A comprehensive pipe gallery collaborative control method and system

Abstract

The application relates to the technical field of comprehensive pipe gallery system control, in particular to a comprehensive pipe gallery cooperative control method and system. The method comprises the following steps: constructing an operation execution condition list; collecting state data and constructing fusion situation information; judging whether the operation execution condition list is satisfied based on the fusion situation information; if the fusion situation information is incomplete or contradictory, giving a dynamic reliability weight to the fusion situation information, comprehensively evaluating, and obtaining an emergency operation set; constructing an emergency operation execution sequence based on the emergency operation set and according to a logical dependence relationship; executing an emergency operation in the emergency operation execution sequence, and performing feedback verification to verify whether the emergency operation achieves an expected effect; if the feedback verification result does not achieve the expected effect, stopping subsequent emergency operations, and updating the emergency operation execution sequence. The method has the advantages that multiple fault interlacing and highly uncertain information can be effectively coped with, and safe and effective cooperative control can be realized.

Description

Technical Field

[0001] This application relates to the technical field of integrated utility tunnel system control, and more specifically, to an integrated utility tunnel collaborative control method and system. Background Technology

[0002] In modern urban infrastructure, integrated utility tunnels achieve intensive management of pipeline resources by integrating multiple municipal subsystems such as electricity, communications, water supply and drainage, and gas. However, while this highly integrated feature improves operation and maintenance efficiency, it also brings significant challenges to emergency management. Especially when facing complex disaster scenarios involving multiple sources of information anomalies and interference between systems, traditional emergency response methods relying on single disaster plans are gradually revealing serious shortcomings.

[0003] Specifically, there is a high degree of coupling between the sensor systems, communication links, ventilation equipment, and smart terminals in the utility tunnel. If multiple faults occur simultaneously, such as a minor gas leak, a decline in ventilation performance, or communication link anomalies, incomplete or even contradictory environmental monitoring data can result. This makes it impossible for the central control room to accurately grasp the actual situation on-site and to make effective response decisions based on existing pre-set plans. More seriously, the response actions of multiple subsystems often exhibit temporality, dependency, and potential conflicts. For example, activating electrical equipment or sprinkler systems without confirming gas shut-off could lead to secondary disasters.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application provides a comprehensive utility tunnel collaborative control method and system, which has the advantages of effectively coping with complex disaster scenarios involving multiple intertwined faults and high information uncertainty, and achieving safe and effective collaborative control.

[0006] This application provides a method for coordinated control of integrated utility tunnels, including: Construct a list of operational execution conditions for each emergency operation. The list of operational execution conditions includes the triggering conditions, resource conditions, and safety conditions for the corresponding emergency operation in the integrated utility tunnel. Collect status data related to emergency operations, perform time synchronization, spatial correlation and data consistency processing on the status data, and construct a fusion situational information that represents the current operation status of the utility tunnel. The status data includes sensor data, control signals and event information from multiple information sources. Based on the fused situational information, it is determined whether each condition in the list of operational execution conditions for each emergency operation is met. If there are incomplete or contradictory fused situational information, dynamic reliability weights are assigned to the fused situational information according to the historical stability of the information source of the corresponding status data, the characteristics of the transmission path, and the physical characteristics of the sensor. The list of operational execution conditions is then comprehensively evaluated in conjunction with preset logical rules to obtain the set of emergency operations that can be executed at the current moment. Based on the set of emergency operations, an emergency operation execution sequence is constructed according to the preset multi-objective optimization criteria and the logical dependencies between emergency operations; Execute each emergency operation in the emergency operation sequence in sequence, and collect relevant feedback data after each emergency operation to verify whether the emergency operation has achieved the expected results. If the feedback verification results indicate that the emergency operation has not achieved the expected results, the subsequent emergency operations in the emergency operation execution sequence shall be suspended. The fused situation information shall be updated based on the feedback data and / or the relevant status data of the indirect impact caused by the emergency operation, and the operation execution condition list shall be re-evaluated to update the emergency operation execution sequence.

[0007] The above solution solves the problem of difficult safe and effective collaborative control in complex disaster scenarios with multiple intertwined faults and high information uncertainty in existing technologies. It can process incomplete and contradictory multi-source information in real time, dynamically assess risks, and generate non-preset cross-system operation sequences with temporal and logical correlation, thereby avoiding conflict handling and achieving safe and effective control of complex disasters.

[0008] To further address the problem, this application also proposes that, in the process of constructing the list of operation execution conditions, for emergency operations that rely on specific power supply resources, the corresponding resource conditions include: the operating status of the power supply resource itself, and the environmental status of environmental sensors physically adjacent to the power supply resource. In the process of determining whether each condition in the list of operational execution conditions for each emergency operation is met, if the power supply itself reports that it is operating normally, but the environmental sensor indicates that the environmental status is abnormal and affects the performance of the power supply, then the resource condition is determined not to be met.

[0009] Furthermore, this application proposes assigning dynamic reliability weights to the fused situational information based on the historical stability of the information source, transmission path characteristics, and sensor physical characteristics of the corresponding state data, including: The historical stability of the information source corresponding to the state data is evaluated. The historical stability is obtained by statistically analyzing the data continuity, fluctuation range and anomaly rate of the information source in the recent historical period. Analyze the transmission path characteristics of the information source of the corresponding status data. The transmission path characteristics include path length, node hop count, and data packet delay and loss. The sensor physical characteristics of the information source for obtaining the corresponding status data include whether there is environmental interference at the sensor's installation location, maintenance cycle, and fault history. Based on historical stability, transmission path characteristics, and sensor physical characteristics, dynamic reliability weights are assigned to various types of state data in the fused situational information.

[0010] Furthermore, this application also proposes to reassess the list of operational execution conditions to update the emergency operational execution sequence, including: Identify the propagation paths of indirect impacts triggered by implemented emergency operations within the utility tunnel; Collect status data from subsystems or sensors that are not directly related to emergency operations along the indirect impact propagation path; Based on the status data of subsystems or sensors that are not directly related to the emergency operation, analyze the secondary negative impacts associated with the emergency operation that has been performed; Integrate secondary negative impacts into the integrated situational information; Based on the integrated situational information, the list of operational execution conditions is reassessed, and the emergency operation execution sequence is updated.

[0011] Furthermore, this application also proposes collecting status data related to emergency operations, performing time synchronization, spatial correlation, and data consistency processing on the status data, and constructing fused situational information representing the current operational status of the utility tunnel, including: Time-stamped standardization processing is performed on status data from multiple information sources to achieve time synchronization of status data; Based on the spatial distribution information of each subsystem in the utility tunnel, a mapping relationship between status data and physical space is established to realize the spatial association of data. Perform consistency checks on the state data after time synchronization and spatial association to identify contradictions and anomalies in the state data. Obtain the physical deployment information, data generation mechanism and collection cycle of the information source corresponding to the status data, and analyze the causes of contradictions or anomalies. Based on the cause of the conflict and in accordance with the preset conflict rules, conflict resolution is performed to retain state data with higher credibility. Based on the status data that has completed consistency verification and conflict resolution, a fused situational information is constructed.

[0012] Furthermore, this application also proposes retaining state data with higher reliability, including: Obtain the operational stability indicators of the information sources corresponding to the multiple state data that constitute the cause of the conflict in the recent historical period. The operational stability indicators include data continuity, fluctuation range and historical anomaly rate. Obtain the data transmission path characteristics of the information source, including path topology length, node hop count, and data latency and packet loss in the recent period; Obtain the physical deployment information of the information source, and extract the environmental interference characteristics based on the environmental conditions of its location. The environmental interference characteristics include the temperature and humidity fluctuation amplitude, vibration interference frequency and electromagnetic noise level. Based on operational stability indicators, data transmission path characteristics, and environmental interference characteristics, dynamic credibility scores are assigned to the corresponding information sources of multiple state data that constitute the cause of conflict. Based on the preset credibility judgment rules, information source data with higher dynamic credibility scores are selected as the state data to be retained after conflict resolution.

[0013] Furthermore, this application proposes to construct an emergency operation execution sequence based on a set of emergency operations, according to a preset multi-objective optimization criterion and the logical dependencies between emergency operations, including: The system pre-determines multi-objective optimization criteria, which include prioritizing personnel safety, optimizing disaster control efficiency, and minimizing equipment damage as optimization objectives. Obtain the preset emergency target impact parameters corresponding to each emergency operation in the emergency operation set. The emergency target impact parameters are used to quantify the degree of impact of each emergency operation on multiple optimization objectives. Based on the current integration status information, determine the priority weights of multiple optimization objectives; Establish a logical dependency graph between emergency operations, and obtain logical dependencies from the logical dependency graph. Logical dependencies include operation order, mutual exclusion relationship and condition trigger relationship. Based on the impact parameters of emergency targets, logical dependencies, and priority weights, a comprehensive priority score for each emergency operation is calculated. Based on comprehensive priority scoring and logical dependencies, the operation order of emergency operations is set, and an emergency operation execution sequence is generated.

[0014] Furthermore, this application also proposes identifying the propagation paths of indirect impacts caused by implemented emergency operations within the utility tunnel, including: Obtain baseline fusion situational information before emergency operations are executed; After an emergency operation is performed, status data from multiple subsystems or areas that are not directly related to the emergency operation are continuously collected. By comparing the state data with the baseline fused situational information, deviations in state parameters or areas of abnormal change can be identified. Based on the correlation between the deviation of state parameters or the abnormal change area and the spatial distribution of emergency operations, the flow of energy and materials, and the physical coupling rules, it is determined whether there is an indirect impact propagation path caused by the emergency operation, and the propagation direction and path range of the indirect impact propagation path are determined.

[0015] Furthermore, this application also proposes collecting status data from subsystems or sensors that are not directly related to emergency operations along the indirect impact propagation path, including: Based on the direction and range of propagation that indirectly affect the propagation path, candidate sensors that have no direct control dependence, resource coupling or physical connection with emergency operations are screened. Access the historical operation logs and current operation status of candidate sensors, and eliminate sensors with abnormal readings, signal interruptions, or verification failures; A synchronous acquisition task is performed on the remaining candidate sensors after elimination to collect the environmental status data they monitor. The environmental status data includes temperature and humidity, smoke concentration, vibration frequency, electrical current and / or electromagnetic intensity. Integrity verification and anomaly detection are performed on environmental status data to remove incomplete or severely disturbed data, and an indirect impact status dataset is constructed for secondary negative impact analysis.

[0016] Furthermore, this application also proposes an integrated utility tunnel collaborative control system, including: The condition list construction module is used to construct the operation execution condition list for each emergency operation. The operation execution condition list includes the trigger conditions, resource conditions and safety conditions for the corresponding emergency operation in the integrated utility tunnel. The status data processing module is used to collect status data related to emergency operations, perform time synchronization, spatial correlation and data consistency processing on the status data, and construct fusion situation information that represents the current operation status of the utility tunnel. The status data includes sensor data, control signals and event information from multiple information sources. The condition determination and situation assessment module is used to determine whether each condition in the list of emergency operation execution conditions is met based on the fused situation information. If there are incomplete or contradictory fused situation information, dynamic reliability weights are assigned to the fused situation information according to the historical stability of the information source of the corresponding status data, transmission path characteristics and sensor physical characteristics. The list of operation execution conditions is then comprehensively evaluated in combination with preset logical rules to obtain the set of emergency operations that can be executed at the current moment. The execution sequence generation module is used to construct an emergency operation execution sequence based on a set of emergency operations, according to preset multi-objective optimization criteria and logical dependencies between emergency operations; The execution and feedback verification module is used to execute each emergency operation in the emergency operation sequence in sequence, and collect relevant feedback data after each emergency operation is executed to verify whether the emergency operation has achieved the expected effect. The sequence reconstruction module is used to terminate subsequent emergency operations in the emergency operation execution sequence if the feedback verification results indicate that the emergency operation has not achieved the expected results. It updates the fused situation information based on the feedback data and / or the relevant status data of the indirect impact caused by the emergency operation, and re-evaluates the operation execution condition list to update the emergency operation execution sequence.

[0017] The above scheme provides a system for implementing the above-mentioned collaborative control method. Through modular design, the method can be effectively deployed and operated, improving the feasibility of practical applications.

[0018] In summary, the integrated utility tunnel collaborative control method and system provided in this application effectively addresses complex disaster scenarios with multiple intertwined faults and high information uncertainty by constructing a list of operational execution conditions for each emergency operation, collecting and processing multi-source status data to construct fused situational information, dynamically evaluating operational execution conditions based on fused situational information and assigning dynamic reliability weights, constructing a multi-objective optimized emergency operation execution sequence, and re-evaluating and updating the sequence when feedback verification fails. This breaks through the limitations of traditional single emergency plans and achieves safe and effective collaborative control. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a collaborative control method for integrated utility tunnels provided in this application.

[0020] Figure 2 A flowchart of a collaborative control system for integrated utility tunnels provided in this application.

[0021] In the diagram: 1. Condition list construction module; 2. State data processing module; 3. Condition judgment and situation assessment module; 4. Execution sequence generation module; 5. Execution and feedback verification module; 6. Sequence reconstruction module. Detailed Implementation

[0022] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] Reference Figure 1 This application proposes a collaborative control method for integrated utility tunnels, including: S1000: Construct a list of operational execution conditions for each emergency operation. The list of operational execution conditions includes the triggering conditions, resource conditions, and safety conditions for the corresponding emergency operation in the integrated utility tunnel. S2000: Collects status data related to emergency operations, performs time synchronization, spatial correlation and data consistency processing on the status data, and constructs fusion situational information that characterizes the current operation status of the utility tunnel. The status data includes sensor data, control signals and event information from multiple information sources. S3000: Based on the fusion situational information, it determines whether each condition in the list of operational execution conditions for each emergency operation is met. If there is incomplete or contradictory fusion situational information, it assigns dynamic reliability weights to the fusion situational information according to the historical stability of the information source of the corresponding status data, the characteristics of the transmission path, and the physical characteristics of the sensor. It also comprehensively evaluates the list of operational execution conditions in combination with preset logical rules to obtain the set of emergency operations that can be executed at the current moment. S4000: Based on the set of emergency operations, an emergency operation execution sequence is constructed according to the preset multi-objective optimization criteria and the logical dependencies between emergency operations; S5000: Sequentially execute each emergency operation in the emergency operation sequence, and collect relevant feedback data after each emergency operation is executed to verify whether the emergency operation has achieved the expected effect. S6000: If the feedback verification results indicate that the emergency operation has not achieved the expected results, the subsequent emergency operations in the emergency operation execution sequence shall be suspended. The fused situation information shall be updated based on the feedback data and / or the relevant status data of the indirect impact caused by the emergency operation, and the operation execution condition list shall be re-evaluated to update the emergency operation execution sequence.

[0025] The operational execution condition list refers to the pre-set set of execution prerequisites for each emergency operation in the integrated utility tunnel, including trigger conditions, resource conditions, and safety conditions. Trigger conditions refer to the occurrence of a specific event, a change in system status, or the achievement of a warning threshold, such as excessive gas concentration, fire alarm signals, or equipment malfunction indications. Resource conditions refer to the availability of the physical or logical resources required to perform the operation, such as power supply, communication links, personnel availability, and the operating status of specific equipment (such as fire pumps and ventilation fans). Safety conditions refer to whether the risks that may arise from performing the operation are controllable or have been eliminated, such as the absence of personnel and flammable or explosive materials in the operation area, and the shutdown of related equipment. Time synchronization of status data refers to the unified calibration of timestamps from status data from different information sources to ensure consistency in the time dimension, for example, through Network Time Protocol (NTP) or high-precision clock synchronization technology. Spatial association refers to mapping status data to its physical location or subsystem within the utility tunnel, establishing a correspondence between data and the actual spatial layout, for example, by binding it through Geographic Information System (GIS) coordinates or equipment numbers.

[0026] Data consistency processing refers to verifying synchronized and associated status data, identifying and resolving contradictions, conflicts, or anomalies between data, such as through data redundancy verification, logical rule judgment, or multi-source data cross-validation. Its main purpose is to ensure the accuracy, completeness, and reliability of utility tunnel operation status information, providing a high-quality data foundation for subsequent situation assessment. Fusion situation information refers to a comprehensive set of information that fully characterizes the current operation status of the integrated utility tunnel after time synchronization, spatial association, and data consistency processing. It integrates various heterogeneous data sources such as sensor data, control signals, and event information. It can be presented in the form of a structured database, knowledge graph, or real-time dashboard. Dynamic reliability weight refers to the dynamically adjusted trust or reliability level assigned to various types of status data in the fusion situation information based on the historical stability of the information source, transmission path characteristics, and sensor physical characteristics. Historical stability can be statistically analyzed based on the data continuity, fluctuation amplitude, and anomaly rate of the information source in recent historical periods. Transmission path characteristics can include path length, node hop count, and data packet delay and loss.

[0027] Sensor physical characteristics can include whether there is environmental interference at the sensor's installation location, maintenance cycle, and fault history. This is primarily to distinguish the credibility of different data sources when information is incomplete or contradictory, reduce the negative impact of low-quality information on decision-making, and improve the accuracy of situation assessment. Multi-objective optimization criteria refer to multiple optimization objectives that need to be considered simultaneously when constructing an emergency operation execution sequence, such as prioritizing personnel safety, optimizing disaster control efficiency, and minimizing equipment damage. These objectives may conflict and require trade-offs and optimization to achieve overall optimality. Logical dependencies between emergency operations refer to the execution order, mutual exclusion, or conditional triggering relationships between different emergency operations. For example, some operations must be performed after other operations are completed (sequential dependency), some operations cannot be performed simultaneously (mutual exclusion), or the execution of some operations requires specific conditions to be met (conditional triggering). These can be defined in the form of a logic graph or rule set. Feedback verification refers to collecting relevant feedback data after each emergency operation is executed and comparing it with the expected results to confirm whether the operation has achieved its predetermined goals. Feedback data can include changes in sensor readings, equipment status indications, system response time, etc. Its main purpose is to achieve closed-loop management of emergency control, promptly detect deviations or failures in operation, and trigger subsequent adjustment mechanisms.

[0028] The core innovation of this application lies in constructing a list of operational execution conditions for each emergency operation and performing time synchronization, spatial correlation, and data consistency processing on multi-source status data to form fused situational information. Simultaneously, a dynamic reliability weighting mechanism is introduced to handle incomplete or contradictory information, thereby enabling accurate assessment of the complex and ever-changing operational status of the utility tunnel. Based on this, and combining preset multi-objective optimization criteria and the logical dependencies between emergency operations, a non-preset, time-sequential, and logically correlated cross-system emergency operation execution sequence is dynamically generated. This is supplemented by a real-time feedback verification and sequence reconstruction mechanism, achieving effective response to complex disasters and realizing safe and collaborative control.

[0029] In some embodiments, this application is implemented as follows: Assume a minor gas leak occurs within the integrated utility tunnel, accompanied by a decrease in ventilation system efficiency and damage to communication optical cables leading to unstable data transmission from some sensors. The system first constructs a list of operational execution conditions for each emergency operation, clarifying the triggering conditions, resource conditions, and safety conditions for each emergency operation. For example, initiating emergency ventilation requires meeting conditions such as gas concentration reaching a warning threshold, normal fan power supply, and safety in the exhaust outlet area; closing the gas valve requires a higher warning level, stable communication link, and no personnel around the valve. The system continuously collects data including environmental sensor data, equipment control status, and emergency event information, generating fused situational awareness information through time synchronization, spatial correlation, and data consistency verification. In cases of data anomalies or interruptions, the system assigns dynamic reliability weights based on historical stability, transmission path conditions, and equipment maintenance records to ensure the credibility of the fused information. For example, if the ventilation system reports normal operation but the surrounding temperature is abnormal, the system will mark its resource conditions as potentially unmet to avoid accidental activation. Based on the fused situational awareness, the system identifies the currently executable set of operations and prioritizes them according to a multi-objective criterion of "personnel safety first, disaster control efficiency second, and equipment damage minimization." Simultaneously, a logical dependency graph is constructed between various operations to identify timing requirements or operational conflicts, such as "evacuate first, then spray." Based on this, the system calculates a comprehensive priority score and generates an optimal execution sequence, such as: notifying personnel to evacuate → restoring local communication → initiating local ventilation → closing gas valves. After each operation is executed, the system verifies based on sensor feedback. If the actual effect does not meet expectations (e.g., gas concentration does not decrease after ventilation), subsequent operations are suspended. At the same time, the potential impact paths of the current operation on other areas are identified, relevant data is collected, the potential for secondary risks is assessed, the fusion situation is updated, and a new operation sequence is regenerated, such as upgrading to "full-coverage ventilation" or initiating "fire sprinkler system." This technical solution achieves dynamic response and safety closed-loop control across multiple systems and states, breaking through the reliance of traditional emergency plans on information integrity and single-hazard assumptions, and significantly improving the collaborative response capability of integrated utility tunnels in complex disaster scenarios.

[0030] In another embodiment of this application, the step of constructing the list of operation execution conditions includes: S1100: In the process of constructing the list of operation execution conditions, for emergency operations that rely on specific power supply resources, the corresponding resource conditions include: the operating status of the power supply resource itself, and the environmental status of environmental sensors that are physically adjacent to the power supply resource. S1200: In the process of determining whether each condition in the list of operational execution conditions for each emergency operation is met, if the power supply itself reports that it is operating normally, but the environmental sensor indicates that the environmental status is abnormal and affects the performance of the power supply, then the resource condition is determined not to be met.

[0031] Among them, environmental sensors physically adjacent to power supply resources refer to sensing devices deployed near power supply resources that can monitor the surrounding environmental parameters in real time. These can be temperature sensors, humidity sensors, smoke sensors, gas concentration sensors, or vibration sensors. The environmental status of the environmental sensors refers to the data set collected by these environmental sensors that reflects the environmental conditions around the power supply resources. Specifically, it can include the real-time values ​​or trends of parameters such as temperature, humidity, smoke concentration, harmful gas concentration, or abnormal vibration. Environmental anomalies that affect the performance of power supply resources refer to environmental parameters monitored by environmental sensors that exceed the preset safety threshold or normal fluctuation range. These anomalies are considered to have a negative impact on the stable operation or output performance of power supply resources. Specifically, they refer to excessively high temperature, excessive humidity, excessive smoke concentration, abnormal corrosive gas concentration, or continuous vibration.

[0032] The proposed solution, when constructing the list of operational execution conditions, expands the resource conditions for emergency operations that rely on specific power supply resources to include not only the operational status of the power supply resources themselves, but also the environmental status of environmental sensors physically adjacent to the power supply resources. This allows for a more comprehensive assessment of the actual availability of power supply resources, effectively avoiding secondary disasters or operational failures that may result from performing emergency operations under environmental risks, thereby improving the accuracy and effectiveness of emergency response for integrated utility tunnels.

[0033] In some preferred embodiments, this application is implemented as follows: Assume there is an emergency lighting power supply within the utility tunnel, and its operational status is reported in real-time by the power management module. Simultaneously, temperature sensors, smoke sensors, and humidity sensors are deployed in the physical vicinity of the emergency lighting power supply. When constructing the list of operational execution conditions, for emergency lighting operations requiring power from the emergency lighting power supply, the resource conditions are set as follows: the emergency lighting power supply itself is operating normally, and the environmental conditions of the temperature, smoke, and humidity sensors are all within normal ranges. When an emergency occurs in the utility tunnel, and the system needs to determine whether an emergency lighting operation can be performed, it first checks the operational status of the emergency lighting power supply. If the emergency lighting power supply reports "operating normally," the system further checks the environmental conditions of the temperature, smoke, and humidity sensors. For example, if the temperature sensor detects that the temperature around the emergency lighting power supply consistently exceeds a preset threshold of 50 degrees Celsius, even if the emergency lighting power supply itself displays normally, the system will determine that the resource conditions for the emergency lighting power supply are not met. Similarly, if the smoke sensor detects that the smoke concentration reaches the warning level, or the humidity sensor detects that the ambient humidity is far beyond the normal range, the system will also determine that the resource conditions are not met. In this way, the system can avoid attempting to start emergency lighting operations even when the emergency lighting power supply may fail due to potential malfunctions caused by environmental overheating, smoke corrosion, or humidity, thus ensuring the reliability of emergency operations.

[0034] In another embodiment of this application, a sub-step of S3000 is further proposed: a method for assigning dynamic reliability weights to the fused situational information based on the historical stability of the information source, transmission path characteristics, and sensor physical characteristics of the corresponding state data includes: S3100: Evaluate the historical stability of the information source for the corresponding state data. Historical stability is obtained by statistically analyzing the data continuity, fluctuation range, and anomaly rate of the information source in the recent historical period. S3200: Analyzes the transmission path characteristics of the information source of the corresponding status data. The transmission path characteristics include path length, node hop count, and data packet delay and loss. S3300: The sensor physical characteristics of the information source for acquiring the corresponding status data, including whether there is environmental interference at the sensor's installation location, maintenance cycle, and fault history. S3400: Based on historical stability, transmission path characteristics, and sensor physical characteristics, dynamic reliability weights are assigned to various types of state data in the fused situational information.

[0035] Among them, historical stability refers to the statistics of data continuity, fluctuation range and anomaly rate of information source in recent historical period; transmission path characteristics refer to the interference and loss that data may be subjected to during transmission, including path length, node hop count and data packet delay and loss; sensor physical characteristics refer to the errors and faults that may exist in the sensor itself, including whether there is environmental interference at the sensor installation location, maintenance cycle and fault history; dynamic reliability weight refers to assigning different weights to various types of state data in the fused situational information based on the above historical stability, transmission path characteristics and sensor physical characteristics.

[0036] The solution proposed in this application achieves refined management of the reliability of various types of status data in the fused situational information by comprehensively considering the characteristics of multiple dimensions of the information source of status data. This avoids the problem of inaccurate evaluation results due to differences in data reliability, thereby improving the accuracy of emergency operation decisions. It ensures that in complex emergency scenarios, the system can make judgments based on situational information that is close to the real situation, and then generate a safe and effective set of emergency operations and execution sequences.

[0037] In some preferred embodiments, this application is implemented as follows: To improve the sensing accuracy and emergency judgment capabilities of integrated utility tunnels in complex operating environments, the reliability of status data sources can be dynamically assessed through multi-dimensional analysis. Specifically, this involves continuously collecting historical operating data from various information sources (such as temperature and gas concentration sensors, ventilation controllers, etc.) within a certain time range, and statistically analyzing their reporting rate, fluctuation amplitude, and anomaly frequency to quantify their historical stability. Sensors with higher continuity, smaller fluctuations, and fewer anomalies receive higher historical stability scores. Simultaneously, the monitoring mechanism of the communication network allows for real-time monitoring of the sensor data transmission path. By analyzing indicators such as packet delay, packet loss rate, and path hop count, the stability of the transmission process can be quantified. Shorter paths, lower delays, and lower packet loss rates indicate higher transmission path reliability. At the physical level, by combining the sensor's installation environment, maintenance records, and historical fault information, the degree of interference and operational health can be determined. For example, if the installation location is near high temperatures or strong electromagnetic interference sources, or if the equipment has recently experienced calibration failures or reading drift, its physical reliability should be adjusted accordingly. Based on the scoring results from the three aspects mentioned above, the system assigns dynamic reliability weights to the status data from each information source. These weights are obtained through weighted calculations, and the weighting coefficients can be set based on experience or expert knowledge. When a data source experiences fluctuations, the system will comprehensively evaluate its historical performance, current network status, and device health to determine the reliability of its data in the current situation. Ultimately, all status data will be weighted according to their respective dynamic weights during the fusion process, prioritizing reliable information and mitigating the impact of uncertainties. This enhances the accuracy and robustness of the fused situational information, providing a more robust foundation for subsequent emergency response and control strategies.

[0038] In another embodiment of this application, it is further proposed that re-evaluating the list of operational execution conditions to update the emergency operational execution sequence includes: S6100: Identify the propagation path of indirect impacts caused by performed emergency operations within the utility tunnel; S6200: Collects status data from subsystems or sensors that are not directly related to emergency operations along the path of indirect impact propagation; S6300: Analyze the secondary negative impacts related to the emergency operations performed, based on the status data of subsystems or sensors that are not directly related to the emergency operations. S6400: Integrates secondary negative impacts into the fused situational information; S6500: Based on the integrated situational information, reassess the list of operational execution conditions and update the emergency operation execution sequence.

[0039] Indirect impact propagation paths refer to the paths through which executed emergency operations affect subsystems or areas outside the direct target of the utility tunnel through physical coupling, energy and material flow, or logical connections. These paths can be determined using internal structural diagrams of the utility tunnel, system interconnection topology diagrams, and historical event correlation analysis. Subsystems or sensors not directly related to the emergency operation refer to equipment or monitoring units within the utility tunnel that are outside the direct target or direct impact range of the emergency operation but may be indirectly affected by it. Specifically, this refers to subsystems or sensors that do not have a direct control command relationship, resource sharing relationship, or physical connection relationship with the emergency operation. For example, when the emergency operation is to close a gas valve, the subsystem not directly related to this operation could be a power system or a communication system, and the sensor could be a sensor monitoring the temperature of power cables or the signal strength of communication optical cables. Secondary negative impacts refer to phenomena or states indirectly caused by executed emergency operations that adversely affect the operational safety of the utility tunnel or the condition of equipment. Specifically, this refers to the chain reaction caused by an emergency operation on the utility tunnel environment or related systems, leading to new faults, exacerbation of existing problems, or a decline in system performance. For example, closing a gas valve may result in insufficient local ventilation, which in turn causes temperature increases or oxygen concentration decreases in other areas. Fusion situational information refers to a comprehensive and highly reliable set of information characterizing the current operational status of the utility tunnel, constructed by synchronizing, spatially correlated, and ensuring data consistency of status data from multiple information sources. The operation execution condition list refers to a pre-set set of conditions for each emergency operation in the integrated utility tunnel, used to determine whether the operation can be safely and effectively executed.

[0040] The proposed solution employs a sophisticated process that, upon failure of emergency operation feedback verification, goes beyond simply halting subsequent operations. Instead, it delves into the potential indirect impacts of the executed operations, thereby comprehensively updating the utility tunnel's status information and replanning emergency strategies. In this way, even if the initial emergency operation fails to achieve its full potential, the system can promptly detect and address any chain reactions it may trigger, ensuring the overall safety and stable operation of the utility tunnel and avoiding global risks caused by localized operational failures.

[0041] In another embodiment of this application, S4000 is further proposed to include: S4100: Preset multi-objective optimization criteria, which include prioritizing personnel safety, optimizing disaster control efficiency, and minimizing equipment damage as optimization objectives; S4200: Obtain the preset emergency target impact parameters corresponding to each emergency operation in the emergency operation set. The emergency target impact parameters are used to quantify the degree of impact of each emergency operation on multiple optimization targets. S4300: Based on the current fusion situation information, determine the priority weights of multiple optimization objectives; S4400: Establish a logical dependency graph between emergency operations, and obtain logical dependencies from the logical dependency graph. Logical dependencies include operation order, mutual exclusion relationship and condition triggering relationship. S4500: Calculate the comprehensive priority score of each emergency operation based on the emergency target impact parameters, logical dependencies, and priority weights; S4600: Based on comprehensive priority scoring and logical dependencies, set the operation order of emergency operations and generate an emergency operation execution sequence.

[0042] Among them, multi-objective optimization criteria refer to multiple objectives that need to be considered and optimized simultaneously during emergency response. These objectives can be defined and quantified using methods such as weight allocation, analytic hierarchy process (AHP), or fuzzy comprehensive evaluation. Emergency objective impact parameters refer to numerical values ​​or indicators that quantify the degree of impact of various emergency operations on multiple preset optimization objectives (such as personnel safety, disaster control efficiency, and equipment damage minimization). These parameters can be obtained using methods such as expert scoring, historical data analysis, or simulation. Logical dependency graphs are graphical representations constructed with emergency operations as nodes and logical dependencies between operations as edges. These can be established using directed graphs, state machine models, or Petri nets. Logical dependencies refer to the relationships between emergency operations. Constraints include execution order, mutual exclusion, and triggering conditions. Operation order refers to the specific sequence in which certain emergency operations must be executed, which can be represented by predecessor-successor relationships or task chains. Mutual exclusion means that certain emergency operations cannot be executed simultaneously, which can be represented by mutex locks or resource occupancy constraints. Triggering conditions mean that the execution of certain emergency operations requires specific preconditions, which can be represented by event-condition-action rules or precondition checks. Comprehensive priority scoring is a quantitative evaluation of the value of each emergency operation, considering its impact on multiple optimization objectives, logical dependencies, and priority weights. It can be calculated using methods such as weighted summation, multi-attribute decision models, or utility functions.

[0043] This solution constructs a rational and effective emergency operation sequence in integrated utility tunnel emergency scenarios through a series of steps. First, it pre-defines multi-objective optimization criteria, clearly defining personnel safety as the priority, disaster control efficiency as the optimal level, and equipment damage as the core objectives. This combination ensures that emergency response is not merely a simple accumulation of operations, but rather a more targeted and effective collaborative control plan based on a deep understanding of complex situations and multi-dimensional considerations. This improves the rationality and effectiveness of emergency response, solving the problem that simply relying on a set of emergency operations cannot guarantee the rationality and effectiveness of the generated emergency operation sequence.

[0044] In some preferred embodiments, this application is implemented as follows: In an emergency scenario simulating a gas leak in a utility tunnel accompanied by localized communication disruption, the system employs a multi-objective optimization mechanism to dynamically prioritize and sequence emergency operations. The system pre-defines three core emergency objectives: prioritizing personnel safety, optimizing disaster control efficiency, and minimizing equipment damage. It also assigns impact parameters to each emergency operation corresponding to these objectives. For example, "closing the gas valve" has a strong effect on disaster control and provides some protection for personnel safety, but may have a slight negative impact on equipment; "activating the ventilation system" balances disaster control and personnel safety, but may also pose risks to equipment. Based on current situational awareness, if personnel are detected as trapped, the system dynamically increases the weight of the "personnel safety" objective and decreases the weight of other objectives; conversely, it treats them with relatively balanced weights. The system also constructs a logical dependency graph between emergency operations, clarifying the order and mutually exclusive conditions of operations. For instance, "closing the gas valve" is a prerequisite for "activating ventilation" and "spraying inert gas," but ventilation and spraying cannot be performed simultaneously; "sending personnel evacuation instructions" depends on the availability of the "personnel positioning system." By combining the target impact parameters, logical constraints, and current weight configurations of each operation, the system calculates a comprehensive priority score for each operation and determines the optimal execution order accordingly. For example, when personnel safety has a high priority, "sending personnel evacuation instructions" will have a higher priority than secondary operations such as "turning off non-critical lighting." The final generated execution sequence, such as "personnel evacuation" → "closing gas valves" → "starting ventilation" → "monitoring gas concentration," ensures that disaster control is carried out in an orderly manner while ensuring personnel safety and avoiding operational conflicts. This solution, through multi-objective optimization and logical constraint modeling, achieves flexible and reliable emergency operation coordination, significantly improving the emergency response effect in complex disaster scenarios.

[0045] In another embodiment of this application, S2000 further includes: S2100: Performs timestamp standardization processing on status data from multiple information sources to achieve time synchronization of status data; S2200: Based on the spatial distribution information of each subsystem in the utility tunnel, establish a mapping relationship between status data and physical space to realize spatial association of data; S2300: Performs consistency verification on the state data after time synchronization and spatial association, and identifies contradictions between state data and anomalies in the state data; S2400: Obtain the physical deployment information, data generation mechanism and collection cycle of the information source corresponding to the status data, and analyze the causes of conflicts or anomalies. S2500: Based on the cause of the conflict and in conjunction with the preset conflict rules, perform conflict resolution processing and retain state data with higher credibility; S2600: Construct fused situational information based on status data that has completed consistency verification and conflict resolution processing.

[0046] Timestamp standardization refers to unifying and calibrating the timestamps of status data from different information sources to eliminate time inconsistencies caused by clock deviations or different time standards. This can be achieved using Network Time Protocol (NTP), Precision Time Protocol (PTP), or other internal clock synchronization mechanisms. Spatial distribution information refers to data describing the specific locations, geometries, and topological relationships of various equipment, sensors, subsystems, and key areas within the integrated utility tunnel in physical space. This can be represented using Geographic Information System (GIS) data, Building Information Modeling (BIM), or a custom spatial coordinate system. Consistency verification refers to logical and numerical checks on the status data after time synchronization and spatial association to identify contradictions, outliers, or non-compliance with preset rules. This can be achieved using methods such as range checks, threshold alarms, multi-sensor cross-validation, or anomaly detection based on historical patterns. Physical deployment information, data generation mechanisms, and acquisition cycles refer to the actual installation locations of sensors or equipment within the utility tunnel, the method of data generation (e.g., continuous measurement or event-triggered), and the frequency or interval of data acquisition. This information can be obtained from equipment ledgers, sensor technical specifications, or system configuration documents. Conflict rules refer to a predefined set of logical judgments or priority strategies used to guide the handling and selection when contradictory or anomaly-like state data is identified. These rules can be based on the reliability level of the data source, data freshness, data accuracy, or expert experience. Conflict resolution processing refers to correcting, selecting, or integrating contradictory or anomaly-like state data according to the identified conflict causes and predefined conflict rules. This aims to eliminate data inconsistencies and generate a more reliable single data representation. Methods such as weighted averaging, majority voting, priority selection, outlier removal, or model-based prediction can be used to achieve this. Higher reliability refers to determining and selecting the higher-quality, more trustworthy data when multiple contradictory state data exist by comprehensively evaluating factors such as the stability of the data source, the reliability of data transmission, and interference in the sensor's environment.

[0047] The proposed solution, through a series of meticulous data processing steps, ensures the accuracy, consistency, and reliability of the constructed integrated situational information. This allows for a more accurate assessment of the actual operational status of the utility tunnel, avoiding misjudgments or incorrect decisions caused by inaccurate or contradictory data, thereby ensuring the timeliness and safety of emergency operations.

[0048] In some preferred embodiments, this application is implemented as follows: In the operation and management of integrated utility tunnels, high-quality processing of status data from multiple information sources is necessary to construct accurate and reliable integrated situational information. First, the system achieves data time synchronization through timestamp standardization. All sensors, controllers, data acquisition units, and the central server periodically synchronize with a high-precision Network Time Protocol (NTP) server. Each device carries a unified reference timestamp when reporting data, ensuring precise alignment of data from different information sources in the time dimension. Next, the system achieves spatial association of data based on BIM (Building Information Modeling) or GIS (Geographic Information System) data. Through the unique identifier of each sensor, the system can locate its physical deployment position in the 3D model, thereby binding the collected data to a specific spatial location. This process gives environmental parameters, equipment status, and other information spatial context, facilitating visualization and regional linkage analysis. After completing time and spatial annotation, the system performs consistency verification on the status data. For example, if a reasonable physical range for environmental parameters is set (e.g., temperature between 0℃ and 60℃), readings outside this range are considered abnormal. Simultaneously, if there are significant deviations between adjacent sensor data (e.g., a temperature difference exceeding 5℃), they are considered contradictory. For equipment status data (such as fan operating status), the system also judges whether there are any abnormalities based on its operating logic, such as a fan indicating it is off but the current value is still too high. Once data anomalies or conflicts are detected, the system further analyzes the causes, including checking the sensor deployment environment (such as poor ventilation in dead corners), data acquisition cycle, data generation mechanism, and historical maintenance records. For example, if a gas sensor shows an abnormally high reading, the system can determine whether it is caused by local accumulation or equipment aging by checking the location and maintenance information. Subsequently, the system processes inconsistent data according to preset conflict resolution rules. If two gas sensor readings contradict each other, and one of the devices has recently been frequently triggering fault alarms or is deployed in a highly interfering environment, the system tends to retain the data from the more stable device. If multiple data sources are all reliable but have slight deviations, the system can use a weighted average, with the weight determined by the stability and accuracy of the data sources. Finally, the system integrates all high-quality status data after time alignment, spatial positioning, consistency verification, and conflict resolution to generate fused situational information. This information comprehensively reflects the environmental status, equipment operation status, personnel distribution, and video anomalies in various areas of the utility tunnel, and is presented in a unified visual format, providing accurate and real-time global support for monitoring and emergency decision-making.

[0049] In another embodiment of this application, a sub-step of S2500 is further proposed: the step of retaining state data with higher reliability includes: S2510: Obtain the operational stability indicators of the information sources corresponding to the multiple state data that constitute the cause of the conflict in the recent historical period. The operational stability indicators include data continuity, fluctuation range and historical anomaly rate. S2520: Obtain the data transmission path characteristics of the information source, including path topology length, node hop count, and data latency and packet loss in the recent period; S2530: Obtain the physical deployment information of the information source and extract environmental interference characteristics based on the environmental conditions of its location. Environmental interference characteristics include temperature and humidity fluctuation amplitude, vibration interference frequency and electromagnetic noise level. S2540: Based on operational stability indicators, data transmission path characteristics, and environmental interference characteristics, dynamic credibility scores are assigned to the corresponding information sources of multiple state data that constitute the cause of conflict. S2550: Based on the preset credibility judgment rules, select information source data with higher dynamic credibility scores as the state data to be retained after conflict resolution.

[0050] Among them, operational stability indicators refer to the operational performance of the information source over a period of time, specifically a comprehensive reflection of data continuity, fluctuation range, and historical anomaly rate; data transmission path characteristics refer to the attributes of the physical and logical paths that data undergoes during transmission from the information source to the processing system, specifically the path topology length, node hop count, and data latency and packet loss in the recent period; environmental interference characteristics refer to the interference that environmental conditions in the physical deployment area of ​​the information source may cause to data acquisition, specifically the amplitude of temperature and humidity fluctuations, vibration interference frequency, and electromagnetic noise level; dynamic credibility score refers to a real-time changing quantitative evaluation value assigned to the information source data based on the operational stability indicators, data transmission path characteristics, and environmental interference characteristics; credibility judgment rules refer to the preset logic or algorithm used to guide how to select and retain state data based on the dynamic credibility score, which can adopt methods such as threshold comparison, weighted averaging, or machine learning models.

[0051] In some preferred embodiments, when the central control system of the integrated utility tunnel performs consistency verification on status data from different information sources, and finds that temperature sensor A and temperature sensor B in a certain area are simultaneously reporting contradictory temperature readings (e.g., sensor A reports 25 degrees Celsius, while sensor B reports 35 degrees Celsius), the system will initiate a conflict resolution process. First, the system will query the operating logs of temperature sensors A and B over the past week to obtain their operational stability indicators. For example, sensor A's data continuity remains at 99%, with small fluctuations and a historical anomaly rate below 0.1%; while sensor B's data continuity may only be 90%, with larger fluctuations and a historical anomaly rate reaching 1%. Next, the system will analyze the data transmission paths of the two sensors. Sensor A's data may be transmitted via a fiber optic link, with a shorter path topology, fewer hops, and recent data latency and packet loss within normal ranges; while sensor B's data may be transmitted via a wireless network, with a relatively longer path topology, more hops, and intermittent data latency and minor packet loss in recent periods. Subsequently, the system will obtain the physical deployment information of the two sensors. Sensor A might be deployed in a relatively stable area inside the utility tunnel, exhibiting low temperature and humidity fluctuations, low vibration frequency, and electromagnetic noise levels within a safe range. Sensor B, on the other hand, might be deployed near a ventilation opening, with larger temperature and humidity fluctuations and the presence of a high-power motor nearby, resulting in higher vibration frequency and electromagnetic noise levels. Based on these operational stability indicators, data transmission path characteristics, and environmental interference characteristics, the system assigns dynamic reliability scores to both Sensor A and Sensor B using a pre-defined evaluation model, such as a weighted scoring algorithm. For example, Sensor A might receive a score of 0.92, while Sensor B receives a score of 0.75. Finally, the system combines this with pre-defined reliability judgment rules, which might specify selecting data with scores higher than 0.85 or directly selecting the highest-scoring data. In this example, the system would select the data from Sensor A (25 degrees Celsius) as the retained state data after conflict resolution and incorporate it into the fused situational information, thereby ensuring the accuracy of the utility tunnel situational information.

[0052] Through the above technical solutions, in the integrated utility tunnel environment, it is possible to more accurately judge and select more reliable status data, effectively avoiding the distortion of fused situational information due to incorrect data selection.

[0053] In another embodiment of this application, S6100 further includes: S6110: Obtain baseline fusion situational information prior to emergency operations; S6120: After an emergency operation is performed, continuously collect status data from multiple subsystems or areas that are not directly related to the emergency operation. S6130: Compare the state data with the baseline fused situation information to identify deviations in state parameters or areas of abnormal change; S6140: Based on the correlation between the deviation of state parameters or abnormal change areas and the spatial distribution of emergency operations, the flow of energy and materials, and the physical coupling rules, determine whether there is an indirect impact propagation path caused by emergency operations, and determine the propagation direction and path range of the indirect impact propagation path.

[0054] Among them, baseline fusion situational information refers to comprehensive information representing the overall operational status of the utility tunnel, constructed by processing multi-source heterogeneous data within the tunnel through time synchronization, spatial correlation, and data consistency before the emergency operation is executed. This information can be implemented using historical data snapshots, real-time data aggregation, or multimodal data fusion models. Status data of multiple subsystems or areas not directly related to the emergency operation refers to operational data generated by subsystems or areas that have no direct causal relationship or direct interaction with the currently executed emergency operation physically, logically, or functionally. This data can be continuously collected using independent sensor networks, cross-system data interfaces, or regional monitoring units. Deviations in status parameters or abnormally changing areas refer to physical or logical areas of the utility tunnel that exceed preset threshold values ​​or exhibit abnormal pattern changes, discovered by comparing real-time status data after the operation with baseline fusion situational information. This data can be implemented using statistical methods, machine learning anomaly detection algorithms, or rule-based threshold judgment. Spatial distribution refers to the physical spatial relationships and layout of various equipment, subsystems, and environmental monitoring points within the utility tunnel. This distribution can be achieved using 3D modeling, geographic information systems (GIS), etc. GIS data or utility tunnel topology maps are used to represent the flow of energy and matter within the utility tunnel, including electricity, gas, water, and ventilation airflow. This can be described using pipeline diagrams, fluid dynamics models, or power network topology maps. Physical coupling rules refer to the physical connections, interactions, or dependencies between different subsystems or equipment within the utility tunnel. Examples include the close proximity of gas pipelines and communication fiber optic cables, and the linkage between ventilation systems and environmental sensors. These can be defined using system interaction models, causal relationship graphs, or expert knowledge bases. Indirect influence propagation paths refer to the path of influence caused by existing... The specific propagation chain or diffusion path caused by the emergency operation, which leads to deviations in state parameters or abnormal changes in the region through non-directly related subsystems or regions via spatial distribution, energy and material flow, or physical coupling rules, can be identified using graph theory algorithms, path search algorithms, or causal chain analysis based on rule-based reasoning. The propagation direction and path range refer to the specific direction in which the indirect impact spreads outward from the source of the operation, as well as the boundaries of the physical or logical regions affected. These can be determined using impact diffusion models, region division algorithms, or propagation pattern analysis based on historical data.

[0055] The proposed solution systematically identifies the indirect impact propagation paths triggered by emergency operations within the utility tunnel through a series of logically rigorous steps. This overcomes the shortcomings of traditional methods in terms of global status perception and impact tracking, enabling emergency decision-making to be based on more comprehensive and accurate information, thereby improving the precision and response efficiency of utility tunnel emergency management.

[0056] In some preferred embodiments, this application is implemented as follows: In a utility tunnel, when a minor gas leak triggers a "shut down the main gas valve" operation, the system not only executes the main control response but also needs to identify the propagation paths of potential indirect impacts. To this end, the system first retrieves baseline fusion status information prior to the operation, which includes parameter data for each subsystem (such as power, communication, ventilation, and fire protection) under normal conditions, such as temperature, humidity, gas concentration, current, voltage, and network latency. After the operation, the system continuously collects status data from multiple subsystems not directly related to the gas system, such as wind speed and pressure in the ventilation system, packet loss rate and latency in the communication system, current and voltage changes in the power system, and environmental data from non-gas pipeline areas. Subsequently, the system compares this data with the baseline status to identify potential abnormal deviations, such as decreased ventilation efficiency or abnormal communication latency. After identifying areas of abnormal change, the system analyzes whether these anomalies are related to the "shut down the gas valve" operation, considering the tunnel's structural layout, equipment physical locations, and the coupling logic between subsystems. If there is airflow connectivity between the abnormal area of ​​the ventilation system and the valve operation area, and the physical mechanism of gas leakage affecting ventilation efficiency is consistent with this, then an indirect impact path from gas operation to ventilation abnormality is identified. Similarly, if the area with increased communication system latency is close to a gas pipeline, and there is a physical coupling mechanism of gas corroding optical cables, a propagation path from gas operation to communication performance degradation can also be identified. Finally, the system will determine the propagation direction and range of these paths, such as diffusion from the leak point to the vent, or affecting multiple subsystems in a specific area, thereby providing more precise support for subsequent intervention.

[0057] In another embodiment of this application, S6200 further includes: S6210: Based on the direction and range of propagation that indirectly affect the propagation path, candidate sensors that have no direct control dependence, resource coupling or physical connection with emergency operations are screened. S6220: Retrieve the historical operation logs and current operation status of candidate sensors, and eliminate sensors with abnormal readings, signal interruptions, or verification failures; S6230: Perform synchronous acquisition tasks on the remaining candidate sensors after elimination, and collect the environmental status data they monitor. The environmental status data includes temperature and humidity, smoke concentration, vibration frequency, electrical current and / or electromagnetic intensity. S6240: Perform integrity verification and anomaly detection on environmental status data, remove incomplete or severely disturbed data, and construct an indirect impact status dataset for secondary negative impact analysis.

[0058] The propagation direction and range of indirect impact propagation paths refer to the specific direction and spatial area covered by the spread of unintended effects caused by emergency operations within the utility tunnel. This can be achieved using utility tunnel topology, energy and material flow models, or historical event data analysis. Candidate sensors without direct control dependence, resource coupling, or physical connection to emergency operations refer to sensors whose operating status or data acquisition is not directly controlled by the current emergency operation, do not share critical resources directly used by the emergency operation, or have no direct physical contact. This can be achieved using system topology analysis, resource allocation table lookup, or physical connection map comparison. Historical operating logs and current operating status refer to the operating data, event records, and real-time operating status information of sensors over a past period. This can be achieved using database queries, API interface calls, or real-time data stream monitoring. Abnormal readings, signal interruptions, or verification failures refer to sensor output data values ​​exceeding the preset normal range, interruptions in the communication link between the sensor and the data acquisition system, or failure to pass preset integrity checks during data transmission. This can be achieved using threshold comparison, heartbeat packet detection, or CRC verification. Synchronous data acquisition refers to the operation of acquiring data from multiple sensors at the same time point or within a very short time window. This can be achieved using a unified clock synchronization mechanism, a distributed acquisition scheduling system, or timestamp alignment technology. Environmental status data refers to various monitoring data reflecting the physical environmental conditions inside the utility tunnel. This data can be acquired using temperature and humidity sensors, smoke sensors, vibration sensors, current sensors, and / or electromagnetic field sensors. Integrity verification and anomaly detection refer to checking the acquired data to ensure its integrity (no missing data, no damage) and accuracy (no outliers, no interference). This can be achieved using data packet length checks, checksum calculations, statistical outlier detection algorithms, or machine learning anomaly detection models. Incomplete or severely interfered data refers to data with missing data packets, corrupted data fields, data values ​​that significantly deviate from the normal range, or data that has lost its validity due to strong external noise sources. This can be achieved using data missing rate threshold judgment, data fluctuation amplitude analysis, or specific noise pattern recognition. Indirect impact state datasets refer to high-quality, reliable sensor data sets used to analyze the secondary negative impacts of emergency operations after screening, elimination, and verification. These datasets can be stored and managed using structured databases, time-series databases, or data lakes.

[0059] The solution proposed in this application addresses the problem of how to efficiently and accurately obtain high-quality indirect impact status data after emergency operation feedback verification failure through a systematic data acquisition and processing process.

[0060] In some preferred embodiments, the specific implementation is as follows: In emergency operations within integrated utility tunnels, if an operation (such as shutting down the ventilation system) fails and an indirect impact propagation path is identified (e.g., ventilation shutdown causing temperature increases in adjacent power cable areas), the system will further collect and process status data along that path. First, based on the propagation direction and path range, the system uses a digital twin model or topology database to filter out candidate sensors that have no direct control over the ventilation system and no physical contact with it for communication or power supply, such as temperature sensors, current sensors, and smoke detectors in the cable area. Then, the system checks the historical operating logs and current status of these sensors. If a sensor exhibits frequent offline behavior, data jumps, or self-test failures, it is removed to ensure the reliability of subsequent data collection. For the remaining valid sensors, the system performs synchronous data acquisition, acquiring environmental monitoring data at uniform time intervals (e.g., every 10 seconds), including refined values ​​such as temperature, smoke concentration, current, and electromagnetic intensity. After acquisition, the system performs integrity checks and anomaly detection on this data, such as checking data length and checksums, or using the Z-score algorithm to identify temperature deviations and filter out severely interfered or abnormal readings. Ultimately, the system integrates the retained data into a set of indirect impact state datasets. This dataset reflects the secondary impact state of emergency operations on areas not directly controlled, and can be stored in a time-series database for subsequent risk identification and response strategy optimization.

[0061] Reference Figure 2 In another embodiment of this application, a comprehensive utility tunnel collaborative control system is further proposed, comprising: Condition list construction module 1 is used to construct the operation execution condition list for each emergency operation. The operation execution condition list includes the triggering conditions, resource conditions and safety conditions for the corresponding emergency operation in the integrated utility tunnel. Status data processing module 2 is used to collect status data related to emergency operations, perform time synchronization, spatial correlation and data consistency processing on the status data, and construct fusion situation information that represents the current operation status of the utility tunnel. The status data includes sensor data, control signals and event information from multiple information sources. The condition determination and situation assessment module 3 is used to determine whether each condition in the list of emergency operation execution conditions is met based on the fused situation information. If there is incomplete or contradictory fused situation information, dynamic reliability weights are assigned to the fused situation information according to the historical stability of the information source of the corresponding status data, transmission path characteristics and sensor physical characteristics. The list of operation execution conditions is then comprehensively evaluated in combination with preset logical rules to obtain the set of emergency operations that can be executed at the current moment. The execution sequence generation module 4 is used to construct an emergency operation execution sequence based on the emergency operation set, according to the preset multi-objective optimization criteria and the logical dependencies between emergency operations; The execution and feedback verification module 5 is used to execute each emergency operation in the emergency operation execution sequence in sequence, and collect relevant feedback data after each emergency operation is executed to verify whether the emergency operation has achieved the expected effect. The sequence reconstruction module 6 is used to terminate subsequent emergency operations in the emergency operation execution sequence if the feedback verification results indicate that the emergency operation has not achieved the expected results. It updates the fused situation information based on the feedback data and / or the relevant status data of the indirect impact caused by the emergency operation, and re-evaluates the operation execution condition list to update the emergency operation execution sequence.

[0062] Specifically, the condition list construction module 1 can use a rule engine-based configuration interface or a structured database to store and retrieve these conditions; the state data processing module 2 can use data acquisition agents, message queues, and data preprocessing algorithms to achieve data synchronization, correlation, and consistency processing; the condition judgment and situation assessment module 3 can use expert systems, fuzzy logic reasoning, or machine learning models to process uncertain information and perform comprehensive evaluation; the execution sequence generation module 4 can use graph theory algorithms, heuristic search, or optimization planners to consider multi-objective optimization and dependencies between operations; the execution and feedback verification module 5 uses automated executors, remote control interfaces, and real-time data analyzers to ensure accurate execution of operations and effect verification; and the sequence reconstruction module 6 can use event-driven mechanisms, state machines, or decision tree models to trigger re-evaluation and sequence updates.

[0063] The solution proposed in this application implements the complex integrated utility tunnel collaborative control method at the physical level through a modular system architecture, thereby solving the problem that the method is difficult to deploy and implement in practical applications.

[0064] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for coordinated control of integrated utility tunnels, characterized in that, include: Construct a list of operational execution conditions for each emergency operation, which includes the triggering conditions, resource conditions, and safety conditions for the corresponding emergency operation in the integrated utility tunnel; Collect status data related to emergency operations, perform time synchronization, spatial correlation and data consistency processing on the status data, and construct fusion situation information that characterizes the current operation status of the utility tunnel. The status data includes sensor data, control signals and event information from multiple information sources. Based on the fused situational information, it is determined whether each condition in the list of execution conditions for each emergency operation is met. If the fused situational information is incomplete or contradictory, dynamic reliability weights are assigned to the fused situational information according to the historical stability of the information source of the corresponding status data, transmission path characteristics, and sensor physical characteristics. The list of execution conditions is then comprehensively evaluated in conjunction with preset logical rules to obtain a set of emergency operations that can be executed at the current moment. Based on the set of emergency operations, an emergency operation execution sequence is constructed according to the preset multi-objective optimization criteria and the logical dependencies between emergency operations; Each emergency operation in the emergency operation sequence is executed sequentially, and relevant feedback data is collected after each emergency operation is executed for feedback verification to verify whether the emergency operation has achieved the expected effect. If the feedback verification result indicates that the emergency operation has not achieved the expected effect, the subsequent emergency operations in the emergency operation execution sequence shall be terminated, the fused situation information shall be updated based on the feedback data and / or the relevant status data of the indirect impact caused by the emergency operation, and the operation execution condition list shall be re-evaluated to update the emergency operation execution sequence.

2. The integrated utility tunnel collaborative control method according to claim 1, characterized in that, In the process of constructing the list of operation execution conditions, for emergency operations that rely on specific power supply resources, the corresponding resource conditions include: the operating status of the power supply resource itself, and the environmental status of environmental sensors that are physically adjacent to the power supply resource. In the process of determining whether each condition in the list of operation execution conditions for each emergency operation is met, if the power supply resource itself reports that it is operating normally, but the environmental status of the environmental sensor indicates an environmental anomaly affecting the performance of the power supply resource, then it is determined that the resource condition is not met.

3. The integrated utility tunnel collaborative control method according to claim 1, characterized in that, Based on the historical stability of the information source, transmission path characteristics, and sensor physical characteristics of the corresponding state data, dynamic reliability weights are assigned to the fused situational information, including: The historical stability of the information source corresponding to the state data is evaluated, and the historical stability is obtained by statistically analyzing the data continuity, fluctuation range and anomaly rate of the information source in the recent historical period. Analyze the transmission path characteristics of the information source corresponding to the state data, including path length, node hop count, and data packet delay and loss. The sensor physical characteristics of the information source corresponding to the state data are obtained, including whether there is environmental interference at the sensor's installation location, maintenance cycle, and fault history. Based on the historical stability, transmission path characteristics, and sensor physical characteristics, dynamic reliability weights are assigned to various types of state data in the fused situational information.

4. The integrated utility tunnel collaborative control method according to claim 1, characterized in that, The fused situational information is updated based on feedback data and / or indirect impact-related status data caused by the emergency operation, and the operation execution condition list is reassessed to update the emergency operation execution sequence, including: Identify the propagation paths of indirect impacts triggered by implemented emergency operations within the utility tunnel; Collect status data from subsystems or sensors that are not directly related to the emergency operation along the indirect impact propagation path; Based on the status data of subsystems or sensors that are not directly related to the emergency operation, analyze the secondary negative impacts associated with the emergency operation that has been performed; The secondary negative impacts are integrated into the fused situational information; Based on the integrated situational information, the list of operational execution conditions is reassessed, and the emergency operation execution sequence is updated.

5. The integrated utility tunnel collaborative control method according to claim 1, characterized in that, Collect status data related to emergency operations, perform time synchronization, spatial correlation, and data consistency processing on the status data, and construct fused situational information representing the current operational status of the utility tunnel, including: Time-stamped standardization processing is performed on status data from multiple information sources to achieve time synchronization of the status data; Based on the spatial distribution information of each subsystem in the utility tunnel, a mapping relationship between the status data and the physical space is established to realize the spatial association of the data; Perform consistency verification on the state data after time synchronization and spatial association to identify contradictions between the state data and anomalies in the state data. Obtain the physical deployment information, data generation mechanism, and collection cycle of the information source corresponding to the status data, and analyze the causes of contradictions or anomalies. Based on the cause of the conflict and in conjunction with the preset conflict rules, conflict resolution processing is performed to retain the state data with higher credibility. Based on the state data that has completed consistency verification and conflict resolution processing, a fused situational information is constructed.

6. The integrated utility tunnel collaborative control method according to claim 5, characterized in that, Retain the state data with higher credibility, including: The operational stability indicators of the information sources corresponding to multiple state data that constitute the cause of the conflict are obtained in the recent historical period. The operational stability indicators include data continuity, fluctuation range and historical anomaly rate. The data transmission path characteristics of the information source are obtained, including path topology length, node hop count, and data latency and packet loss in the recent period. Obtain the physical deployment information of the information source, and extract environmental interference characteristics based on the environmental conditions of its location. The environmental interference characteristics include temperature and humidity fluctuation amplitude, vibration interference frequency, and electromagnetic noise level. Based on the aforementioned operational stability indicators, data transmission path characteristics, and environmental interference characteristics, dynamic credibility scores are assigned to the corresponding information sources of multiple state data that constitute the cause of the conflict. Based on the preset credibility judgment rules, information source data with higher dynamic credibility scores are selected as state data to be retained after conflict resolution.

7. The integrated utility tunnel collaborative control method according to claim 1, characterized in that, Based on the aforementioned set of emergency operations, and according to preset multi-objective optimization criteria and logical dependencies between emergency operations, an emergency operation execution sequence is constructed, including: A multi-objective optimization criterion is preset, which includes prioritizing personnel safety, optimizing disaster control efficiency, and minimizing equipment damage as optimization objectives. Obtain the preset emergency target impact parameters corresponding to each emergency operation in the emergency operation set. The emergency target impact parameters are used to quantify the degree of impact of each emergency operation on multiple optimization targets. Based on the current fusion situation information, determine the priority weights of multiple optimization objectives; Establish a logical dependency graph between the emergency operations, and obtain logical dependencies from the logical dependency graph. The logical dependencies include operation order, mutual exclusion relationship and condition triggering relationship. Based on the emergency target impact parameters, the logical dependencies, and the priority weights, calculate the comprehensive priority score for each emergency operation; Based on the comprehensive priority score and the logical dependency relationship, the operation order of the emergency operations is set, and the emergency operation execution sequence is generated.

8. The integrated utility tunnel collaborative control method according to claim 4, characterized in that, Identify the propagation paths of indirect impacts triggered by implemented emergency operations within the utility tunnel, including: Obtain baseline fusion situational information prior to the execution of the emergency operation; After the emergency operation is executed, status data of multiple subsystems or areas that are not directly related to the emergency operation are continuously collected. The state data is compared with the baseline fused situational information to identify deviated state parameters or abnormal change areas; Based on the correlation between the deviation of the state parameters or the abnormal change area and the spatial distribution, energy and material flow direction and physical coupling rules of the emergency operation, it is determined whether there is an indirect impact propagation path caused by the emergency operation, and the propagation direction and path range of the indirect impact propagation path are determined.

9. The integrated utility tunnel collaborative control method according to claim 8, characterized in that, Collecting status data from subsystems or sensors that are not directly related to the emergency operation along the indirect impact propagation path, including: Based on the propagation direction and path range of the indirect influence propagation path, candidate sensors that have no direct control dependence, resource coupling or physical connection with the emergency operation are screened. The historical operation logs and current operation status of the candidate sensors are retrieved, and sensors with abnormal readings, signal interruptions, or verification failures are eliminated. A synchronous acquisition task is performed on the remaining candidate sensors after elimination to collect the environmental status data they monitor. The environmental status data includes temperature and humidity, smoke concentration, vibration frequency, electrical current and / or electromagnetic intensity. The environmental state data is subjected to integrity verification and anomaly detection. Incomplete or severely disturbed data is removed, and an indirect impact state dataset is constructed for secondary negative impact analysis.

10. A comprehensive utility tunnel collaborative control system, characterized in that, include: The condition list construction module is used to construct an operation execution condition list for each emergency operation. The operation execution condition list includes the triggering conditions, resource conditions, and safety conditions for the corresponding emergency operation in the integrated utility tunnel. The status data processing module is used to collect status data related to emergency operations, perform time synchronization, spatial correlation and data consistency processing on the status data, and construct fusion situation information that characterizes the current operation status of the utility tunnel. The status data includes sensor data, control signals and event information from multiple information sources. The condition determination and situation assessment module is used to determine whether each condition in the list of operation execution conditions for each emergency operation is met based on the fused situation information. If there is incomplete or contradictory fused situation information, dynamic reliability weights are assigned to the fused situation information according to the historical stability of the information source of the corresponding state data, transmission path characteristics and sensor physical characteristics. The operation execution condition list is then comprehensively evaluated in combination with preset logical rules to obtain the set of emergency operations that can be executed at the current moment. The execution sequence generation module is used to construct an emergency operation execution sequence based on the set of emergency operations, according to a preset multi-objective optimization criterion and the logical dependencies between emergency operations; The execution and feedback verification module is used to execute each emergency operation in the emergency operation execution sequence in sequence, and collect relevant feedback data after each emergency operation is executed to verify whether the emergency operation has achieved the expected effect. The sequence reconstruction module is used to, if the feedback verification result indicates that the emergency operation has not achieved the expected effect, suspend the subsequent emergency operations in the emergency operation execution sequence, update the fused situation information based on the feedback data and / or the relevant status data of the indirect impact caused by the emergency operation, and re-evaluate the operation execution condition list to update the emergency operation execution sequence.

Images