Joint controllability evaluation system and method for multi-agency cooperative fire response network

Abstract

This application relates to the technical field of a joint controllability assessment system and method for multi-agency collaborative fire response networks. The system and method include: incorporating multiple types of agencies participating in fire response into a unified network and defining their functions; establishing a time-based quantitative model of external support capabilities for each agency; introducing a correction factor based on real-time risk profiling of building IoT to dynamically correct support capabilities; and finally, integrating the corrected support capabilities of each agency with the internal controllability of fire protection professionals to calculate the joint controllability characterizing the overall fire resilience of the city. This invention, by quantifying the collaborative contributions of multiple agencies and integrating real-time IoT data, solves the technical problem of existing technologies being unable to dynamically assess the overall effectiveness of multi-agency collaborative fire response, providing a scientific decision-making basis for urban emergency command and dispatch.

Description

Technical Field

[0001] This application relates to the technical field of a joint controllability assessment system and method for a multi-agency collaborative fire response network, and in particular to a joint controllability assessment system and method for a multi-agency collaborative fire response network. Background Technology

[0002] The rapid and effective response to major urban fires relies heavily on efficient collaboration between professional firefighting forces and external agencies such as public security, traffic management, emergency medical services, gas, electricity, and community property management. However, the existing urban fire emergency response system suffers from the following structural and technical problems: First, the response is limited to a single entity. The existing system mainly relies on professional fire brigades as the sole response entity. When the fire is large-scale, the fire brigade alone cannot handle multiple tasks such as traffic control, personnel evacuation, gas cut-off, and power isolation simultaneously, which can easily lead to resource bottlenecks and low overall response efficiency.

[0003] Second, there is a lack of a unified, collaborative, and quantitative framework. Although the participating agencies act according to their functions, they operate independently and lack a technical framework that can uniformly describe and quantify the support capabilities of each party. This prevents the emergency command center from assessing, in advance or in real time, critical decision-making issues such as "how much the probability of overall success would increase if traffic police completed road closures and the gas company cut off the gas supply."

[0004] Third, external support capabilities are not included in the controllability assessment. Existing fire controllability assessments typically only focus on the internal handling capabilities of professional fire fighting forces, such as their arrival and water supply, and fail to quantify and integrate the support capabilities of external organizations, such as traffic clearing time and the probability of gas valve shut-off, into the overall assessment system.

[0005] Fourth, there is a disconnect between IoT data and collaborative decision-making. Data such as fire alarms, smoke detectors, and personnel density collected in real time by the building's IoT system are not dynamically applied to the response priorities and capacity assessments of external agencies. As a result, external support actions are often based on static plans rather than the real-time risk status within the building. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this application provides a joint controllability assessment system and method for multi-agency collaborative fire response networks.

[0007] Firstly, this application provides a joint controllability assessment method for multi-agency collaborative fire response networks, employing the following technical solution: A joint controllability assessment method for multi-agency collaborative fire response networks includes the following steps: The organizations involved in fire response are divided into multiple categories, including at least public security organs, traffic management departments, medical emergency institutions, gas companies, power companies, urban operation centers, property management companies, and community organizations, and the functions of each category of organizations in fire response are defined. An external support capability model is established for each type of collaborative mechanism. This model uses time as the independent variable and outputs the probability value of the mechanism completing its key support actions at different times after the event occurs. The system acquires real-time IoT risk data of the target building, calculates a dynamic correction factor based on the real-time risk data, and uses the dynamic correction factor to adjust the probability value output by the external support capability model in real time to obtain the corrected support capability. The internal controllability of fire protection professional forces is obtained, and based on the modified support capacity and the internal controllability, the joint controllability of the multi-agency collaborative fire response network is calculated through a joint controllability model, which is constructed based on the probability multiplication rule.

[0008] Optionally, the external support capability model includes: ; in, Let m be the probability value of the support capability of mechanism m at time t; is the maximum support capacity coefficient of mechanism m, with a value range of [0,1]. The capability accumulation rate parameter for mechanism m is a parameter; the larger the value, the faster the capability accumulation. For institution m, this is the earliest moment from the occurrence of an event to the point where it begins to provide effective support; when hour, .

[0009] Optionally, the dynamic correction factor Fire risk profile score based on the target building and real-time personnel activity indicators The calculation is performed using the following formula: ; in, and The preset weighting coefficients, The maximum designed occupancy capacity of the target building; calculated Limited to a range Inside; The revised support capacity The calculation method is as follows: ; in The probability value output by the external support capability model.

[0010] Optionally, the calculation formula for the joint controllability model is: ; in, For the joint controllability, To ensure the internal controllability of fire protection professional forces, Let m be the current availability coefficient of the organization, with a value range of [0,1], and M be the total number of external organizations participating in the collaboration; the product symbol iterates through all M external organizations.

[0011] Optionally, the current availability coefficient The calculation method is as follows: ; in, The number of personnel from organization m who arrived at the scene. The standard number of personnel required for the institution as specified in the contingency plan. The overall availability of the equipment on site was scored. The standard equipment availability rate is scored.

[0012] Optionally, it also includes a collaborative process executed by the city operation center, which includes: Early warning phase: In the initial period after the event occurs, receive IoT alarm information, send notifications to the public security authorities, medical emergency institutions and property management, and assign tasks; Response Phase: During the handling period following the warning phase, the traffic management department, gas company, and power company are coordinated to complete their key support actions, and the current joint controllability is calculated in real time to dynamically adjust resource allocation; Stabilization Phase: During the period following the response phase, the community organizations are coordinated to carry out personnel resettlement and post-resettlement support.

[0013] Secondly, this application provides a joint controllability assessment system for a multi-agency collaborative fire response network, employing the following technical solution: A joint controllability assessment system for multi-agency collaborative fire response networks includes: The organization modeling module is used to configure the parameters of the identity, function, and external support capability models of the various types of collaborative organizations; The Internet of Things (IoT) interface module is used to acquire the real-time IoT risk data of the target building and calculate the dynamic correction factor. An evaluation calculation module is used to receive the outputs of the mechanism modeling module and the Internet of Things interface module, and to perform the calculation of the joint controllability model to generate the joint controllability; The command and dispatch interface module is used to push the joint controllability generated by the evaluation and calculation module and the corrected support capabilities of each agency to the command platform of the city operation center.

[0014] Optionally, the mechanism modeling module is configured to be able to determine the maximum support capacity coefficient for each type of collaborative mechanism. The capability accumulation rate parameter and the earliest moment when effective support begins to be generated. Perform independent calibration and storage.

[0015] Optionally, the evaluation calculation module is configured to receive dynamic correction factors pushed by the IoT interface module in real time, and trigger a recalculation of the joint controllability in response to the update of the dynamic correction factors.

[0016] In summary, this application includes at least the following beneficial technical effects: This application incorporates up to eight types of urban agencies, including public security, transportation, medical, and municipal services, into a unified quantitative framework for fire protection collaboration, addressing the technical blind spot where multi-agency collaboration could not be objectively assessed. By constructing an external support capability function that includes parameters such as capability accumulation rate and maximum support capacity, the response process of each agency can be quantified, providing a mathematical basis for comparing the benefits of different collaborative schemes. Real-time risk data from the Internet of Things in buildings is integrated into the assessment model as dynamic correction factors, enabling the assessment results to reflect the actual risk status of buildings at present, achieving a technological leap from static plans to dynamic decision-making. Finally, through organic integration with existing internal fire protection controllability models, a complete closed loop for urban fire resilience assessment is formed, encompassing internal fire protection forces and external collaborative support. This makes the framework highly abstract and easily adaptable to emergency management systems in different countries and regions. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the joint controllability assessment method in this application.

[0018] Figure 2 This is a system architecture diagram of the multi-agency collaborative fire response network in this application.

[0019] Figure 3 This is a function representing the external support capability of some mechanisms in the embodiments of this application. A schematic diagram of the curve changing over time.

[0020] Figure 4 For the joint controllability in the embodiments of this application Simulation curves showing changes over time and compared with a non-cooperative scheme. Detailed Implementation

[0021] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0022] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] This application discloses a joint controllability assessment system and method for a multi-agency collaborative fire response network. Its core is to incorporate multiple types of urban agencies into a unified quantitative assessment framework, describe the external support capabilities of each agency over time using a mathematical model, dynamically correct these capabilities using real-time data from the Internet of Things in buildings, and finally probabilistically integrate them with the internal controllability of fire protection professionals to obtain a joint controllability index that can dynamically reflect the overall fire resilience of the city.

[0024] like Figure 1 As shown, the joint controllability assessment method for a multi-agency collaborative fire response network includes the following steps: The organizations involved in fire response are divided into multiple categories, including at least public security organs, traffic management departments, medical emergency institutions, gas companies, power companies, urban operation centers, property management companies, and community organizations, and the functions of each category of organizations in fire response are defined. An external support capability model is established for each type of collaborative mechanism. This model uses time as the independent variable and outputs the probability value of the mechanism completing its key support actions at different times after the event occurs. The system acquires real-time IoT risk data of the target building, calculates a dynamic correction factor based on the real-time risk data, and uses the dynamic correction factor to adjust the probability value output by the external support capability model in real time to obtain the corrected support capability. The internal controllability of fire protection professional forces is obtained, and based on the modified support capacity and the internal controllability, the joint controllability of the multi-agency collaborative fire response network is calculated through a joint controllability model, which is constructed based on the probability multiplication rule.

[0025] like Figure 2As shown, in the multi-agency collaborative fire response network architecture constructed in this application, the city operation center serves as the command node, overseeing eight types of collaborative agencies: public security organs, traffic management departments, emergency medical institutions, gas companies, power companies, property management companies, and community organizations. Each type of agency is configured with a unique identity and predefined functional interfaces within the system.

[0026] In practical implementation, the following information for the above eight types of institutions is first configured through the institution modeling module: Identity information: organization name, mailing address, API call method; Functional definition: The key support actions of this agency in fire response (e.g., the key support action of traffic management departments is "clearing the core road section"); Capability model parameters: , , These parameters are initially calibrated based on the city's historical emergency response statistics or relevant industry standards.

[0027] For example, traffic management departments can analyze statistical data on the clearing of road sections by traffic police in similar fire scenarios over the past three years to determine the maximum support capacity coefficient. (This indicates that even under optimal conditions, there is still a 15% probability that clearing the system will not be completed due to uncontrollable factors such as extreme congestion); Capacity accumulation rate Minutes (reflecting clearing efficiency); Earliest effective support time Minutes (i.e., average arrival time of traffic police). Once these parameters are calibrated, they are stored in the database of the organization modeling module for use by the evaluation calculation module.

[0028] like Figure 3 As shown, the external support capability functions of different mechanisms The growth curves differ over time. In practice, the evaluation and calculation module calculates the probability value of the support capacity of each mechanism at any time t using the following formula: ; To illustrate with a specific calibration example: For gas companies, calibration is performed based on the "Regulations on the Administration of Urban Gas" and local emergency drill data. , minute, Minutes. Then, after the fire occurred... At the minute mark, its support capacity is: ; That is, the probability that the gas company will be able to shut off the valve at this time is approximately 73.8%. Figure 2 The gas company curve illustrates this growth process.

[0029] Similarly, power companies can label it as , / minute, Minutes; medical emergency services can be designated as , / minute, Minutes. These parameters can all be configured independently according to the actual situation of the city where implementation takes place.

[0030] This application utilizes real-time data from the building's Internet of Things (IoT) to dynamically adjust external support capabilities. In specific implementation, the IoT interface module periodically (e.g., every 30 seconds) obtains the following data from the building's fire protection IoT platform: Fire risk profile score, with a value range of [0,1], is calculated based on the status of smoke detectors, heat detectors, sprinkler systems, etc. in the building (the existing FSP algorithm can be used). Current personnel activity indicators can be obtained in real time from Wi-Fi probes, Bluetooth beacons, video analytics systems, etc. within the building; The building's maximum designed capacity for people is pre-stored in the system database.

[0031] Dynamic correction factor The calculation is performed in the IoT interface module, and its formula is: ; In a preferred embodiment, take , , , .

[0032] when (High risk) When (crowded), the calculation is as follows After limiting, the value is set to 1.27. This value is then pushed to the evaluation calculation module.

[0033] The evaluation calculation module received Then, the original support capacity was modified: ; The value range is [0.7, 1.3], which can be defined as the task difficulty coefficient (the task difficulty coefficient mentioned in this specification is the specific value of the dynamic correction factor mentioned in the claims at the implementation level; the two are different expressions of the same physical quantity, obtained by the aforementioned dynamic correction factor through a conversion relationship negatively correlated with task difficulty, so that the supporting capacity is correspondingly discounted as the real-time risk of the building increases). This indicates that the task is more difficult than the benchmark (the supporting capabilities are discounted). This indicates that the task difficulty is lower than the benchmark (the support capability is amplified). The calculation formula is negatively correlated with the task difficulty, specifically: ; Thus, when , hour, Take 0.73. After correction. This reduced the supporting contribution.

[0034] The evaluation calculation module calculates joint controllability in real time using the following formula: ; in: The internal controllability of fire protection professional forces is obtained from the internal controllability assessment system of fire protection professional forces through an external interface and is dynamically updated over time; The current availability coefficient of organization m is calculated using the following formula: ; in This represents the number of personnel from organization m who have arrived at the site at time t (obtained through GPS positioning or on-site reporting). The standard number of personnel required by the contingency plan. The overall availability rate of the equipment on site is scored (based on the equipment self-inspection report or manually entered). The standard intact rate is (usually taken as 1.0).

[0035] like Figure 4 As shown, the curve illustrates the joint controllability. The process of change over time. The solid line represents the controllability curve using the method of this application (integrating multi-agency collaboration), and the dashed line represents the controllability curve relying solely on fire protection professionals. The controllability curve is shown in the figure. As can be seen from the figure, during the critical window period of approximately 15-30 minutes after the event, the method of this application significantly improved the overall controllability. This is due to the gradual accumulation and effectiveness of the support capabilities of various external institutions during this period.

[0036] Taking a fire scenario in a 20-story commercial complex as an example, this paper details the complete implementation process of this application.

[0037] Step 1: System Initialization and Parameter Configuration In the institutional modeling module, the following parameters are pre-configured (based on historical data of the city): Step 2: Fire Occurrence and IoT Data Collection At 14:23, the IoT system triggered a fire alarm, and the IoT interface module received the following: , Calculate the task difficulty coefficient: Take 0.73.

[0038] Step 3: Real-time data update and controllability calculation: exist At 14:40, the statuses are as follows: Internal controllability assessment system provides (The fire department has arrived at the scene); Actual attendance of each organization: Traffic management department: 5 people, equipment availability rate 1.0; Gas company: 4 people, equipment availability rate 0.95; Power company: 4 people, equipment availability rate 0.90; Medical emergency organization: 5 people (1 person missing), equipment availability rate 0.95.

[0039] Calculate the availability coefficient for each institution: ; ; ; ; Calculate the original support capacity of each institution: ; ; ; ; Corrected support capacity: ; ; ; ; Substituting into the joint controllability formula (taking traffic management departments, gas companies, and power companies as examples; the contribution of emergency medical services to controllability is indirect and optional): ; That is, the joint controllability is 96.8%, which is significantly higher than the 82% that relies solely on fire fighting forces.

[0040] Step 4: Results Output and Decision Support The evaluation calculation module will The revised support capabilities of each institution are pushed to the command platform of the city operation center through the command and dispatch interface module. Command personnel can intuitively see that the overall controllability is high, but the availability of medical emergency services is low (one person is missing), and can issue reinforcement orders to medical emergency services accordingly. If the calculated... If the level falls below a preset threshold (e.g., 80%), the system will automatically issue a warning, indicating a high risk.

[0041] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A joint controllability assessment method for multi-agency collaborative fire response networks, characterized in that, Includes the following steps: The organizations involved in fire response are divided into multiple categories, including at least public security organs, traffic management departments, medical emergency institutions, gas companies, power companies, urban operation centers, property management companies, and community organizations, and the functions of each category of organizations in fire response are defined. An external support capability model is established for each type of collaborative mechanism. This model uses time as the independent variable and outputs the probability value of the mechanism completing its key support actions at different times after the event occurs. The system acquires real-time IoT risk data of the target building, calculates a dynamic correction factor based on the real-time risk data, and uses the dynamic correction factor to adjust the probability value output by the external support capability model in real time to obtain the corrected support capability. The internal controllability of fire protection professional forces is obtained, and based on the modified support capacity and the internal controllability, the joint controllability of the multi-agency collaborative fire response network is calculated through a joint controllability model, which is constructed based on the probability multiplication rule.

2. The joint controllability assessment method for a multi-agency collaborative fire response network according to claim 1, characterized in that, The external support capability model includes: ； in, Let m be the probability value of the support capability of mechanism m at time t; is the maximum support capacity coefficient of mechanism m, with a value range of [0,1]. The capability accumulation rate parameter for mechanism m is a parameter; the larger the value, the faster the capability accumulation. For institution m, this is the earliest moment from the occurrence of an event to the point where it begins to provide effective support; when hour, .

3. The joint controllability assessment method for a multi-agency collaborative fire response network according to claim 1, characterized in that, The dynamic correction factor Fire risk profile score based on the target building and real-time personnel activity indicators The calculation is performed using the following formula: ； in, and The preset weighting coefficients, The maximum designed occupancy capacity of the target building; calculated Limited to a range Inside; The revised support capacity The calculation method is as follows: ； in The probability value output by the external support capability model.

4. The joint controllability assessment method for a multi-agency collaborative fire response network according to claim 1, characterized in that, The calculation formula for the joint controllability model is as follows: ； in, For the joint controllability, To ensure the internal controllability of fire protection professional forces, Let m be the current availability coefficient of the organization, with a value range of [0,1], and M be the total number of external organizations participating in the collaboration; the product symbol iterates through all M external organizations.

5. The joint controllability assessment method for a multi-agency collaborative fire response network according to claim 4, characterized in that, Current availability coefficient The calculation method is as follows: ； in, The number of personnel from organization m who arrived at the scene. The standard number of personnel required for the institution as specified in the contingency plan. The overall availability of the equipment on site was scored. The standard equipment availability rate is scored.

6. The joint controllability assessment method for a multi-agency collaborative fire response network according to claim 1, characterized in that, It also includes a collaborative process executed by the city operations center, which includes: Early warning phase: In the initial period after the event occurs, receive IoT alarm information, send notifications to the public security authorities, medical emergency institutions and property management, and assign tasks; Response Phase: During the handling period following the warning phase, the traffic management department, gas company, and power company are coordinated to complete their key support actions, and the current joint controllability is calculated in real time to dynamically adjust resource allocation; Stabilization Phase: During the period following the response phase, the community organizations are coordinated to carry out personnel resettlement and post-resettlement support.

7. A joint controllability assessment system for a multi-agency collaborative fire response network, used to implement the method according to any one of claims 1 to 6, characterized in that, include: The organization modeling module is used to configure the parameters of the identity, function, and external support capability models of the various types of collaborative organizations; The Internet of Things (IoT) interface module is used to acquire the real-time IoT risk data of the target building and calculate the dynamic correction factor. An evaluation calculation module is used to receive the outputs of the mechanism modeling module and the Internet of Things interface module, and to perform the calculation of the joint controllability model to generate the joint controllability; The command and dispatch interface module is used to push the joint controllability generated by the evaluation and calculation module and the corrected support capabilities of each agency to the command platform of the city operation center.

8. The joint controllability assessment system for a multi-agency collaborative fire response network according to claim 7, characterized in that, The mechanism modeling module is configured to be able to determine the maximum support capacity coefficient for each type of collaborative mechanism. The capability accumulation rate parameter and the earliest moment when effective support begins to be generated. Perform independent calibration and storage.

9. The joint controllability assessment system for a multi-agency collaborative fire response network according to claim 7, characterized in that, The evaluation calculation module is configured to receive dynamic correction factors pushed by the IoT interface module in real time, and trigger a recalculation of the joint controllability in response to the update of the dynamic correction factors.

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