support capability index, the static operational resilience margin index is obtained; Based on the operational risk status index, the operational support capability index, and the adjacent tunnel spacing data, a dynamic operational resilience measurement index is obtained. The operational resilience level of highway tunnels is determined based on the static operational resilience margin index and the dynamic operational resilience measurement index.

[0013] Optionally, the processing of the multi-source data to obtain the operational risk status index and the operational support capability index includes: The multi-source data is rated according to the risk assessment method to obtain tunnel risk data; the tunnel risk data includes the statistical quantity of each level of tunnel risk; Based on the tunnel risk data, an operational risk status index is obtained; Based on the multi-source data, operational functionality score, standard adaptability score, and maintenance effectiveness score are obtained; The operational support capability index is obtained based on the operational functionality score, the standard adaptability score, and the maintenance effectiveness score.

[0014] Optionally, obtaining the operational risk status index based on the tunnel risk data includes: The basic cumulative risk value is obtained by calculating the tunnel risk data using a linear weighting method. Based on the tunnel risk data, the regional risk energy is obtained; Based on the preset strength matrix and tunnel risk data, the cross-domain coupling increment is obtained; The operational risk status index is obtained based on the basic cumulative risk value and the cross-domain coupling increment.

[0015] Optionally, obtaining the operational functionality score, standard adaptability score, and maintenance effectiveness score based on the multi-source data includes: The multi-source data is rated according to a preset operational functionality evaluation method to obtain an operational functionality score; The multi-source data is rated according to a preset standard adaptability evaluation method to obtain a standard adaptability score; The multi-source data is rated according to the preset management effectiveness evaluation method to obtain a management effectiveness score.

[0016] Optionally, obtaining the dynamic operational resilience measurement index based on the operational risk status index, the operational support capability index, and the adjacent tunnel spacing data includes: The maximum performance loss depth is obtained based on the operational risk status index. The network-level performance recovery rate is obtained based on the operational support capability index and the distance data between adjacent tunnel stations. A dynamic operational resilience metric index is obtained based on the maximum performance loss depth and the network-level performance recovery rate.

[0017] Optionally, obtaining the network-level performance recovery rate based on the operational support capability index and the adjacent tunnel stationing data includes: in, For network-level performance recovery rate; The baseline recovery coefficient; This serves as the current assessment index for the tunnel's security capabilities. For the first The guarantee capacity index of adjacent tunnels; This is the resistance sensitivity coefficient; This refers to the spacing data between adjacent tunnel stations; The radius of influence is a characteristic.

[0018] Optionally, the process of obtaining the dynamic operational resilience metric index based on the maximum performance loss depth and network-level performance recovery rate includes: in, It serves as a dynamic operational resilience metric. The maximum performance loss depth; For network-level performance recovery rate; To assess the normalized total duration.

[0019] Secondly, embodiments of this application provide a highway tunnel operational resilience assessment device, comprising: The data acquisition module is used to acquire multi-source data and distance data between adjacent tunnel stations; The operation index determination module is used to process the multi-source data to obtain the operation risk status index and the operation support capability index. The static resilience determination module is used to obtain a static operational resilience margin index based on the operational risk status index and the operational support capability index. The dynamic resilience determination module is used to obtain a dynamic operational resilience measurement index based on the operational risk status index, the operational support capability index, and the adjacent tunnel stationing distance data. The resilience level determination module is used to determine the operational resilience level of highway tunnels based on the static operational resilience margin index and the dynamic operational resilience metric index.

[0020] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the highway tunnel operational resilience assessment method as described in any one of the first aspects above.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the highway tunnel operational resilience assessment method as described in any one of the first aspects above.

[0022] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the highway tunnel operational resilience assessment method described in any of the first aspects above.

[0023] In this embodiment, the application acquires multi-source data and adjacent tunnel stationing distance data; processes the multi-source data to obtain an operational risk status index and an operational support capability index; obtains a static operational resilience margin index based on the operational risk status index and the operational support capability index; obtains a dynamic operational resilience measurement index based on the operational risk status index, the operational support capability index, and the adjacent tunnel stationing distance data; and determines the operational resilience level of the highway tunnel based on the static operational resilience margin index and the dynamic operational resilience measurement index. This achieves accurate understanding of the performance of highway tunnel operation, reduces the level of tunnel operational risk, and improves the operational support capability of highway tunnels. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0025] Figure 1 This is a flowchart illustrating the first embodiment of the highway tunnel operational resilience assessment method provided in this application; Figure 2 This is a flowchart illustrating the overall implementation calculation of the first embodiment of the highway tunnel operational resilience assessment method provided in this application. Figure 3 This is a schematic diagram of the highway tunnel operation risk status index structure of the first embodiment of the highway tunnel operation resilience assessment method provided in this application; Figure 4 This is a schematic diagram of the highway tunnel operation support capability index structure of the first embodiment of the highway tunnel operation resilience assessment method provided in this application; Figure 5 This is a schematic diagram of the whole-process evolution model of tunnel operation performance based on system dynamics, which is a first embodiment of the highway tunnel operation resilience assessment method provided in this application. Figure 6 This is a schematic diagram of the structure of the highway tunnel operation resilience assessment device provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0026] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0027] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0028] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] Figure 1 The diagram illustrates a first embodiment of the highway tunnel operational resilience assessment method provided in this application. It is provided as an example and not a limitation; this method can be applied to the aforementioned highway tunnel operational resilience assessment device. Figure 1 As shown, the method may include: S10, acquire multi-source data and adjacent tunnel station spacing data; In order to accurately grasp the operational safety performance of highway tunnels, reduce the level of tunnel operation risks, and improve the operational support capability of tunnels, the highway tunnel operation resilience assessment device acquires multi-source data and station spacing data of adjacent tunnels.

[0030] As one implementation method, the highway tunnel operation resilience assessment device can construct a multi-dimensional evaluation index system by acquiring multi-source data. The highway tunnel operation resilience assessment index system is established, which includes two independent and coupled dimensions: (1) Operation risk status (numerator): including four secondary indicators: structural facility risk, external environment risk, traffic operation risk and operation control risk, as well as corresponding tertiary risk source indicators such as lining blockage, rainstorm, high traffic saturation, and maintenance road occupation. (2) Operation support capability (denominator): including three secondary indicators: operation functionality, standard adaptability, and maintenance effectiveness, as well as tertiary quantitative indicators such as physical technical condition, resilience facility redundancy, disaster prevention structure setting, and emergency response timeliness.

[0031] The multi-source data includes: traffic flow and environmental data obtained through the tunnel electromechanical monitoring system; institutional data, management record data and material data obtained through the maintenance management platform; civil engineering scores and electromechanical scores obtained through the periodic inspection database; and real-time or statistical values ​​of various indicators (highway tunnel operation resilience assessment index system) obtained through multiple channels such as manual entry.

[0032] The overall implementation process of the highway tunnel operational resilience assessment method in this application is as follows: Figure 2 As shown.

[0033] S20, Process the multi-source data to obtain the operational risk status index and the operational support capability index; After acquiring multi-source data, the highway tunnel operation resilience assessment device processes the multi-source data to obtain an operation risk status index. and operational support capability index Among them, the operational risk status index For highway tunnel operation risk status index; operation support capability index This refers to the index of operational support capacity for highway tunnels.

[0034] To address the multi-source risks faced by tunnels, this application constructs a domain-based aggregation trigger-coupled model that combines "low-risk linear accumulation with high-risk nonlinear coupling" to calculate the operational risk status index. This model can balance computational convenience under low-risk conditions with the representation of disaster cascading effects under high-risk conditions.

[0035] As one implementation method, the multi-source data is processed to obtain an operational risk status index and an operational support capability index, specifically including: A1. The multi-source data is rated according to the risk assessment method to obtain tunnel risk data; the tunnel risk data includes the statistical quantity of each level of tunnel risk; After acquiring multi-source data, the highway tunnel operational resilience assessment device rates the multi-source data according to a risk assessment method to obtain tunnel risk data. The tunnel risk data includes statistical data on the number of risks at each level of the tunnel. Typical risk types for highway tunnels are shown in Table 1 and below. Figure 3 .

[0036] That is, common risk assessment methods such as the Job Condition Hazard Assessment (LEC) method or the risk matrix method are used to rate the three levels of risk sources that exist at the current moment, and classify them into Level I (extremely high risk), Level II (high risk), Level III (medium risk), and Level IV (low risk).

[0037] Table 1 Typical Operational Risks of Highway Tunnels A2, Based on the tunnel risk data, the operational risk status index is obtained; As one implementation method, an operational risk status index is obtained based on the tunnel risk data, including: B1. The basic cumulative risk value is obtained by calculating the tunnel risk data using a linear weighting method. After obtaining tunnel risk data, the highway tunnel operational resilience assessment device uses a linear weighting method to calculate the basic cumulative risk value. ; After obtaining tunnel risk data (i.e., the number of risks at each level), the highway tunnel operational resilience assessment device uses a linear weighted method to calculate and statistically calculate the risk of all levels of the entire tunnel (i.e., to calculate the basic cumulative risk value). ).

[0038] In the formula, Based on the cumulative risk value; The risk level is The formula represents the number of risk sources at each level, with i=1, 2, 3, 4 corresponding to levels I, II, III, and IV risks, respectively. The exponential coefficient difference (100:10:1:0.1) reflects the devastating impact of high-level risks on system security.

[0039] B2, based on the tunnel risk data, obtain the domain risk energy; After obtaining tunnel risk data, the highway tunnel operational resilience assessment device calculates the domain-specific risk energy based on that data. ; Divide the risk sources Four categories (1 for structural facilities, 2 for external environment, 3 for traffic operation, and 4 for operation control) were used to count the number of Level I and Level II risks in each category.

[0040] in, For domain-specific risk energy; The number of Level I risks; The number of Level II risks is set at 100:10, consistent with the weighting system of the basic cumulative risk value, to strengthen the energy representation of high-level risks.

[0041] B3, based on the preset strength matrix and tunnel risk data, obtains the cross-domain coupling increment. ; After obtaining tunnel risk data, the highway tunnel operational resilience assessment device, based on a preset strength matrix and the tunnel risk data, derives the cross-domain coupling increment. ; A "trigger mechanism" is introduced, which initiates calculations only when a high-level risk (Level I or Level II) exists in the system; otherwise... .

[0042] As one implementation method, based on a preset strength preset matrix and tunnel risk data, a cross-domain coupling increment is obtained, including: C1, if the risk energy of the domain is... If the value is greater than zero, the tunnel risk data is processed according to the preset strength matrix to obtain the cross-domain coupling increment. ; Among them, based on the principle of quadratic aggregation, the interaction between each pair of different risk categories is calculated.

[0043] In the formula: For cross-domain coupling increments; The coupling sensitivity coefficient is set to 0.15. The preset cross-domain coupling strength coefficient is detailed in the Risk Coupling Matrix Parameter Setting Table (Table 2). Risk category A; It falls under risk category B.

[0044] To comprehensively cover the interaction of the four major types of risks during tunnel operation, this invention constructs a risk coupling matrix based on the accident chain evolution mechanism, as detailed in Table 2.

[0045] Table 2 Preset Matrix of Risk Coupling Strength Across Highway Tunnels C2, if the risk energy of the domain If the value is zero, then the cross-domain coupling increment is zero. ; A "trigger mechanism" is introduced, which initiates calculations only when a high-level risk (Level I or Level II) exists in the system; otherwise... .

[0046] B4. Based on the aforementioned basic cumulative risk value and the aforementioned cross-domain coupling increment, the operational risk status index is obtained.

[0047] After obtaining the basic cumulative risk value and the cross-domain coupling increment, the highway tunnel operation resilience assessment device derives an operation risk status index based on the basic cumulative risk value and the cross-domain coupling increment. .

[0048] Operational Risk Status Index Based on the cumulative risk value and coupling correction value (cross-domain coupling increment) Composition. Among them... .

[0049] A3. Based on the multi-source data, the operational functionality score, standard adaptability score, and maintenance effectiveness score are obtained. After obtaining tunnel risk data, the highway tunnel operation resilience assessment device generates operation functionality score, standard adaptability score, and maintenance effectiveness score based on the multi-source data. The operation functionality evaluation index, standard adaptability evaluation index, and maintenance effectiveness evaluation index are shown in Tables 3, 5, and 6, respectively.

[0050] As one implementation method, based on the multi-source data, operational functionality scores, standard adaptability scores, and maintenance effectiveness scores are obtained, specifically including: D1. The multi-source data is rated according to the preset operational functionality evaluation method to obtain an operational functionality score. Operational functionality evaluation, also known as operational functionality score, can be calculated by weighting the physical technical condition (based on the technical condition of civil structures, electromechanical facilities, and other engineering facilities as measured by regular inspections) and the redundancy of resilience facilities (such as whether incremental facilities such as intelligent monitoring and flexible protection are configured). The value description is shown in Table 3.

[0051] Table 3. Operational Functional Evaluation Indicators and Value Explanation Because the configuration of electromechanical facilities varies greatly depending on the tunnel length, different weighting parameters are applied to civil engineering, electromechanical engineering, and other engineering projects based on the actual technical conditions and the different tunnel types, as detailed in Table 4.

[0052] Table 4. Weight values ​​of parameters for different tunnel types D2, the multi-source data is rated according to a preset standard adaptability evaluation method to obtain a standard adaptability score; The standard adaptability evaluation, also known as the standard adaptability score, can be scored based on the degree of conformity between the tunnel's actual geometric parameters (clearance, alignment, slope, etc.), electromechanical facility configuration, disaster prevention structure (such as pedestrian and vehicular cross passage density, emergency parking lane setting) and current specifications. The value explanation is shown in Table 5.

[0053] Table 5. Standard Adaptability Evaluation Indicators and Value Explanation D3. The multi-source data is rated according to the preset management effectiveness evaluation method to obtain the management effectiveness score.

[0054] The evaluation of maintenance effectiveness, also known as the maintenance effectiveness score, can be achieved by quantifying key "soft power" indicators, including the establishment of systems, the rate of personnel holding certificates, the timeliness of emergency response (based on the average time of historical drills), and the closed-loop rate of hazard management. See Table 6 for the value explanation.

[0055] Table 6. Evaluation Indicators and Values ​​for Maintenance Effectiveness A4. Based on the operational functionality score, the standard adaptability score, and the maintenance effectiveness score, the operational support capability index is obtained. .

[0056] A weighted comprehensive scoring method is used to quantify the tunnel's overall ability to withstand risks, which yields the operational support capability index. .in, In the formula, For operational support capability index; Weights for each level of indicators, Each indicator is scored. The specific scoring dimensions include three parts: operational functionality score, standard adaptability score, and maintenance effectiveness score, with weights of 0.5, 0.2, and 0.3 respectively. For details of the evaluation system, please refer to [link to evaluation system]. Figure 4 .

[0057] S30. Based on the operational risk status index and the operational support capability index, the static operational resilience margin index is obtained. After obtaining the operational risk status index and the operational support capability index, the highway tunnel operational resilience assessment device derives a static operational resilience margin index based on these two indices. .

[0058] As one implementation method, a static operational resilience margin index is obtained based on the operational risk status index and the operational support capability index, specifically including: in, This is a static operational resilience margin index; This is an index representing the operational risk status. For operational support capability index; In other words, based on a supply-demand coupling model, the static operational toughness margin of the tunnel is calculated. This indicator characterizes the "safety capacity" of the system remaining after offsetting current risks, which can be used to absorb shocks and restore functionality. Core calculation formula: Correction rule: If (Loss of security capability) then judge ; If the calculation result (Risk spillover), then corrected to This indicates that the system is in an extremely dangerous state of depleted resilience.

[0059] S40. Based on the operational risk status index, the operational support capability index, and the adjacent tunnel stationing distance data, a dynamic operational resilience measurement index is obtained. After obtaining the operational risk status index, operational support capability index, and adjacent tunnel spacing data, the highway tunnel operational resilience assessment device derives a dynamic operational resilience measurement index based on these data. .

[0060] As one implementation method, a dynamic operational resilience measurement index is obtained based on the operational risk status index, the operational support capability index, and the adjacent tunnel spacing data, specifically including: E1, based on the operational risk status index, yields the maximum performance loss depth; After obtaining the operational risk status index, the highway tunnel operation resilience assessment device uses the operational risk status index to... To obtain the maximum performance loss depth .

[0061] To quantitatively assess the dynamic characteristics of a tunnel during the entire "absorption-recovery" process after being subjected to a risk impact, toughness is no longer considered a static ratio, but rather a full-process evolution assessment is performed to derive the integral area under the performance curve (e.g., Figure 5 As shown, this invention establishes a system performance evolution function that includes a performance degradation phase and a recovery and improvement phase. .

[0062] Maximum performance loss depth Characterizing the tunnel system's resistance to and absorption of risk shocks, and its operational risk status index. It exhibits a non-linear positive correlation. Maximum performance loss depth. ; in: This is an operational risk index, with a value range of [0, 100]. The risk sensitivity coefficient is recommended to have a value range of [0.12, 0.18], with a preferred value of 0.15, to ensure that the system performance retention is always greater than 0 under extreme risk.

[0063] E2, based on the aforementioned operational support capability index The network-level performance recovery rate is obtained by combining the distance data between the station numbers of adjacent tunnels. ; The highway tunnel operation resilience assessment device obtained the operation support capability index. After obtaining the station spacing data of adjacent tunnels, the operational support capability index is used. The network-level performance recovery rate is obtained by combining the distance data between the station numbers of adjacent tunnels. .

[0064] At the road network level, traffic recovery depends on the weakest nodes on each road segment. Network-level performance recovery rate. Characterizes the network-level recovery and improvement capability of a tunnel system after damage, taking into account the spatial neighborhood blocking effect.

[0065] Network-level performance recovery rate It can be done Calculated.

[0066] in, The basic recovery rate of a single tunnel; The network-level resistance coefficient is used to evaluate objects that are in tunnel group conditions or have adjacent tunnels.

[0067] Under single tunnel operating conditions, the tunnel system's recovery and improvement capabilities after damage are correlated with its operational support capability index. It exhibits a non-linear exponential growth relationship, with a basic recovery rate The calculation formula is: in, The operational support capability index calculated in step S3 has a range of values. ; The baseline recovery factor is recommended to have a range of values ​​of [value range missing]. The preferred value is 0.05 to ensure that the system can complete functional recovery within one evaluation cycle under standard assurance capabilities.

[0068] Network-level hysteresis coefficient The calculation introduces a triggering mechanism: Isolated tunnel: If there are no other tunnels within a 5km radius before and after the tunnel entrance, then ,at this time .

[0069] Continuous tunnels: if they exist There are several adjacent tunnels (or the tunnel is located in a tunnel group), and the protection capacity of the adjacent tunnels is lower than that of the current tunnel (i.e., If this is the case, then the hysteresis calculation is triggered, where the network-level hysteresis coefficient is... The calculation formula is: in, This serves as the current assessment index for the tunnel's security capabilities. For the first The guarantee capacity index of adjacent tunnels. Only statistics are included. Short-sighted tunnels will be replaced by more robust tunnels to prevent obstruction. This is the resistance sensitivity coefficient, which can be set to 0.5. It represents the weighting of the resistance level of nearby tunnels on the recovery rate of this tunnel.

[0070] in, This is a spatial attenuation function. The closer the continuous tunnels are, the greater the impact. ;in, The distance between the entrances of adjacent tunnels and the current tunnel is expressed in km. This is the characteristic influence radius (which can be taken as 3km). Beyond this distance, the influence decays rapidly.

[0071] As one implementation method, based on the aforementioned operational support capability index The network-level performance recovery rate is obtained by combining the distance data between the station numbers of adjacent tunnels. ,include: in, For network-level performance recovery rate; The baseline recovery coefficient; This serves as the current assessment index for the tunnel's security capabilities. For the first The guarantee capacity index of adjacent tunnels; This is the resistance sensitivity coefficient; This refers to the spacing data between adjacent tunnel stations; The radius of influence is a characteristic.

[0072] in, E3, based on the maximum performance loss depth and network-level performance recovery rate To obtain a dynamic operational resilience metric index .

[0073] The highway tunnel operation toughness assessment device obtained the depth of maximum performance loss. and network-level performance recovery rate Then, based on the maximum performance loss depth and network-level performance recovery rate To obtain a dynamic operational resilience metric index .

[0074] The highway tunnel operation toughness assessment device obtained the depth of maximum performance loss. and network-level performance recovery rate Then, a piecewise evolution function can be constructed. .set up The moment the event is triggered. To evaluate the normalized total duration (e.g., taking...) Initial performance (i.e., 100%).

[0075] when (Absorption and Decline Period): The system performance drops instantly from P0 to its lowest point. .

[0076] when (Recovery and Recovery Period): System performance follows a linear recovery trajectory, i.e. .

[0077] when (Stability period): System performance recovers to... .

[0078] Then, calculate the dynamic operational resilience metric index. Based on the above evolutionary model, the performance curves were calculated. The integral area enclosed by the time axis quantifies the tunnel's resilience throughout the entire spatiotemporal dimension. The formula is: For ease of engineering application, this integral formula can be simplified to geometric calculation. in, The area of ​​the performance loss triangle is calculated as follows. In practice, it is assumed that the absorption time is extremely short and negligible; the overall calculation is simplified to the area of ​​a triangle. If the calculated recovery time exceeds the total evaluation time... If so, it is calculated based on the cross-sectional area.

[0079] Dynamic operational resilience index The final calculation formula is: in: This is a dynamic operational resilience metric index, with a value range of [value range missing]. , usually corrected to ; The maximum performance loss depth characterizes the magnitude of the risk impact; The network-level performance recovery rate (i.e., the rate after considering the spatial neighborhood blocking effect) characterizes the system's recovery efficiency. To assess the normalized total duration (suggested method is to take...), Coefficient 1 represents the ideal performance state of the system. (Normalized to 1).

[0080] As one implementation method, based on the maximum performance loss depth and network-level performance recovery rate To obtain a dynamic operational resilience metric index ,include: in, It serves as a dynamic operational resilience metric. The maximum performance loss depth; For network-level performance recovery rate; To assess the normalized total duration.

[0081] S50 determines the operational resilience level of highway tunnels based on the static operational resilience margin index and the dynamic operational resilience measurement index. The highway tunnel operation resilience assessment device obtained the static operation resilience margin index. and dynamic operational resilience metrics Subsequently, based on the static operational resilience margin index and dynamic operational resilience metrics Based on the preset evaluation criteria, highway tunnels are classified and given early warnings, and the operational resilience level of highway tunnels is finally determined.

[0082] In other words, the highway tunnel operation resilience assessment device obtains the static operation resilience margin index. and dynamic operational resilience metrics Subsequently, the static operational resilience margin index can be calculated separately. or dynamic operational resilience index The system uses a tiered early warning system based on preset evaluation criteria. The dynamic resilience grading criteria developed in this invention are shown in Table 7. Both static and dynamic operational resilience assessment results can provide decision support for specific maintenance and repair funding allocations.

[0083] Table 7: Classification of Highway Tunnel Operational Resilience In this embodiment, an in-service highway tunnel on a national or provincial trunk line (hereinafter referred to as "Tunnel A") is selected as the evaluation object, and its operational resilience is quantitatively evaluated using the evaluation method of this invention. The evaluation process mainly includes three steps: calculation of operational risk status, calculation of operational support capability, and determination of tunnel operational resilience.

[0084] Tunnel A, a trunk line in a certain province, opened to traffic in 2006. The tunnel is 3205 meters long. The latest technical condition assessment results are: Civil engineering structure, Class 2 (76.50); ​​Electromechanical facilities, Class 1 (92.50); ​​Other engineering facilities, Class 1 (100). No strong earthquakes or extreme weather events have occurred in the tunnel site area over the years. Tunnel B is located 2 km downstream of Tunnel A.

[0085] Step A2, Tunnel Operation Risk Status Index calculate (a) Calculation of the actual operational risk of Tunnel A According to the "Interim Measures for Safety Production Risk Management in the Highway and Waterway Industry" and related regulations, the identified risk sources are classified into four categories based on their severity: Level I (major risk), Level II (relatively high risk), Level III (general risk), and Level IV (minor risk). The statistics of the number of risks in each category for Tunnel A are shown in Table 8.

[0086] Table 8. Statistical Table of Risk Identification for Tunnel A No Level I or II risks have been detected in Tunnel A. Based on the triggering mechanism, its coupling correction value... To verify the effectiveness of the model of this invention, hypothetical scenario B was constructed for deduction: Suppose that tunnel A is hit by a "severe rainstorm," which induces a "traffic accident inside the tunnel." The following newly identified high-level risks are: Structural facilities domain No new high-risk areas were reported.

[0087] External environment domain One rainstorm disaster was reported (classified as Level II).

[0088] Traffic Operations Domain One multi-vehicle rear-end collision (classified as Level II).

[0089] Operation control domain No new high-risk areas were reported.

[0090] Based on the data for Tunnel A shown in Table 8 and the assumptions of Scenario B, the number of Level I risks... Level II risk quantity Level III risk quantity Level IV risk quantity .

[0091] (1) Basic cumulative risk value calculate (2) Domain-specific risk energy calculate (3) Cross-domain coupling increment calculate Based on the principle of quadratic aggregation, the pairwise interactions between different risk categories are calculated. Scanning revealed... and Non-zero values ​​trigger the "environment-traffic" coupling term. Referring to the preset matrix of cross-domain risk coupling strength for highway tunnels (Table 2 above), we obtain... .

[0092] (4) Operational Risk Status Index calculate Total risk increment =Basic Increment 10 points + Coupling Correction 1.2 points = 11.2 points. The coupling term contributed approximately 12% of the additional risk value, which accurately quantifies the implicit cross-domain coupling risk of "difficulty in handling accidents in rainy weather".

[0093] Step A4, Tunnel Operation Support Capability Index calculate (a) Operational functionality Tunnel A is 3205 meters long and is classified as an extra-long tunnel. According to Table 9, the weight values ​​of the parameters for extra-long tunnels are selected as follows: Civil Engineering Structure (0.45), Mechanical and Electrical Facilities (0.45), and Other Engineering Facilities (0.1).

[0094] Table 9. Weight values ​​of various parameters for different tunnel types Based on the latest tunnel technical condition assessment results and toughness settings of Tunnel A, the specific quantitative results of the operational functionality indicators of Tunnel A are shown in Table 10.

[0095] Table 10 Evaluation Table of Operational Functional Indicators for Tunnel A Therefore, the operational functionality indicators are calculated using the weighted comprehensive scoring method as follows: (ii) Standard Adaptability Although Tunnel A was completed and opened to traffic in 2026, its civil engineering structure geometry, alignment, and other parameters all meet the relevant requirements of the "Specifications for Design of Highway Tunnels, Volume 1: Civil Engineering" (JTG 3370.1-2018). Furthermore, its electromechanical facilities, through continuous renovations and upgrades during operation, also meet the relevant requirements of the "Specifications for Design of Highway Tunnels, Volume 2: Traffic Engineering and Ancillary Facilities" (JTGD70 / 2-2014). The specific quantitative results of Tunnel A's standard adaptability indicators are shown in Table 11.

[0096] Table 11. Standard Adaptability Evaluation Indicators and Values ​​for Tunnel A Therefore, the standard adaptability index is calculated using the weighted comprehensive scoring method as follows: (III) Effectiveness of Management Based on the requirements of the "National Highway Network Key Bridge and Tunnel Monitoring and Evaluation Regulations" (TCECS GE41-04-2019) and other relevant regulations, the superior unit conducted a standardized inspection of the maintenance of Tunnel A, and the results are as follows.

[0097] The tunnel has established management, funding, and maintenance systems, but lacks a safety system; 90% of special operations personnel hold certificates, and safety and technical training are conducted annually; maintenance machinery and equipment in the ledgers and equipment warehouse meet the required standards. A tunnel emergency plan has been developed and filed in accordance with the "Guidelines for the Preparation of Emergency Response Plans for Production Safety Accidents in Production and Business Units" (GB / T 29639-2020), but emergency supplies such as emergency lighting are lacking; historical drills or accident data show an average on-site response time of 20 minutes; a joint drill or meeting is conducted annually across units; the closed-loop rate for hazard identification and management is 94%. The specific quantitative results of the tunnel A management effectiveness indicators are shown in Table XII.

[0098] Table XII. Evaluation Indicators and Values ​​for Tunnel A Maintenance Effectiveness Therefore, the maintenance effectiveness index is calculated using the weighted comprehensive scoring method as follows: (iv) Operational Support Capability Index Based on the weights of each secondary indicator (operational functionality 0.5, standard adaptability 0.2, maintenance effectiveness 0.3), substitute them into formula (2) respectively. Weighting: The calculation results show that the operational support capability index of Tunnel A is 81.71. This value reflects the tunnel's ability to ensure normal operation under the current maintenance conditions.

[0099] Step S30: Tunnel Static Operational Resilience Margin Index calculate Substitute the tunnel operation risk index calculated in the first two steps and tunnel operation support capability index The static operational resilience margin index was calculated. .

[0100] Step S40: Tunnel Dynamic Operational Resilience Margin Index calculate (a) Initial performance The setting is 100%, which represents the ideal operating state of the tunnel under no-event interference.

[0101] (ii) Maximum performance loss depth Based on the operational risk status index Driven by the nonlinear mapping function (Equation 7) used in this invention, the adjustment coefficient is set. Substitute the operational risk status of Tunnel A data : That is, at the current risk level, once a triggering event occurs, the system performance is expected to decrease by 57.5%, with the remaining performance... .

[0102] (iii) Network-level performance recovery rate Based on its own operational support capability index It is determined by both the bottleneck effect of adjacent road sections and the short-board resistance effect.

[0103] (1) Calculate the basic recovery rate Substituting the restoring stiffness equation (Equation 9) defined in this invention, let the adjustment coefficient be... Incorporate data on the operational support capability index of Tunnel A. : This value represents the performance recovery efficiency per unit time, specifically 11.3% of the performance can be restored per unit time.

[0104] (2) Calculate the network-level hindrance coefficient Tunnel B exists 2 km downstream of Tunnel A. An assessment indicates that Tunnel B's operational reliability index is low due to aging electromechanical facilities. .because It was determined that Tunnel B was the operational bottleneck of this section of road, causing obstruction to Tunnel A.

[0105] Coefficient calculation: Substitute into the hindrance model (Equation 10). Take the sensitivity. Characteristic radius .

[0106] Impedance coefficient: (3) Calculate the corrected network-level recovery rate Note: Due to the drag from downstream tunnel B, the effective recovery efficiency of tunnel A decreased by approximately 10.6%.

[0107] (iv) Dynamic Operational Resilience Measurement Index Comprehensive calculation The final index is calculated using the "risk-recovery-time" fusion formula (Equation 15) proposed in this invention (assuming a normalized assessment period). ): Step S50: Grading Determination and Result Analysis The calculated static operational resilience margin index With dynamic operational resilience metrics Substitute the values ​​into the Highway Tunnel Operational Resilience Classification Table (as shown in Table 7), and perform a dual-modal comprehensive diagnosis based on the corresponding classification criteria.

[0108] (a) Judgment Result Static assessment results: Calculated based on the supply and demand comparison formula. According to the grading standard, this value falls within the range of [35, 55), and the rating is "Vulnerable (Level 3)". This indicates that under the extreme coupled conditions of "extreme rainstorm + traffic accident", the risk requirement is high. The rapid expansion severely eroded the system's safety margin, pushing the tunnels to the brink of functional failure, and exacerbating the supply-demand imbalance.

[0109] Dynamic evaluation results: Calculated based on the system dynamics model, According to the grading standards, this value falls within the range of [80, 90), and the rating is "Good (Level 2)," indicating that despite significant risk impact, the tunnel itself possesses a solid foundation of protection capabilities (high). The high recovery rate results in a system that retains a strong resilience after being damaged, keeping the total loss within a manageable range.

[0110] (II) Bimodal Differentiation Analysis In this embodiment, the static indicators issue a "red alert" while the dynamic indicators remain "relatively robust." This significant difference reveals deeper characteristics of tunnel operation.

[0111] “Low static” reveals high-risk stress. The main reason for the low risk level is that Tunnel A accumulated two Level I risks and a large number of Level III and IV general risks (a total of 150). Furthermore, the cross-domain interaction between the "external environment" and "traffic operation" (coupling coefficient 0.8) generated additional risk energy, leading to... The index rose to 45.15. This objectively reflects that the tunnel is under "high load" of risk exposure during operation, and the static safety margin has been largely consumed.

[0112] "High dynamics" unlocks recovery potential. The higher rate is due to the better physical technical condition (civil engineering / mechanical engineering) and standard adaptability of Tunnel A. In the evolutionary model of this invention, higher... This translates into a higher recovery rate. This means that although high risk may lead to accidents (i.e., Although the disaster caused significant damage (larger area), thanks to relatively robust hardware and management, the system was able to quickly organize resources for functional recovery, thereby minimizing the total spatiotemporal loss caused by the disaster. Keep it at a low level.

[0113] (III) Operation and Maintenance Decision Recommendations Based on the above dual-modal evaluation results, the following targeted decision-making recommendations are given: Recent Strategy (Risk Reduction): Given Approaching the critical threshold, it is recommended to prioritize "risk reduction actions." The focus should be on addressing the two Level II risks, followed by the 59 Level III risks listed in Table 1 (such as insufficient road surface skid resistance and water accumulation in cable trenches). Simulations show that if Level II risks can be completely eliminated, the cumulative risk value will be significantly reduced. and cross-domain coupling increment The reduction, This will decrease significantly, thereby greatly increasing the static toughness margin.

[0114] Medium- to Long-Term Strategy (Addressing Weaknesses): Although the dynamic resilience rating is "Good," sensitivity analysis shows that the limiting factors are the weakest indicators in "maintenance effectiveness" and "network-level hindering effects." Regarding the weakest indicators, Tunnel A scored only 50 points in both "joint operation and coordination" and "resilience facility redundancy." As for network-level hindering effects, although Tunnel A itself... Up to 81.71, but downstream tunnel B This constitutes a bottleneck in the road section, resulting in a drag coefficient of approximately 10.6%. Therefore, simply upgrading the facilities of Tunnel A has already shown diminishing marginal returns. There is no need for excessive intervention in the civil engineering structure, which is still in acceptable technical condition (Technical Condition Level 2, 76.5 points). Besides the necessary improvement of the "maintenance effectiveness" of Tunnel A, next year's technical upgrade funds should be shifted from Tunnel A to downstream Tunnel B. According to model projections, if the "weakest link" of Tunnel B's guarantee capacity index can be improved... Improving the score to 70 or above will significantly reduce the network-level impedance coefficient. This will unleash the suppressed recovery potential of Tunnel A and achieve a synergistic leap in the operational resilience of the entire road section.

[0115] This application presents a static operational resilience assessment model based on supply and demand coupling. It is easy to calculate, the required parameters are readily available, and it can intuitively quantify the "safety surplus" at the current moment, making it suitable for large-scale rapid screening at the regional road network level.

[0116] This application presents a dynamic operational resilience assessment model based on system dynamics. It is suitable for refined diagnosis of key tunnels (such as extra-long tunnels and high-risk tunnels). By calculating the performance retention in the spatiotemporal dimensions through integration, it accurately quantifies the system's recovery potential. It not only focuses on "resistance" but also on "recovery after damage".

[0117] This application establishes a highly compatible and responsive cumulative risk assessment system. This application uses a risk index... The calculation employs a nonlinear cumulative model. The data source for this index system directly connects to the widely used Operational Hazard Assessment (LEC) method or the Transportation Industry Safety Production Risk Classification (LC) method, exhibiting strong compatibility and broad application scenarios. Simultaneously, the calculation formula considers both the severity (level) and quantity of risks. Through exponential weighting, it can accurately capture the devastating impact of a single extremely high risk or the superposition of multiple risk sources on the system, comprehensively reflecting the dynamic risk status of the tunnel.

[0118] This application establishes an integrated hardware and software-software-covering evaluation system for operational support capabilities. The system aligns with resilience theory's concepts of resistance (operational functionality), absorption capacity (standard adaptability), and recovery and improvement capacity (management effectiveness). Notably, it incorporates soft indicators such as emergency response timeliness and personnel certification rates, addressing the shortcomings of traditional evaluations that neglect the "soft power" of management and maintenance. Furthermore, it creatively extends the "resilience concept," previously confined to disaster prevention and mitigation, to the daily operation and maintenance phase. This evaluation system can be directly integrated with the Ministry of Transport's "National Highway Network Technical Condition Monitoring Project" (National Assessment), directly utilizing the physical technical conditions and management standardization data monitored by the National Assessment, further enhancing the accuracy and industry authority of the evaluation results.

[0119] This application constructs an evolutionary model based on the "absorption-recovery" mechanism, scientifically quantifying the time value of "management soft power." This invention utilizes nonlinear functions to measure operational support capabilities. Mapped to the system's recovery rate This innovation not only solves the problem that "soft indicators" (such as emergency response and joint operations) are difficult to evaluate on the same scale as "hard indicators" (civil engineering structures), but also intuitively demonstrates through evolution curves that improving soft power can significantly increase the recovery slope and reduce the area of ​​disaster loss. This provides strong theoretical support for maintenance units to shift from "passive road construction" to "proactive improvement of management efficiency".

[0120] This application provides scientific decision support based on quantitative data to assist in tunnel maintenance and operation. Based on the calculated operational resilience score and its sub-items, the system can assist industry authorities in accurately grasping the operational resilience of tunnels within their jurisdiction. Simultaneously, the assessment results can directly serve scientific maintenance decisions based on the current situation, guiding maintenance units to make the optimal choice between "risk reduction" and "capability improvement," achieving more rational investment and optimized resource allocation within limited maintenance funds.

[0121] In summary, this application obtains multi-source data and adjacent tunnel stationing distance data; processes the multi-source data to obtain an operational risk status index and an operational support capability index; obtains a static operational resilience margin index based on the operational risk status index and the operational support capability index; obtains a dynamic operational resilience measurement index based on the operational risk status index, the operational support capability index, and the adjacent tunnel stationing distance data; and determines the operational resilience level of the highway tunnel based on the static operational resilience margin index and the dynamic operational resilience measurement index. This achieves accurate understanding of the performance of highway tunnel operation, reduces the level of tunnel operational risk, and improves the operational support capability of highway tunnels.

[0122] For those consistent with the above, please refer to Figure 6 , Figure 6This application provides a schematic diagram of the structure of a highway tunnel operational resilience assessment device. Figure 6 As shown, the device includes: The data acquisition module 601 is used to acquire multi-source data and adjacent tunnel station spacing data. The operation index determination module 602 is used to process the multi-source data to obtain the operation risk status index and the operation support capability index; The static resilience determination module 603 is used to obtain a static operational resilience margin index based on the operational risk status index and the operational support capability index. The dynamic resilience determination module 604 is used to obtain a dynamic operational resilience measurement index based on the operational risk status index, the operational support capability index, and the adjacent tunnel stationing distance data. The toughness level determination module 605 is used to determine the operational toughness level of a highway tunnel based on the static operational toughness margin index and the dynamic operational toughness measurement index.

[0123] like Figure 7 As shown, this application embodiment also provides a terminal device 2, which includes: at least one processor 20, a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor. The processor 20 and the memory 21 are connected. When the processor 20 executes the computer program 22, it implements the steps in the robot motion planning method embodiment.

[0124] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the highway tunnel operational resilience assessment methods described in the above method embodiments.

[0125] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the highway tunnel operational resilience assessment methods described in the above method embodiments.

[0126] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable storage media cannot be electrical carrier signals or telecommunication signals.

[0127] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0128] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0129] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0130] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A method for assessing the operational resilience of highway tunnels, characterized in that, The method includes: Acquire multi-source data and adjacent tunnel station spacing data; The multi-source data is processed to obtain the operational risk status index and the operational support capability index; Based on the operational risk status index and the operational support capability index, the static operational resilience margin index is obtained; Based on the operational risk status index, the operational support capability index, and the adjacent tunnel spacing data, a dynamic operational resilience measurement index is obtained. The operational resilience level of highway tunnels is determined based on the static operational resilience margin index and the dynamic operational resilience measurement index.

2. The method for assessing the operational resilience of highway tunnels according to claim 1, characterized in that, The process of processing the multi-source data to obtain the operational risk status index and the operational support capability index includes: The multi-source data is rated according to commonly used international and domestic risk assessment methods to obtain tunnel risk data; the tunnel risk data includes the statistical quantity of each level of tunnel risk; Based on the tunnel risk data, an operational risk status index is obtained; Based on the multi-source data, operational functionality score, standard adaptability score, and maintenance effectiveness score are obtained; The operational support capability index is obtained based on the operational functionality score, the standard adaptability score, and the maintenance effectiveness score.

3. The method for assessing the operational resilience of highway tunnels according to claim 2, characterized in that, The process of obtaining the operational risk status index based on the tunnel risk data includes: The basic cumulative risk value is obtained by calculating the tunnel risk data using a linear weighting method. Based on the tunnel risk data, the regional risk energy is obtained; Based on the preset strength matrix and tunnel risk data, the cross-domain coupling increment is obtained; The operational risk status index is obtained based on the basic cumulative risk value and the cross-domain coupling increment.

4. The method for assessing the operational resilience of highway tunnels according to claim 2, characterized in that, The process of obtaining operational functionality scores, standard adaptability scores, and maintenance effectiveness scores based on the multi-source data includes: The multi-source data is rated according to a preset operational functionality evaluation method to obtain an operational functionality score; The multi-source data is rated according to a preset standard adaptability evaluation method to obtain a standard adaptability score; The multi-source data is rated according to the preset management effectiveness evaluation method to obtain a management effectiveness score.

5. The method for assessing the operational resilience of highway tunnels according to any one of claims 1 to 4, characterized in that, The dynamic operational resilience measurement index is obtained based on the operational risk status index, the operational support capability index, and the adjacent tunnel spacing data, including: The maximum performance loss depth is obtained based on the operational risk status index. The network-level performance recovery rate is obtained based on the operational support capability index and the distance data between adjacent tunnel stations. A dynamic operational resilience metric index is obtained based on the maximum performance loss depth and the network-level performance recovery rate.

6. The method for assessing the operational resilience of highway tunnels according to claim 5, characterized in that, The step of obtaining the network-level performance recovery rate based on the operational support capability index and the adjacent tunnel stationing data includes: in, For network-level performance recovery rate; The baseline recovery coefficient; This serves as the current assessment index for the tunnel's security capabilities. For the first The guarantee capacity index of adjacent tunnels; This is the resistance sensitivity coefficient; This refers to the spacing data between adjacent tunnel stations; The radius of influence is a characteristic.

7. The method for assessing the operational resilience of highway tunnels according to claim 5, characterized in that, The dynamic operational resilience metric index, derived based on the maximum performance loss depth and network-level performance recovery rate, includes: in, It serves as a dynamic operational resilience metric. The maximum performance loss depth; For network-level performance recovery rate; To assess the normalized total duration.

8. A device for assessing the operational resilience of highway tunnels, characterized in that, include: The data acquisition module is used to acquire multi-source data and distance data between adjacent tunnel stations; The operation index determination module is used to process the multi-source data to obtain the operation risk status index and the operation support capability index. The static resilience determination module is used to obtain a static operational resilience margin index based on the operational risk status index and the operational support capability index. The dynamic resilience determination module is used to obtain a dynamic operational resilience measurement index based on the operational risk status index, the operational support capability index, and the adjacent tunnel stationing distance data. The resilience level determination module is used to determine the operational resilience level of highway tunnels based on the static operational resilience margin index and the dynamic operational resilience metric index.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the highway tunnel operational resilience assessment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the highway tunnel operational resilience assessment method as described in any one of claims 1 to 7.

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