Fire-fighting deployment method and device under PLC (Programmable Logic Controller) IOT (Internet of Things) framework and IOT platform

Abstract

The invention relates to the field of fire safety engineering, and discloses a fire-fighting deployment method and device under a PLC Internet of Things framework and an Internet of Things platform, and the method comprises the steps: firstly obtaining fire-fighting equipment in a target area, analyzing a cooperative linkage mode, and formulating an early warning response rule; analyzing a data transmission channel according to a rule, querying a performance threshold value, detecting channel performance data, calculating a resource conflict value according to the performance threshold value, and constructing a fire-fighting resource deployment process; secondly, partitioning a deployment process task, and determining an execution sequence and an execution time window; and finally, monitoring a window activity state, constructing a deployment response path, identifying a path core node, and making a fire-fighting optimization deployment scheme. And the response efficiency of fire early warning is improved.

Description

Technical Field

[0001] This invention relates to the field of fire safety engineering, and in particular to a fire protection deployment method, device and IoT platform under a PLC IoT framework. Background Technology

[0002] Fire protection systems are core facilities for ensuring building safety. Traditional deployments often rely on independent sensors and wired communication, which suffer from problems such as complex wiring, poor flexibility, and insufficient real-time response. With the development of power line carrier communication (PLC) technology, its ability to transmit data through power lines provides a new approach for fire protection IoT.

[0003] Currently, existing fire protection deployment methods are mainly based on wireless sensor networks or traditional wired systems, but they have the following drawbacks: First, wireless signals are easily interfered with by building structures, resulting in communication blind spots; second, wired systems have poor scalability and high transformation costs; third, they lack dynamic resource scheduling capabilities, making it difficult to cope with the sudden traffic demands in fire scenarios. In addition, existing solutions mostly focus on monitoring a single node and do not fully utilize the wide coverage advantage of PLCs to achieve multi-terminal collaborative early warning. These problems lead to fire protection system response delays, high false alarm rates, and low operation and maintenance efficiency. Therefore, it is necessary to develop a fire protection deployment method under the PLC IoT framework to optimize resource allocation and fault tolerance, thereby improving the response efficiency of fire early warning. Summary of the Invention

[0004] This invention provides a fire protection deployment method, device, and IoT platform within a PLC IoT framework to improve the response efficiency of fire early warning systems.

[0005] Firstly, a fire protection deployment method under a PLC IoT framework is provided, including: Obtain the fire-fighting equipment corresponding to the target area, analyze the collaborative linkage mode between the fire-fighting equipment, and formulate early warning response rules corresponding to the collaborative linkage mode; Based on the aforementioned early warning response rules, the data transmission channels in the preset PLC IoT framework are analyzed, the performance thresholds corresponding to the data transmission channels are queried, and based on the performance thresholds, the channel performance data corresponding to the data transmission channels in the PLC IoT framework is detected. Based on the channel performance data, the resource conflict value of the fire-fighting equipment during deployment is calculated, and based on the resource conflict value, the deployment process corresponding to the fire-fighting resources in the PLC IoT framework is constructed. The deployment process is divided into task blocks to obtain deployment task blocks. The execution order of the deployment task blocks is analyzed, and the execution time window corresponding to the deployment task blocks is constructed based on the execution order. Monitor the window activity status corresponding to the execution time window, construct the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity status, identify the core nodes of the path corresponding to the deployment response path, and formulate an optimized fire protection deployment scheme under the PLC IoT framework based on the core nodes of the path.

[0006] Secondly, a fire protection deployment device under a PLC IoT framework is provided, including: The rule-making module is used to acquire the fire-fighting equipment corresponding to the target area, analyze the collaborative linkage mode between the fire-fighting equipment, and formulate the early warning response rules corresponding to the collaborative linkage mode. The data detection module is used to analyze the data transmission channels in the preset PLC IoT framework based on the early warning response rules, query the performance thresholds corresponding to the data transmission channels, and detect the channel performance data corresponding to the data transmission channels in the PLC IoT framework based on the performance thresholds. The process construction module is used to calculate the resource conflict value of the fire-fighting equipment in the deployment based on the channel performance data, and to construct the deployment process corresponding to the fire-fighting resources in the PLC IoT framework based on the resource conflict value. The window construction module is used to divide the deployment process into task blocks to obtain deployment task blocks, analyze the execution order corresponding to the deployment task blocks, and construct the execution time window corresponding to the deployment task blocks based on the execution order. The scheme formulation module is used to monitor the window activity status corresponding to the execution time window, construct the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity status, identify the core nodes of the deployment response path, and formulate the fire protection optimization deployment scheme under the PLC IoT framework based on the core nodes of the path.

[0007] Thirdly, an Internet of Things (IoT) platform is provided, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the fire protection deployment method under the aforementioned PLC IoT framework.

[0008] Compared to the problems described in the background technology, firstly, this invention, by acquiring the fire-fighting equipment corresponding to the target area and analyzing the collaborative linkage mode between the fire-fighting equipment, can clearly grasp the correlation mechanism between the equipment, optimize resource allocation, break the limitations of independent operation of equipment, significantly improve the overall response speed and handling efficiency of the fire-fighting system, enhance fire prevention and control capabilities, and effectively protect the safety of people's lives and property. Secondly, based on the early warning response rules, this invention analyzes the data transmission channels in the preset PLC IoT framework, which can accurately match the data transmission needs of fire-fighting operations, ensure the transmission priority and reliability of key information such as alarms and linkage commands, and optimize channel resource allocation, improving the stability and timeliness of data transmission, laying a solid communication foundation for the real-time response and collaborative operation of the fire-fighting system. Thirdly, based on the channel performance data, this invention calculates the resource conflict value of the fire-fighting equipment in deployment, which can accurately quantify the resource competition between equipment, clearly grasp the power line transmission resource carrying pressure, and avoid data transmission delays caused by resource conflicts. This invention addresses issues such as chaotic command execution, providing crucial data support for optimizing fire equipment deployment schemes and ensuring efficient and stable system operation. Furthermore, by dividing the deployment process into task blocks, this invention breaks down the complex fire resource deployment work into clear and independent units, clarifying the responsibilities of each stage, avoiding task confusion and omissions, optimizing resource allocation rhythm, improving execution efficiency, and facilitating the orderly advancement of fire resource deployment under the PLC IoT framework, ensuring deployment quality and system synergy. Finally, by monitoring the window activity status corresponding to the execution time window, this invention can monitor the progress and resource usage of deployment tasks in real time, promptly detecting anomalies such as task delays and resource conflicts, and quickly adjusting task timing or reallocating resources to ensure the deployment process proceeds as planned, improving the controllability and efficiency of fire resource deployment, and ensuring the timeliness and stability of system construction under the PLC IoT framework. Therefore, the fire deployment method, device, and IoT platform under the PLC IoT framework proposed in this invention can improve the response efficiency of fire early warning. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of an application environment for a fire protection deployment method under a PLC IoT framework in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a fire protection deployment method under a PLC IoT framework in one embodiment of the present invention; Figure 3 This is a schematic diagram of a fire-fighting deployment device under a PLC IoT framework in one embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an Internet of Things platform according to one embodiment of the present invention; Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] The fire protection deployment method under the PLC IoT framework provided in this embodiment of the invention can be applied to, for example... Figure 1 In this application environment, the client communicates with the server via a network. The server can obtain the fire-fighting equipment corresponding to the target area through the client, analyze the collaborative linkage mode between the fire-fighting equipment, and formulate early warning response rules corresponding to the collaborative linkage mode. Based on the early warning response rules, the server analyzes the data transmission channels in the preset PLC IoT framework, queries the performance thresholds corresponding to the data transmission channels, and detects the channel performance data corresponding to the data transmission channels in the PLC IoT framework based on the performance thresholds. Based on the channel performance data, the server calculates the resource conflict value of the fire-fighting equipment in deployment, and constructs the deployment process corresponding to the fire-fighting resources in the PLC IoT framework based on the resource conflict value. The server divides the deployment process into task blocks to obtain deployment task blocks, analyzes the execution order corresponding to the deployment task blocks, and constructs the execution time window corresponding to the deployment task blocks based on the execution order. The server monitors the window activity status corresponding to the execution time window, constructs the deployment response path of the PLC IoT framework in fire-fighting deployment based on the window activity status, identifies the core nodes of the path corresponding to the deployment response path, and formulates an optimized fire-fighting deployment scheme under the PLC IoT framework based on the core nodes of the path. The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0013] Please see Figure 2 As shown, Figure 2 A flowchart illustrating a fire protection deployment method under a PLC IoT framework provided in this embodiment of the invention includes the following steps: S1. Obtain the fire-fighting equipment corresponding to the target area, analyze the collaborative linkage mode between the fire-fighting equipment, and formulate the early warning response rules corresponding to the collaborative linkage mode.

[0014] This invention, by acquiring the fire-fighting equipment corresponding to the target area and analyzing the collaborative linkage mode between the fire-fighting equipment, can clearly grasp the correlation mechanism between the equipment, optimize resource allocation, break the limitations of independent operation of equipment, significantly improve the overall response speed and handling efficiency of the fire-fighting system, enhance fire prevention and control capabilities, and effectively protect the safety of people's lives and property.

[0015] The target area refers to the specific spatial range for carrying out fire protection deployment. It can be a building, an industrial park, a community, etc., flexibly defined according to fire management needs. For example, in a large commercial complex, the entire complex is the target area, encompassing different functional areas such as shopping malls, office buildings, and underground parking lots, requiring unified deployment and management of its fire protection system. The fire protection equipment refers to various facilities and devices used for fire prevention, monitoring, fighting, and ensuring personnel evacuation. Common examples include fire detectors, fire extinguishers, fire hydrants, fire doors, emergency lighting, and evacuation signs. For instance, in an office building, smoke detectors installed in corridors can detect... Real-time fire detection, fire extinguishers and fire hydrants can be used for initial fire suppression, and fire doors can prevent the spread of fire; these are all fire-fighting equipment. The collaborative linkage mode refers to the operating mode in which fire-fighting equipment cooperates and works together according to preset rules when a fire occurs. When a fire detector in a certain area detects that the smoke concentration exceeds the standard, it will trigger nearby audible and visual alarms to sound an alarm, and at the same time, emergency lighting will automatically turn on, fire-resistant roller shutters will descend, and fire broadcasts will broadcast evacuation guidance. All equipment works together to improve the fire response effect. Optionally, the acquisition of the fire-fighting equipment corresponding to the target area can be achieved through IoT device scanning methods, such as using the Honeywell Xtralis VESDA very early smoke detection system combined with BIM building information model to locate the equipment, thereby obtaining the fire-fighting equipment. The analysis of the collaborative linkage mode between the fire-fighting equipment can be achieved through intelligent agent modeling and simulation methods, such as using the AnyLogic multi-agent simulation platform to build an interactive model of fire-fighting equipment, and combining Petri nets for state transition analysis, thereby obtaining the collaborative linkage mode.

[0016] Furthermore, by formulating early warning response rules corresponding to the collaborative linkage mode, this invention can clarify the execution standards and operating procedures of each fire-fighting equipment in a fire scenario, avoid chaos and disorder, help improve the automation response level of the fire-fighting system, reduce the risk of human misjudgment and operational errors, maximize the effectiveness of fire-fighting equipment, and build a rule-based defense line for fire prevention and emergency rescue.

[0017] The aforementioned early warning response rules refer to the specific rules that fire-fighting equipment must follow when working together, which are ultimately determined based on logical optimization relationships. For example, it is stipulated that when the smoke concentration in a certain area exceeds a certain value and the temperature reaches a threshold, an audible and visual alarm will be triggered immediately, and the automatic sprinkler system in that area will be activated after 10 seconds. At the same time, an alarm message will be sent to the fire control room. This series of clear rules constitutes the early warning response rules.

[0018] As an embodiment of the present invention, the step of formulating the early warning response rules corresponding to the collaborative linkage mode includes: parsing the event triggering features in the collaborative linkage mode; based on the event triggering features, traversing a preset response strategy library to obtain an event response set; performing logical adaptation on the event response set to obtain event response logic; removing logical conflict items in the event response logic to obtain a logical optimization relationship; and formulating the early warning response rules corresponding to the collaborative linkage mode based on the logical optimization relationship.

[0019] The event triggering characteristics refer to the key features and conditions that trigger coordinated fire equipment events, including changes in physical quantities such as smoke concentration, temperature changes, and the appearance of flames during a fire, as well as human operation signals such as manual alarm button triggering. For example, when a smoke detector detects that the smoke concentration exceeds a threshold, or when an alarm button is manually pressed, the characteristics of these signals constitute the event triggering characteristics, used to determine whether to activate fire linkage. The preset response strategy library refers to a set of pre-stored response strategies for different fire events, including operation schemes for fire equipment in various fire scenarios. For example, the response strategy library stores strategies such as returning fire elevators to the bottom, activating smoke extraction systems, and broadcasting evacuation routes in the event of a high-rise fire, providing a reference for responding to various fire situations. The event response set refers to all possible applicable response strategy combinations selected from the preset response strategy library based on the event triggering characteristics. For example, when a sudden temperature rise and smoke are detected in a certain area, relevant strategies such as activating fire alarms, turning on emergency lighting, and closing fire doors are selected from the response strategy library. These strategies together constitute... This constitutes the event response set for this incident. The event response logic refers to the logical sorting and integration of various response strategies within the event response set, clarifying the sequence, triggering conditions, and interrelationships between each strategy. For example, in a fire incident, the fire detector alarm is triggered first, followed by the activation of emergency lighting and evacuation signs, and the simultaneous activation of smoke exhaust fans. The logical relationships and execution order among these strategies form the event response logic. The logical conflict items refer to contradictory strategies or conditions in the event response logic that cannot be executed simultaneously or have an unreasonable execution order. For example, in the same area, if there are two strategies simultaneously—opening fire doors for evacuation and closing fire doors to prevent the spread of fire—a logical conflict item is formed. The logical optimization relationship refers to the optimization and adjustment of the remaining response strategies after removing logical conflict items, forming a reasonable, orderly, and efficient logical relationship. For example, the optimized logical relationship might be to first trigger the fire alarm, then delay for several seconds to activate the smoke exhaust system to avoid premature smoke spread, and simultaneously activate emergency lighting. These strategies form the optimized logical relationship.

[0020] Furthermore, the analysis of event triggering features in the collaborative linkage mode can be achieved through time-series pattern mining algorithms, such as using the PrefixSpan sequence pattern mining method to analyze the temporal correlation of historical alarm events, thereby obtaining event triggering features; the traversal of the preset response strategy library can be achieved through graph database retrieval technology, such as using the Neo4j graph database to construct a response strategy knowledge graph and matching similar event cases based on the Cypher query language, thereby obtaining an event response set; the logical adaptation of the event response set can be achieved through rule engine reasoning methods, such as using the Drools rule engine to execute the Rete algorithm for condition-action matching, thereby obtaining the event response logic; the removal of logical conflicts in the event response logic can be achieved through constraint solving algorithms, such as applying the Z3 solver to detect and eliminate mutual exclusion constraints between strategies, thereby obtaining logical optimization relationships; the formulation of early warning response rules corresponding to the collaborative linkage mode can be achieved through decision tree induction methods, such as using the C4.5 algorithm to generate a classification rule tree and adjusting threshold parameters based on expert experience, thereby obtaining early warning response rules.

[0021] S2. Based on the early warning response rules, analyze the data transmission channels in the preset PLC IoT framework, query the performance thresholds corresponding to the data transmission channels, and detect the channel performance data corresponding to the data transmission channels in the PLC IoT framework based on the performance thresholds.

[0022] Based on the aforementioned early warning response rules, this invention analyzes the data transmission channels in the preset PLC IoT framework, which can accurately match the data transmission needs of fire protection services, ensure the transmission priority and reliability of key information such as alarms and linkage commands, and optimize channel resource allocation to improve the stability and timeliness of data transmission, thus laying a solid communication foundation for the real-time response and collaborative operation of the fire protection system.

[0023] The pre-defined PLC IoT framework refers to a pre-built IoT architecture based on Power Line Carrier Communication (PLC) technology. It uses power lines to achieve data transmission and system linkage of fire protection equipment, integrating components such as sensors, controllers, and communication modules to construct a fire protection IoT network covering the target area. For example, in an office building, the existing power lines can be used to build the PLC IoT framework, connecting smoke detectors, fire hydrant controllers, and other equipment on each floor to the network without the need for additional communication cables. The data transmission channel refers to the path in the PLC IoT framework that uses power lines to transmit data, carrying monitoring data and control commands from fire protection equipment. Signal transmission can be achieved through the phase and neutral wires of the power lines. For example, when a smoke detector on a certain floor detects a fire, the data is transmitted to the host computer in the fire control room through the power line of that floor (i.e., the data transmission channel), and the linkage command is simultaneously transmitted to the emergency lighting controller in the corresponding area, completing the alarm and equipment linkage. Optionally, the analysis of the data transmission channel in the pre-defined PLC IoT framework can be achieved through industrial protocol reverse engineering, such as using Wireshark network analysis tools to capture Modbus TCP protocol data packets and combining them with SCL structured control language to parse the communication topology, thereby obtaining the data transmission channel.

[0024] Furthermore, by querying the performance thresholds corresponding to the data transmission channel, this invention can clarify the key indicator range for normal channel operation, providing a benchmark for real-time monitoring of channel performance, enabling timely detection of channel anomalies, facilitating early optimization measures, ensuring the stability and reliability of fire protection data transmission, and ensuring the effective implementation of early warning response rules at the communication level.

[0025] The performance threshold refers to the critical value or standard range for measuring whether the performance of the data transmission channel in the PLC IoT framework is normal. It includes the upper and lower limits of key indicators such as data transmission rate, signal attenuation rate, bit error rate, and anti-interference capability. For example, the performance threshold of a data transmission channel can be set as follows: transmission rate not less than 100kbps, signal attenuation rate not more than 30dB, and bit error rate less than 0.1%. When the actual monitored channel transmission rate drops to 80kbps (below the lower limit of the threshold), it indicates that the channel performance may be abnormal and needs to be investigated and optimized in time. Optionally, the performance threshold corresponding to the data transmission channel can be obtained by using an industrial protocol specification parsing method, such as extracting the cycle time parameter of PROFINET real-time communication based on the IEC 61784-CPF standard document to obtain the performance threshold.

[0026] Furthermore, based on the performance threshold, this invention detects the channel performance data corresponding to the data transmission channel in the PLC IoT framework, which can monitor the actual operating status of the data transmission channel in real time, accurately identify whether it deviates from the normal operating range, and provide a scientific basis for dynamically adjusting the fire data transmission strategy and optimizing the channel resource allocation.

[0027] The data transmission channel refers to the path for data transmission between fire protection equipment within the PLC IoT framework, utilizing power lines. Power lines serve as the carrier for transmitting fire alarm and equipment control information, eliminating the need for rewiring. For example, in an office building, smoke detectors on each floor transmit detection data to the fire control room via power lines; this power line is the data transmission channel, enabling effective data transmission. The channel performance data refers to a comprehensive set of data reflecting the overall performance of the data transmission channel, integrating various aspects of its performance information. This data provides a basis for channel performance evaluation. For instance, summarizing data such as transmission rate, bit error rate, signal strength, and latency of a data transmission channel forms a complete set of data, which can be used to determine the quality of channel performance.

[0028] As an embodiment of the present invention, the step of detecting the channel performance data corresponding to the data transmission channel in the PLC IoT framework based on the performance threshold includes: parsing the monitoring parameter standard corresponding to the performance threshold; locating the data transmission channel in the PLC IoT framework based on the monitoring parameter standard; obtaining the channel operating status corresponding to the data transmission channel; analyzing the status performance index corresponding to the channel operating status; and detecting the channel performance data corresponding to the data transmission channel in the PLC IoT framework based on the status performance index.

[0029] The monitoring parameter standards refer to specific quantitative indicators and specifications used to measure the performance of data transmission channels in the PLC IoT framework. They serve as the basis for judging whether the channel is operating normally and cover standards such as transmission rate, signal strength, and bit error rate. For example, the data transmission channel is required to have a transmission rate of ≥100Mbps, a signal strength of not less than -60dBm, and a bit error rate of ≤0.01%. These specific numerical requirements constitute the monitoring parameter standards. The data transmission channel refers to the path for data transmission between fire protection equipment within the PLC IoT framework, utilizing power lines as a carrier to transmit fire alarm and equipment control information without the need for rewiring. For example, in an office building, smoke detectors on each floor transmit detection data to the fire control room via power lines; this power line is the data transmission channel, enabling effective data transmission. The channel operating status describes the real-time working condition of the data transmission channel during operation, including normal transmission, transmission delay, and signal interruption. For example, during off-peak electricity hours at night, the data transmission channel in a certain area is in a normal state with smooth data transmission and no packet loss; while during peak electricity hours, signal instability and transmission delay may occur due to power fluctuations. The status performance index refers to specific numerical indicators for quantitatively evaluating the channel operating status, used to reflect the channel's operating quality and performance level. Data such as the transmission rate, signal attenuation, and packet loss rate of a data transmission channel at a certain moment are all state performance indicators. For example, if a channel has a transmission rate of 80Mbps, a signal attenuation of 15dB, and a packet loss rate of 2% during testing, these values ​​are the state performance indicators of that channel.

[0030] Furthermore, the analysis of the monitoring parameter standards corresponding to the performance threshold can be achieved through industrial standard mapping methods, such as establishing a packet loss rate and delay correlation matrix of the PRP / HSR redundant network based on the IEC 62439-3 standard to obtain the monitoring parameter standards; the location of the data transmission channel in the PLC IoT framework can be achieved through topology discovery protocols, such as using the LLDP link layer discovery protocol to scan the port connection relationships of industrial switches and combining it with the SNMPv3 protocol to obtain device topology information to obtain the data transmission channel; the acquisition of the channel operating status corresponding to the data transmission channel can be achieved through industrial bus diagnostic technology, such as using Wireshark to capture PROFIBUS-DP messages and parse the Diagnostic_Data field to obtain the channel operating status; the analysis of the status performance indicators corresponding to the channel operating status can be achieved through time series feature extraction algorithms, such as using the TSFRESH library to calculate the mean, variance, and autocorrelation characteristics of Modbus TCP communication traffic to obtain the status performance indicators; the detection of the channel performance data corresponding to the data transmission channel in the PLC IoT framework can be achieved through hardware-in-the-loop testing methods, such as using NI... The PXI platform injects EtherCAT frames and measures slave response jitter, then evaluates synchronization accuracy using the IEC 61784-2 standard to obtain channel performance data.

[0031] S3. Based on the channel performance data, calculate the resource conflict value of the fire protection equipment during deployment, and based on the resource conflict value, construct the deployment process corresponding to the fire protection resources in the PLC IoT framework.

[0032] Based on the channel performance data, this invention calculates the resource conflict value of the fire-fighting equipment during deployment, which can accurately quantify the resource competition between equipment, clearly understand the power line transmission resource carrying pressure, and avoid problems such as data transmission delay and command execution confusion caused by resource conflicts. It provides key data support for optimizing fire-fighting equipment deployment schemes and ensuring the efficient and stable operation of the system.

[0033] The resource conflict value refers to the quantitative value of the conflict caused by the competition for data transmission channel resources in the deployment of fire protection equipment. It integrates factors such as equipment data demand and channel transmission capacity, and reflects the contradiction of resource adaptation between equipment and channel. The higher the value, the greater the risk of data transmission congestion and command delay, which threatens the linkage efficiency of the fire protection system.

[0034] As an embodiment of the present invention, calculating the resource conflict value of the fire-fighting equipment during deployment based on the channel performance data includes: The resource conflict value of the fire-fighting equipment during deployment is calculated using the following formula:

[0035] in, This indicates the resource conflict value of the fire-fighting equipment during deployment. This indicates the total number of devices corresponding to the aforementioned fire-fighting equipment. This indicates the quantity index corresponding to the fire-fighting equipment. This indicates the total number of data transmission channels in the PLC IoT framework. The channel index represents the data transmission channel. This represents the association weight coefficient between the i-th fire-fighting equipment and the j-th data transmission channel. This represents the equipment data requirements for the i-th fire-fighting device. This represents the transmission capacity of the j-th data transmission channel. This represents the bandwidth allocation ratio of the i-th fire-fighting equipment on the j-th data transmission channel. This represents the transmission stability coefficient of the i-th fire-fighting equipment on the j-th data transmission channel. and These represent the start and end times of the statistical time interval corresponding to the channel performance data, respectively. This represents the operational quality function of the j-th data transmission channel when the i-th fire-fighting equipment is running at time t.

[0036] In detail, the correlation weight coefficient refers to the coefficient that measures the degree of correlation between the i-th fire-fighting equipment and the j-th data transmission channel. It is determined based on the equipment installation location, functional dependencies (such as strong correlation between the alarm host and the core channel), etc., and reflects the channel's support priority for equipment data transmission. The range is [0,1], and the larger the value, the stronger the correlation. The equipment data requirement refers to the amount of data that the i-th fire-fighting equipment needs to transmit per unit time (such as per second). It covers the scale of information such as fire alarm, equipment status monitoring (temperature / smoke sensor data), and linkage control commands. For video surveillance equipment, due to the transmission of images, Typically much larger than ordinary alarm buttons; the transmission capacity refers to the maximum data transmission rate (in bps) that the j-th data transmission channel can theoretically stably carry, which is determined by the channel's physical characteristics (power line bandwidth, modulation and demodulation capabilities), limiting the upper limit of fire data transmission, and is a key indicator for planning the number of connected devices and avoiding congestion; the bandwidth allocation ratio refers to the proportion allocated to the i-th fire equipment in the total bandwidth of the j-th channel, which is dynamically allocated according to the priority of fire services (fire alarm > status monitoring) to ensure priority transmission of data from key equipment (such as smoke detectors on the fire floor), with a range of [0,1], reflecting a resource tilting strategy; the transmission stability coefficient refers to the degree of stability of the i-th device when transmitting data in the j-th channel under the influence of interference (power noise, electromagnetic crosstalk), which is... Historical transmission error rate and packet loss rate are statistically calculated, ranging from [0,1]. Higher values ​​indicate more stable transmission, ensuring reliable delivery of fire commands. The statistical time interval refers to the time range for collecting and analyzing channel performance data, which needs to cover typical working scenarios of fire equipment (such as daily inspections, fire simulation drills, and real fire alarm responses) to ensure that resource conflict calculations reflect the risks throughout the entire process. The time accuracy (seconds / minutes) is set according to fire service requirements. The operational quality function describes the quality dynamic function of data transmission from the j-th channel to the i-th device at time t. It integrates indicators such as latency (time difference between data initiation and reception), error rate (percentage of erroneous data), and packet loss rate (percentage of lost data) to characterize the transmission service level of fire data for the channel during different time periods (peak / off-peak electricity consumption).

[0037] Based on the resource conflict value, this invention constructs the deployment process corresponding to fire protection resources in the PLC IoT framework. It can accurately identify the conflict risk of fire protection resources in channel transmission, provide a quantitative basis for resource allocation, optimize the compatibility between equipment and channels, avoid data transmission being affected by resource competition, ensure the linkage response efficiency of the fire protection system, and help build a more stable and reliable PLC IoT fire protection deployment system.

[0038] The fire protection resources refer to various elements related to data transmission, monitoring, and control functions in fire protection scenarios within the PLC IoT framework. These elements encompass hardware (such as sensors, controllers, and power transmission lines), software (data processing algorithms and communication protocols), and data (alarm signals and equipment status information). They form the foundation for the operation of fire protection systems. For example, in a shopping mall fire protection system, smoke sensors, power channels for transmitting fire data, and fire alarm linkage control programs all fall under the category of fire protection resources. The deployment process refers to a sequence of operational steps based on the resource deployment section, guiding the specific configuration of fire protection resources within the PLC IoT framework. For instance, the deployment process specifies that within the resource deployment section, repeaters should be installed to enhance signals first, then device communication time slots should be adjusted, and finally, a full-network integration test should be conducted.

[0039] As an embodiment of the present invention, the step of constructing the deployment process corresponding to fire protection resources in the PLC IoT framework based on the resource conflict value includes: determining the priority deployment area corresponding to the fire protection resources in the PLC IoT framework according to the resource conflict value; scanning the area conflict nodes in the priority deployment area; fitting the node fluctuation curve corresponding to the area conflict node; extracting the resource deployment segment in the node fluctuation curve; and constructing the deployment process corresponding to the fire protection resources in the PLC IoT framework based on the resource deployment segment.

[0040] The "priority deployment area" refers to the area where fire protection resources need to be prioritized based on resource conflict values. This is typically an area with high conflict values ​​and high fire risk. For example, a shopping mall's underground parking lot has a resource conflict value of 85 points (threshold 70), and is therefore designated as a priority deployment area, requiring priority installation of fire detectors and communication relay equipment. The "area conflict node" refers to the specific location within the priority deployment area where resource conflicts are concentrated, such as power line branches or intersections where multiple devices compete for the same channel. For instance, in a 15-story office building's low-voltage electrical shaft, three fire alarm control panels share a single power line carrier channel, causing severe signal interference; this low-voltage electrical shaft is considered a conflict node in the area. Conflict nodes; the node fluctuation curve refers to the curve describing the change of resource conflict value of regional conflict nodes in the time dimension, reflecting the dynamic characteristics of the conflict. For example, the node fluctuation curve of a factory's power distribution room shows that the conflict value rises from 60 to 90 during the peak production power consumption period from 18:00 to 20:00 every day, showing periodic fluctuations; the resource deployment segment refers to the time period or condition range extracted from the node fluctuation curve that is suitable for fire protection resource deployment. For example, the fluctuation curve shows that the conflict value is below 40 from 2:00 to 4:00 in the morning. This time period is extracted as the resource deployment segment, where equipment upgrades can be carried out without affecting normal communication.

[0041] Furthermore, determining the priority deployment area corresponding to fire protection resources in the PLC IoT framework can be achieved through a spatial risk heatmap algorithm, such as: using kernel density estimation to analyze the spatial distribution of historical fire alarm data, and combining it with the structural features of the BIM model to calculate risk weights, thereby obtaining the priority deployment area; scanning the regional conflict nodes in the priority deployment area can be achieved through industrial protocol conflict detection technology, such as: using the TIA Portal diagnostic tool to scan for IP address conflicts and duplicate device IDs in the PROFINET network, and using OPC... The UA server logs are analyzed to determine communication resource contention, thereby identifying conflicting nodes in the region. Fitting the node fluctuation curves corresponding to these conflicting nodes can be achieved using time series decomposition algorithms, such as applying the STL seasonal decomposition method to process fire equipment communication delay data, separating trend and periodic terms, and reconstructing the residual sequence to obtain the node fluctuation curves. Extracting resource deployment segments from the node fluctuation curves can be achieved using abrupt change detection algorithms, such as using the CUSUM control chart algorithm to identify step changes in communication quality indicators, and combining this with sliding window technology to divide stable intervals, thus obtaining resource deployment segments. Constructing the deployment process corresponding to fire resources within the PLC IoT framework can be achieved using an industrial automation orchestration engine, such as writing a function block diagram based on the CODESYS development environment for the IEC 61131-3 standard, integrating OPC UA Pub / Sub communication mode to implement device linkage logic, thereby obtaining the deployment process.

[0042] S4. Divide the deployment process into task blocks to obtain deployment task blocks, analyze the execution order of the deployment task blocks, and construct the execution time window of the deployment task blocks based on the execution order.

[0043] This invention divides the deployment process into task blocks to obtain deployment task blocks, which can break down the complex fire resource deployment work into clear and independent units, clarify the responsibilities of each link, avoid task confusion and omissions, optimize the resource allocation rhythm, improve execution efficiency, help the orderly advancement of fire resource deployment under the PLC IoT framework, and ensure deployment quality and system synergy.

[0044] The deployment task block refers to a task set formed by integrating tasks that can be executed collaboratively or have similar attributes in the task scheduling sequence. For example, in the deployment of fire-fighting equipment, "the installation and preliminary debugging of smoke detectors and heat detectors" can be integrated into a deployment task block to unify resource planning and execution.

[0045] As an embodiment of the present invention, the step of dividing the deployment process into task blocks to obtain deployment task blocks includes: parsing the execution dependencies of process nodes in the deployment process; dividing the deployment process into independent task units based on the execution dependencies; allocating concurrent execution priorities corresponding to the independent task units; generating a task scheduling sequence corresponding to the concurrent execution priorities; and dividing the deployment process into task blocks based on the task scheduling sequence to obtain deployment task blocks.

[0046] The execution dependency relationship refers to the sequential execution association between tasks in the deployment process due to constraints such as logic and resources. For example, in the deployment of fire protection equipment, the "installation of the fire alarm control panel" must be completed before the "pairing of the control panel with the smoke detectors" can be carried out. The former is a prerequisite for the latter, reflecting the dependency logic between tasks. The independent task unit refers to the smallest task granularity in the deployment process that can be carried out independently without relying on other tasks (except for necessary prerequisite tasks). For example, in the deployment of a fire protection system, the "installation of a temperature detector on a certain floor" can be executed independently after the basic wiring of that area is completed, without waiting for other floor tasks, and is an independent task unit. The concurrent execution priority refers to the execution priority level set for independent task units based on the criticality and urgency of the task to the deployment of the fire protection system. For example, the "debugging of smoke detectors" in high-risk fire areas has a higher priority than the "inspection of backup power supply" in low-risk areas, prioritizing the deployment progress of critical areas. The task scheduling sequence refers to the arrangement of task execution order by sorting independent task units according to concurrent execution priority. For example, in fire protection deployment, the "building of the core module of the fire alarm system" is scheduled first, followed by the "deployment of peripheral sensors" and "testing of linkage equipment" in sequence, guiding the task execution order.

[0047] Furthermore, the parsing of execution dependencies among process nodes in the deployment process can be achieved using a directed acyclic graph (DAG) analysis algorithm, such as using Graphviz to visualize the process topology and identifying the sequential constraints between nodes based on depth-first search, thereby obtaining the execution dependencies. The division of the deployment process into independent task units can be achieved using a modularity clustering algorithm, such as applying the Louvain community detection algorithm to segment the process network, dividing highly cohesive and loosely coupled node sets into independent units, thus obtaining independent task units. The allocation of concurrent execution priorities corresponding to the independent task units can be achieved using critical path evaluation methods, such as using P... ERT technology calculates the earliest and latest start times of each task unit and dynamically adjusts the parallel execution weights based on the time difference to obtain the concurrent execution priority. The generation of the task scheduling sequence corresponding to the concurrent execution priority can be achieved through a list scheduling algorithm, such as evaluating the communication overhead of the task processor based on the HEFT algorithm and eliminating communication delays on the critical path through task replication technology to obtain the task scheduling sequence. The task block division of the deployment process can be achieved through workflow pattern recognition technology, such as analyzing the control flow pattern using the BPMN 2.0 standard and automatically encapsulating the AND-split / join structure into parallelizable blocks to obtain deployment task blocks.

[0048] Furthermore, by analyzing the execution order of the deployment task blocks, this invention can clarify the logical connections and dependencies between tasks, avoid resource waste and deployment interruptions caused by task misordering, optimize resource utilization efficiency, ensure smooth progress of complex deployment work, reduce conflicts and interference between tasks, and provide strong support for building an efficient and stable PLC IoT fire protection deployment system.

[0049] The execution order refers to the sequence in which each deployment task block performs its work during the fire protection resource deployment process under the PLC IoT framework. It is determined based on the logical relationships, resource dependencies, and technical requirements between tasks. A reasonable execution order ensures efficient deployment and avoids resource conflicts and duplication of effort. For example, in fire protection deployment in a residential community, power line inspection (a basic task) must be completed first, followed by the installation of fire detectors and line connections, and finally, system integration testing must be conducted to ensure orderly deployment. Optionally, the analysis of the execution order corresponding to the deployment task blocks can be achieved using a topological sorting algorithm. For instance, based on a directed acyclic graph model, task dependencies can be constructed, and the Kahn algorithm can be used to remove nodes with zero in-degree to generate a linear sequence, thereby obtaining the execution order.

[0050] Furthermore, based on the execution order, the present invention constructs the execution time window corresponding to the deployment task block, which can deeply bind the time arrangement and logical order of the deployment task block, ensuring that each task starts and completes within a reasonable time period, thereby improving the time utilization rate of the deployment process. At the same time, it provides a time benchmark for monitoring task progress and coordinating resources from multiple parties, ensuring that the deployment of fire protection resources under the PLC IoT framework is carried out efficiently and on schedule.

[0051] The execution time window refers to the specific time range set for each deployment task block, including the start time, end time, and resource usage. It needs to take into account task dependencies, resource weights, and time period characteristics (such as less interference during off-peak electricity periods). For example, the execution time window for the "Fire Emergency Broadcast System Debugging" task is set to 23:00-24:00 at night. During this period, the power load is low and there is less environmental interference, which can ensure the stability of debugging data transmission.

[0052] As an embodiment of the present invention, constructing the execution time window corresponding to the deployment task block based on the execution order includes: extracting the task dependencies in the execution order; analyzing the task startup time slots corresponding to the deployment task block according to the task dependencies; dividing the time slices corresponding to the deployment task block based on the task startup time slots; mapping the resource execution weights corresponding to the time slices; and constructing the execution time window corresponding to the deployment task block according to the resource execution weights.

[0053] The task dependency relationship refers to the sequential constraints between deployed task blocks, including the triggering conditions of the completion of a preceding task for a subsequent task (e.g., "line detection" is a prerequisite for "equipment installation"). For example, in factory fire protection deployment, the "fire control panel connection and power-on" task cannot be started until the "power line load test" task is completed, thus forming a mandatory dependency relationship. The task start time slot refers to the earliest time point at which a subsequent task can start execution, determined by the task's planned or actual completion time. For example, if the "floor smoke detector wiring installation" task is planned to be completed at 10:00, then the start time slot for its subsequent task, "smoke detector power-on debugging," is 10:00, and it must wait for the preceding task to release channel resources before it can start. The time zone refers to dividing the overall deployment time into multiple consecutive dedicated time periods based on the task start time slots and estimated time consumption, with each zone corresponding to one or more time slots. For example, the morning period (9:00-12:00) of office building fire protection deployment is divided into the "basic line inspection" area, and the afternoon period (13:30-17:00) is divided into the "equipment installation and initial adjustment" area to ensure that similar tasks are executed in a concentrated manner. The resource execution weight refers to the priority of the deployment task block in occupying fire protection resources (such as power channel bandwidth, construction personnel, and testing equipment). The higher the value, the higher the priority of resource allocation. For example, the "fire alarm linkage test" task directly affects the effectiveness of the fire protection system, so its resource execution weight is higher than that of the "ordinary equipment status inspection" task, and communication channels and technical personnel should be allocated first.

[0054] Furthermore, the extraction of task dependencies in the execution order can be achieved using a directed graph parsing algorithm, such as: constructing a task node relationship graph using the NetworkX library, and traversing predecessor and successor nodes based on depth-first search to obtain task dependencies; the analysis of the task startup slots corresponding to the deployment task blocks can be achieved using a time automaton modeling method, such as: using the UPPAAL tool to establish a task timing constraint model, and calculating the earliest trigger time point through reachability analysis to obtain the task startup slots; the division of the time slices corresponding to the deployment task blocks can be achieved using a sliding window segmentation algorithm, such as: employing dynamic time warping techniques. The execution time sequence of tasks is aligned using K-means clustering to divide similar execution periods, thereby obtaining time slices. The resource execution weights corresponding to the time slices can be evaluated using an entropy weighting model, such as constructing a resource consumption feature matrix and calculating objective weight coefficients for dimensions such as CPU and memory using information entropy, thereby obtaining resource execution weights. The execution time window corresponding to the deployment task block can be constructed using constraint satisfaction solution methods, such as using the Optaplanner engine to set constraints such as task duration and resource capacity, and using a tabu search algorithm to generate feasible scheduling windows, thereby obtaining the execution time window.

[0055] S5. Monitor the window activity status corresponding to the execution time window, construct the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity status, identify the core nodes of the path corresponding to the deployment response path, and formulate an optimized fire protection deployment scheme under the PLC IoT framework based on the core nodes of the path.

[0056] This invention monitors the window activity status corresponding to the execution time window, enabling real-time monitoring of the deployment task progress and resource usage. It can promptly detect anomalies such as task delays and resource conflicts, quickly adjust task timing or reallocate resources, ensure the deployment process proceeds as planned, improve the controllability and efficiency of fire protection resource deployment, and guarantee the timeliness and stability of system construction under the PLC IoT framework.

[0057] The window activity status refers to the actual execution status of the deployed task blocks within the execution time window, including the status of task start, in progress, paused, completed, delayed, etc., as well as the real-time occupancy of resources (manpower, equipment, data channels). For example, in the deployment of a shopping mall fire protection system, the "fire control panel debugging" time window is originally scheduled from 14:00 to 16:00. If it has not started by 14:30, its window activity status is "delayed"; if it is paused due to channel resource conflicts during execution, the status changes to "paused". Optionally, the monitoring of the window activity status corresponding to the execution time window can be achieved by a time-series state machine modeling method, such as using the SCADE Suite tool to build a finite state machine model and using formal verification technology to track the window state transition in real time to obtain the window activity status.

[0058] Furthermore, based on the window activity state, the present invention constructs the deployment response path of the PLC IoT framework in fire protection deployment. It can quickly generate dynamic adjustment strategies for task execution anomalies (such as delays and conflicts), optimize the deployment process in real time, ensure smooth connection of each link in fire protection deployment, and improve the adaptability and deployment efficiency of the PLC IoT framework in complex scenarios.

[0059] The deployment response path refers to a dynamic handling link composed of key response nodes, which includes a complete process of data acquisition, analysis, and execution. For example, when the "Underground Garage Gas Fire Extinguishing System Debugging" window is interrupted due to power interference, the deployment response path is automatically activated: ① Data acquisition: Nearby signal strength sensors detect a sudden increase in channel bit error rate; ② Analysis and decision: Edge computing nodes call interference identification algorithms and determine that it is motor starting interference; ③ Execution adjustment: Switch to backup power line (node ​​A → node C), and at the same time adjust the debugging period to the off-peak electricity period at night to ensure that the debugging task is completed within the new time window.

[0060] As an embodiment of the present invention, the step of constructing the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity state includes: querying the window linkage identifier corresponding to the window activity state; parsing the fire protection response requirement corresponding to the window linkage identifier; dividing the dynamic deployment level corresponding to the PLC IoT framework based on the fire protection response requirement; extracting key response nodes in the dynamic deployment level; and constructing the deployment response path of the PLC IoT framework in fire protection deployment based on the key response nodes.

[0061] The window linkage identifier refers to a unique identifier that associates the execution time window with the fire deployment logic, used to record information such as dependencies and resource sharing rules between windows. For example, in the fire deployment of a shopping mall, the "Atrium Large Space Detector Installation" window and the "Fire Emergency Broadcast Debugging" window are bound together by the linkage identifier F-03-LINK, stipulating that the former must trigger the latter's channel resource reservation within 2 hours after completion to ensure that the broadcast system has exclusive access to the power line carrier channel during debugging; the fire response requirement refers to the targeted handling requirements generated based on the window activity status (such as delay, conflict), covering resource allocation, task priority adjustment, etc. For example, when the "Fire Pump Control Cabinet Networking" window is delayed due to insufficient channel bandwidth, the system generates a response requirement after parsing the linkage identifier: ① Increase the bandwidth allocation ratio of this task from 15% Increase to 30%; ② Suspend non-emergency "fire hydrant pressure monitoring" data uploads; ③ Notify maintenance personnel to bring relay equipment to the site to ensure priority access for critical equipment; The dynamic deployment hierarchy refers to a fire resource priority system based on real-time division of response needs, dividing deployment elements into a core layer, support layer, and auxiliary layer. For example, in fire protection deployment in industrial parks: Core layer: fire alarm controller, emergency start / stop button (requires a 99.9% communication success rate); Support layer: power line bridge, edge computing unit (responsible for data relay and preprocessing); Auxiliary layer: video surveillance camera, environmental temperature and humidity sensor (tolerable for short-term data delays).

[0062] When an anomaly occurs in the core layer window, the system automatically reduces the sampling frequency of the auxiliary layer devices to release resources. The key response node refers to the physical or logical unit in the dynamic deployment hierarchy that plays a decisive role in the efficiency or stability of fire protection deployment. For example, in a high-rise building fire protection system: physical node: power line carrier repeater in the weak current shaft of each floor, responsible for cross-floor data transmission; logical node: the task scheduling module of the fire control panel, which dynamically allocates communication time slots according to the window status; virtual node: the conflict prediction model in the cloud, which analyzes the channel load in real time and warns of potential congestion. When a repeater fails, the system quickly switches to the backup path through the key node to avoid affecting the transmission of alarm signals.

[0063] Furthermore, the querying of the window linkage identifier corresponding to the window activity status can be achieved through industrial protocol reverse parsing technology, such as using Wireshark to capture PROFINET real-time communication messages and parsing the window identifier field in the IRT synchronization domain to obtain the window linkage identifier; the parsing of the fire response requirements corresponding to the window linkage identifier can be achieved through semantic network reasoning methods, such as constructing a fire response knowledge graph based on the OWL ontology and matching the emergency plan clauses associated with the identifier through the SPARQL query engine to obtain the fire response requirements; the division of the dynamic deployment levels corresponding to the PLC IoT framework can be achieved through network topology layering algorithms, such as using the GN algorithm to divide the industrial switch connection graph into communities and constructing a hierarchical topology structure based on bandwidth and latency indicators to obtain the dynamic deployment levels; the extraction of key response nodes in the dynamic deployment levels can be achieved through graph theory centrality analysis methods, such as applying the PageRank algorithm to calculate the importance of network nodes and selecting the communication hub nodes with the top 20% betweenness centrality to obtain the key response nodes; the construction of the deployment response path of the PLC IoT framework in fire deployment can be achieved through time-sensitive routing planning methods, such as using IEEE The 802.1Qbv standard configuration uses a TSN traffic scheduling table, which employs the Yen algorithm to generate k shortest redundant paths, thereby obtaining the deployment response path.

[0064] This invention identifies the core nodes of the deployment response path, which can accurately locate the key elements that play a decisive role in fire protection deployment, clarify the key directions of resource allocation and fault handling, avoid response delays caused by interference from secondary links, and improve the stability and emergency handling capabilities of the PLC IoT framework in fire protection deployment.

[0065] The core node of the path refers to the key physical device or logical unit that plays a decisive role in the data transmission efficiency and task execution effect in the deployment response path. It is the core hub that ensures the smooth flow of the path and the normal realization of fire protection functions. For example, in the fire protection deployment response path of a large commercial complex, the main PLC controller in the fire control room is the core node, responsible for aggregating the data of fire detectors in each area and issuing linkage commands. Its stability directly affects the alarm and response efficiency of the entire system. Once this node fails, it may cause a global communication interruption. Optionally, the identification of the core node of the deployment response path can be achieved by network flow analysis algorithms, such as using the Ford-Fulkerson maximum flow algorithm to calculate the flow carrying capacity of each node in the path and filtering the transmission hubs with flow concentration exceeding the threshold, thereby obtaining the core node of the path.

[0066] Furthermore, based on the core nodes of the aforementioned path, this invention formulates a fire protection optimization deployment scheme under the PLC IoT framework. It can target key aspects affecting the effectiveness of the fire protection system and, by strengthening the hardware redundancy, communication priority, or algorithm optimization of the core nodes, ensure the real-time transmission of fire protection data and the accuracy of command execution, thus providing a scientific basis for building an efficient and stable PLC IoT fire protection system.

[0067] The fire protection optimization deployment scheme refers to a set of improvement strategies formulated based on path core node analysis, targeting the fire protection resource allocation and task flow under the PLC IoT framework, with the goal of improving system reliability and response efficiency. This scheme addresses potential conflicts and bottlenecks in deployment by optimizing core node performance, adjusting resource allocation priorities, and improving redundancy backup mechanisms. For example, in the fire protection system of a high-rise office building, communication repeaters distributed in the weak current shafts on each floor are used as core nodes. The optimization scheme may include adding backup power supplies to the repeaters and dynamically adjusting their data forwarding priorities to ensure real-time and stable transmission of fire alarm data, avoiding system paralysis due to single-point failures. Optionally, the formulation of the fire protection optimization deployment scheme under the PLC IoT framework can be achieved through a multi-objective genetic algorithm, such as using the NSGA-II algorithm to simultaneously optimize objectives such as equipment coverage, response time, and cost budget, and determining the optimal solution set through Pareto front analysis, thereby obtaining the fire protection optimization deployment scheme.

[0068] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0069] In one embodiment, a fire-fighting deployment device under a PLC IoT framework is provided, which corresponds one-to-one with the fire-fighting deployment method under the PLC IoT framework in the above embodiments. For example... Figure 3 As shown, the fire protection deployment device under this PLC IoT framework includes a rule-making module 201, a data detection module 202, a process construction module 203, a window construction module 204, and a scheme formulation module 205. Detailed descriptions of each functional module are as follows: The rule-making module 201 is used to acquire the fire-fighting equipment corresponding to the target area, analyze the collaborative linkage mode between the fire-fighting equipment, and formulate the early warning response rules corresponding to the collaborative linkage mode. The data detection module 202 is used to analyze the data transmission channels in the preset PLC IoT framework based on the early warning response rules, query the performance thresholds corresponding to the data transmission channels, and detect the channel performance data corresponding to the data transmission channels in the PLC IoT framework based on the performance thresholds. The process construction module 203 is used to calculate the resource conflict value of the fire-fighting equipment in the deployment based on the channel performance data, and to construct the deployment process corresponding to the fire-fighting resources in the PLC IoT framework based on the resource conflict value. The window construction module 204 is used to divide the deployment process into task blocks to obtain deployment task blocks, analyze the execution order corresponding to the deployment task blocks, and construct the execution time window corresponding to the deployment task blocks based on the execution order. The scheme formulation module 205 is used to monitor the window activity status corresponding to the execution time window, construct the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity status, identify the core nodes of the path corresponding to the deployment response path, and formulate the fire protection optimization deployment scheme under the PLC IoT framework based on the core nodes of the path.

[0070] Specific limitations regarding fire protection deployment devices within the PLC IoT framework can be found in the above section on front-end management methods, and will not be repeated here. Each module in the aforementioned fire protection deployment device within the PLC IoT framework can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the IoT platform, or stored in software within the IoT platform's memory, allowing the processor to invoke and execute the corresponding operations of each module.

[0071] In one embodiment, the rule-making module 201 executes the formulation of early warning response rules corresponding to the collaborative linkage mode, including: parsing the event triggering features in the collaborative linkage mode; based on the event triggering features, traversing a preset response strategy library to obtain an event response set; performing logical adaptation on the event response set to obtain event response logic; removing logical conflict items in the event response logic to obtain logical optimization relationships; and formulating early warning response rules corresponding to the collaborative linkage mode based on the logical optimization relationships.

[0072] In one embodiment, the data detection module 202 performs the following steps when detecting channel performance data corresponding to the data transmission channel in the PLC IoT framework based on the performance threshold: parsing the monitoring parameter standard corresponding to the performance threshold; locating the data transmission channel in the PLC IoT framework based on the monitoring parameter standard; obtaining the channel operating status corresponding to the data transmission channel; analyzing the status performance index corresponding to the channel operating status; and detecting the channel performance data corresponding to the data transmission channel in the PLC IoT framework based on the status performance index.

[0073] In one embodiment, the window construction module 203 executes a deployment process for fire protection resources in the PLC IoT framework based on the resource conflict value, including: determining the priority deployment area for fire protection resources in the PLC IoT framework according to the resource conflict value; scanning the area conflict nodes in the priority deployment area; fitting the node fluctuation curves corresponding to the area conflict nodes; extracting the resource deployment segments from the node fluctuation curves; and constructing the deployment process for fire protection resources in the PLC IoT framework based on the resource deployment segments.

[0074] In one embodiment, the scheme formulation module 204 constructs an execution time window corresponding to the deployment task block based on the execution order, including: extracting task dependencies in the execution order; analyzing the task startup time slots corresponding to the deployment task block according to the task dependencies; dividing the time slices corresponding to the deployment task block based on the task startup time slots; mapping the resource execution weights corresponding to the time slices; and constructing the execution time window corresponding to the deployment task block according to the resource execution weights.

[0075] In one embodiment, the scheme formulation module 205 performs the following steps when constructing the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity state: querying the window linkage identifier corresponding to the window activity state; parsing the fire response requirement corresponding to the window linkage identifier; dividing the dynamic deployment level corresponding to the PLC IoT framework based on the fire response requirement; extracting key response nodes in the dynamic deployment level; and constructing the deployment response path of the PLC IoT framework in fire protection deployment based on the key response nodes.

[0076] Specific limitations regarding fire protection deployment devices within the PLC IoT framework can be found in the above section on fire protection deployment methods within the PLC IoT framework, and will not be repeated here. Each module in the aforementioned fire protection deployment device within the PLC IoT framework can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the IoT platform, or stored in software within the memory of the IoT platform, allowing the processor to invoke and execute the corresponding operations of each module.

[0077] In one embodiment, an Internet of Things (IoT) platform is provided, which may be a server-side component, and its internal structure diagram may be as follows: Figure 4As shown, the IoT platform includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the IoT platform is used for communication with external clients via network connection. When the computer program is executed by the processor, it implements the functions or steps of a fire protection deployment method on the server side within a PLC IoT framework.

[0078] In one embodiment, an Internet of Things (IoT) platform is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Obtain the fire-fighting equipment corresponding to the target area, analyze the collaborative linkage mode between the fire-fighting equipment, and formulate early warning response rules corresponding to the collaborative linkage mode; Based on the aforementioned early warning response rules, the data transmission channels in the preset PLC IoT framework are analyzed, the performance thresholds corresponding to the data transmission channels are queried, and based on the performance thresholds, the channel performance data corresponding to the data transmission channels in the PLC IoT framework is detected. Based on the channel performance data, the resource conflict value of the fire-fighting equipment during deployment is calculated, and based on the resource conflict value, the deployment process corresponding to the fire-fighting resources in the PLC IoT framework is constructed. The deployment process is divided into task blocks to obtain deployment task blocks. The execution order of the deployment task blocks is analyzed, and the execution time window corresponding to the deployment task blocks is constructed based on the execution order. Monitor the window activity status corresponding to the execution time window, construct the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity status, identify the core nodes of the path corresponding to the deployment response path, and formulate an optimized fire protection deployment scheme under the PLC IoT framework based on the core nodes of the path.

[0079] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or the Internet of Things platform described above can be referred to the relevant descriptions on the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0080] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0081] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0082] The above-described embodiments are merely illustrative of the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention. It should be noted that if any software tools or components not belonging to this company appear in the embodiments of this application, they are merely illustrative examples and do not represent actual use.

Claims

1. A fire protection deployment method under a PLC IoT framework, characterized in that, include: Obtain the fire-fighting equipment corresponding to the target area, analyze the collaborative linkage mode between the fire-fighting equipment, and formulate early warning response rules corresponding to the collaborative linkage mode; Based on the aforementioned early warning response rules, the data transmission channels in the preset PLC IoT framework are analyzed, the performance thresholds corresponding to the data transmission channels are queried, and based on the performance thresholds, the channel performance data corresponding to the data transmission channels in the PLC IoT framework is detected. Based on the channel performance data, the resource conflict value of the fire-fighting equipment during deployment is calculated, and based on the resource conflict value, the deployment process corresponding to the fire-fighting resources in the PLC IoT framework is constructed. The deployment process is divided into task blocks to obtain deployment task blocks. The execution order of the deployment task blocks is analyzed, and the execution time window corresponding to the deployment task blocks is constructed based on the execution order. Monitor the window activity status corresponding to the execution time window, construct the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity status, identify the core nodes of the path corresponding to the deployment response path, and formulate an optimized fire protection deployment scheme under the PLC IoT framework based on the core nodes of the path.

2. The fire protection deployment method under the PLC IoT framework as described in claim 1, characterized in that, The formulation of early warning response rules corresponding to the collaborative linkage mode includes: Analyze the event triggering characteristics in the aforementioned collaborative linkage mode; Based on the event triggering characteristics, the preset response strategy library is traversed to obtain the event response set; The event response set is logically adapted to obtain the event response logic; Remove the logically conflicting items from the event response logic to obtain the logically optimized relationship; Based on the aforementioned logical optimization relationship, early warning response rules corresponding to the collaborative linkage mode are formulated.

3. The fire protection deployment method under the PLC IoT framework as described in claim 1, characterized in that, The step of detecting channel performance data corresponding to the data transmission channel in the PLC IoT framework based on the performance threshold includes: Analyze the monitoring parameter standards corresponding to the performance threshold; Based on the monitoring parameter standards, locate the data transmission channel in the PLC IoT framework; Obtain the channel operating status corresponding to the data transmission channel; Analyze the status performance indicators corresponding to the channel's operating status; Based on the aforementioned status performance indicators, the channel performance data corresponding to the data transmission channel in the PLC IoT framework is detected.

4. The fire protection deployment method under the PLC IoT framework as described in claim 1, characterized in that, The calculation of the resource conflict value of the fire-fighting equipment during deployment based on the channel performance data includes: The resource conflict value of the fire-fighting equipment during deployment is calculated using the following formula: in, This indicates the resource conflict value of the fire-fighting equipment during deployment. This indicates the total number of devices corresponding to the aforementioned fire-fighting equipment. This indicates the quantity index corresponding to the fire-fighting equipment. This indicates the total number of data transmission channels in the PLC IoT framework. The channel index represents the data transmission channel. This represents the association weight coefficient between the i-th fire-fighting equipment and the j-th data transmission channel. This represents the equipment data requirements for the i-th fire-fighting device. This represents the transmission capacity of the j-th data transmission channel. This represents the bandwidth allocation ratio of the i-th fire-fighting equipment on the j-th data transmission channel. This represents the transmission stability coefficient of the i-th fire-fighting equipment on the j-th data transmission channel. and These represent the start and end times of the statistical time interval corresponding to the channel performance data, respectively. This represents the operational quality function of the j-th data transmission channel when the i-th fire-fighting equipment is running at time t.

5. The fire protection deployment method under the PLC IoT framework as described in claim 1, characterized in that, The deployment process for fire protection resources in the PLC IoT framework, based on the resource conflict value, includes: Based on the resource conflict value, the priority deployment area corresponding to the fire protection resources in the PLC IoT framework is determined; Scan for conflicting nodes in the priority deployment area; Fit the node fluctuation curves corresponding to the conflict nodes in the region; Extract the resource deployment segment from the node fluctuation curve; Based on the resource deployment section, the deployment process corresponding to the fire protection resources in the PLC IoT framework is constructed.

6. The fire protection deployment method under the PLC IoT framework as described in any one of claims 1, characterized in that, The step of dividing the deployment process into task blocks to obtain deployment task blocks includes: Analyze the execution dependencies of process nodes in the deployment process; Based on the execution dependencies, the deployment process is divided into independent task units; Assign concurrent execution priorities to the independent task units; Generate the task scheduling sequence corresponding to the concurrent execution priority; Based on the task scheduling sequence, the deployment process is divided into task blocks to obtain deployment task blocks.

7. The fire protection deployment method under the PLC IoT framework as described in any one of claims 1, characterized in that, The step of constructing the execution time window corresponding to the deployment task block based on the execution order includes: Extract the task dependencies in the execution order; Based on the task dependencies, analyze the task startup time slots corresponding to the deployment task blocks; Based on the task start time slot, divide the time slice corresponding to the deployment task block; Map the resource execution weights corresponding to the time slices; Based on the resource execution weights, construct the execution time window corresponding to the deployment task block.

8. The fire protection deployment method under the PLC IoT framework as described in claim 1, characterized in that, The step of constructing the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity state includes: Query the window linkage identifier corresponding to the window's activity status; Analyze the fire response requirements corresponding to the window linkage identifier; Based on the fire response requirements, the dynamic deployment levels corresponding to the PLC IoT framework are defined. Extract the key response nodes in the dynamic deployment hierarchy; Based on the key response nodes, the deployment response path of the PLC IoT framework in fire protection deployment is constructed.

9. A fire-fighting deployment device under a PLC IoT framework, characterized in that, include: The rule-making module is used to acquire the fire-fighting equipment corresponding to the target area, analyze the collaborative linkage mode between the fire-fighting equipment, and formulate the early warning response rules corresponding to the collaborative linkage mode. The data detection module is used to analyze the data transmission channels in the preset PLC IoT framework based on the early warning response rules, query the performance thresholds corresponding to the data transmission channels, and detect the channel performance data corresponding to the data transmission channels in the PLC IoT framework based on the performance thresholds. The process construction module is used to calculate the resource conflict value of the fire-fighting equipment in the deployment based on the channel performance data, and to construct the deployment process corresponding to the fire-fighting resources in the PLC IoT framework based on the resource conflict value. The window construction module is used to divide the deployment process into task blocks to obtain deployment task blocks, analyze the execution order corresponding to the deployment task blocks, and construct the execution time window corresponding to the deployment task blocks based on the execution order. The scheme formulation module is used to monitor the window activity status corresponding to the execution time window, construct the deployment response path of the PLC IoT framework in fire protection deployment based on the window activity status, identify the core nodes of the deployment response path, and formulate the fire protection optimization deployment scheme under the PLC IoT framework based on the core nodes of the path.

10. An Internet of Things (IoT) platform, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the fire protection deployment method under the PLC IoT framework as described in any one of claims 1 to 8.

Images