A robot fire patrol inspection early warning supervision system

Abstract

The application discloses a kind of robot fire-fighting inspection early warning supervision systems, the present application relates to fire-fighting inspection technical field, including fire risk quantization early warning decision module and hierarchical response and resource scheduling module, fire risk quantization early warning decision module includes inspection risk unit, risk diffusion unit and global early warning response unit.The application exports single-point inspection risk comprehensive index by inspection risk unit, constructs quantization model by fusing multidimensional parameters, solves the one-sidedness of traditional single parameter judgment, exports regional risk diffusion coefficient by risk diffusion unit, judges diffusion trend in combination with space environment parameters, solves the situation distortion problem of regional risk assessment, exports global early warning response grade by global early warning response unit, integrates multidimensional decision parameters to construct hierarchical model, realizes early warning and risk, emergency capability accurate match, promotes fire-fighting supervision to change to active prevention.

Description

Technical Field

[0001] This invention relates to the field of fire inspection technology, specifically a robotic fire inspection, early warning, and monitoring system. Background Technology

[0002] Fire inspection is a core component of ensuring fire safety in industrial production and civil buildings. Its core objective is to identify fire hazards in a timely manner, predict the spread of risks, and trigger precise emergency responses. With the development of intelligent manufacturing and Internet of Things technologies, robotic fire inspection systems are gradually replacing traditional manual inspections and becoming an important means of fire safety supervision.

[0003] Existing single-point risk assessments may rely on a single or a few monitoring parameters, such as triggering an early warning based solely on a temperature threshold, without integrating multi-dimensional data such as equipment operating status (aging degree and heat accumulation effect), environmental parameters (humidity and dust accumulation), and multi-gas concentrations. This may result in insufficient ability to identify potential hazards caused by multiple factors, making it easy to miss or misjudge, and failing to accurately locate the core risk at a single point.

[0004] Existing regional risk assessments may rely on simple summation or averaging of single-point risk values, without considering key factors affecting diffusion such as risk transmission characteristics, regional combustible material distribution, and ventilation conditions. This makes it impossible to scientifically predict the trend and scope of risk spreading from a single point to the surrounding areas, resulting in distorted regional risk situation assessments and making it difficult to achieve precise deployment of pre-disaster prevention.

[0005] Existing global early warning systems may rely heavily on experience to set fixed thresholds, without linking them to dynamic parameters such as emergency resource capabilities (e.g., the availability of fire-fighting facilities and the difficulty of personnel response) and regional spread trends. This leads to a mismatch between early warning levels and actual emergency needs, which can result in excessive resource allocation leading to waste or delayed responses that miss opportunities for action. Consequently, it is impossible to achieve precise linkage between early warning and resource allocation. Summary of the Invention

[0006] The purpose of this invention is to provide a robotic fire inspection and early warning monitoring system, which solves the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a robotic fire inspection and early warning monitoring system, including a fire risk quantification and early warning decision-making module and a graded response and resource scheduling module;

[0008] The fire risk quantification and early warning decision-making module is used to receive pre-processed inspection risk data, risk diffusion data, and early warning response correlation data.

[0009] The fire risk quantification and early warning decision-making module includes:

[0010] Inspection risk units, risk diffusion units, and global early warning response units;

[0011] The inspection risk unit is based on the real-time equipment temperature, heat accumulation risk coefficient and multi-gas risk contribution factor in the inspection risk data, and performs weighted processing to output a single-point inspection risk comprehensive index.

[0012] The risk diffusion unit, based on the risk impact area, risk transmission coefficient, combustible density coefficient, and ventilation diffusion acceleration ratio of the i-th inspection point in the risk diffusion data, introduces a single-point inspection risk comprehensive index according to the inspection point, and performs weighted processing to output the regional risk diffusion coefficient.

[0013] The global early warning response unit outputs the global early warning response level based on the video surveillance coverage, fire-fighting facility integrity rate, and personnel emergency response difficulty coefficient in the early warning response associated data, and introduces the single-point maximum risk comprehensive index and regional risk diffusion coefficient in the region. Combined with weighted processing, the global early warning response level is output.

[0014] The hierarchical response and resource scheduling module:

[0015] It is used to receive the comprehensive risk index of single-point inspection, the regional risk diffusion coefficient, and the global early warning response level, so as to realize the implementation of robot fire inspection, early warning and supervision.

[0016] Optionally, the processing procedure for the inspection risk unit is as follows:

[0017] A1. The impact of equipment operating temperature on fire safety is considered by combining the real-time temperature of the equipment, the normal operating temperature of the equipment, and the maximum allowable temperature of the equipment.

[0018] A2. By considering the relationship between the continuous operating time of the equipment and fire safety, the degree of amplification of fire risk due to heat accumulation after continuous operation of the equipment is reflected, so as to calculate the heat accumulation risk coefficient.

[0019] A3. By combining the real-time concentrations of carbon monoxide, methane, and hydrogen sulfide, the contribution of the concentrations of various combustible and toxic gases at the inspection point to the fire risk is comprehensively reflected, so as to calculate the multi-gas risk contribution factor.

[0020] A4. By combining the equipment's operating time with its designed service life, the aging status of the equipment can be reflected, and the equipment aging coefficient can be calculated.

[0021] A5. Calculate the relative humidity of the environment by analyzing the relationship between the relative humidity of the environment around the inspection point and the short-circuit risk of electrical equipment;

[0022] A6. By considering the real-time dust concentration at the inspection point, the impact of environmental dust on fire risk is quantified, thereby calculating the environmental dust concentration coefficient. The parameters output in the above steps are then weighted to output a comprehensive risk index for single-point inspection.

[0023] Optionally, the processing procedure of the risk diffusion unit is as follows:

[0024] B1. By precisely dividing the inspection order, the comprehensive risk index of the single-point inspection of the i-th inspection point is introduced into this risk diffusion unit.

[0025] B2. Calculate the risk impact area of ​​the i-th inspection point by analyzing the risk diffusion coverage of the i-th inspection point.

[0026] B3. By analyzing the combustible materials around the equipment, we can analyze the ease with which the risk of the i-th inspection point is transmitted to the surrounding area, thereby calculating the risk transmission coefficient of the i-th inspection point, and summing the parameters calculated in the above steps.

[0027] B4. Calculate the combustible density coefficient of the i-th inspection point by analyzing the impact of the density of combustibles around the inspection point on risk diffusion.

[0028] B5. By combining the real-time wind speed of the j-th vent, the effective area of ​​the j-th vent, the wind direction correction coefficient, and the minimum ventilation volume threshold of the area, the influence of the area ventilation conditions on the smoke diffusion speed is reflected, so as to output the ventilation diffusion acceleration ratio, and finally output the area risk diffusion coefficient.

[0029] Optionally, the processing procedure of the global early warning response unit is as follows:

[0030] C1. Introduce the maximum risk comprehensive index of a single point within the region into the global early warning response unit to assess the highest single-point inspection risk comprehensive index among all inspection points in the current assessment region. By introducing the regional risk diffusion coefficient into the global early warning response unit, the overall diffusion status of risk within the region can be reflected.

[0031] C2. By analyzing the coverage ratio of video surveillance in the area, the video surveillance coverage rate is calculated to reflect the risk visualization monitoring capability, thereby correcting the early warning response level.

[0032] C3. By analyzing the proportion of intact fire protection facilities in the area, the hardware capabilities for risk management in the area are reflected, and the integrity rate of fire protection facilities is calculated.

[0033] C4. By combining relative personnel density, regional personnel fire safety training completion rate, average response time of the region's most recent emergency drill, and average response time of emergency drills in the same industry, the difficulty level of personnel emergency response in the region is reflected, and the personnel emergency response difficulty coefficient is calculated to finally output the global early warning response level.

[0034] Optionally, the hierarchical response and resource scheduling module includes a hierarchical early warning response unit, a robot inspection and scheduling unit, and a fire resource linkage unit.

[0035] Optionally, the tiered early warning response unit specifically comprises:

[0036] When 0 ≤ Global Early Warning Response Level < 2, it indicates a low early warning level. At this time, the normal operation status is maintained, and only daily safety inspection logs are generated and pushed to the enterprise safety management department for archiving.

[0037] When 2 ≤ Global Early Warning Response Level < 4, it indicates a medium early warning level. At this time, a risk attention warning notification is pushed to the operation and maintenance personnel, along with the location markers of high-risk single points and medium-risk diffusion areas, and video AI real-time monitoring of key areas is initiated.

[0038] When 4 ≤ Global Early Warning Response Level < 6, it indicates a high early warning level. At this time, the internal fire alarm of the enterprise is triggered, the emergency preparedness warning is pushed to the emergency command center, and high diffusion risk areas are marked at the same time, and the pre-linkage mode of fire protection facilities is activated.

[0039] When the global early warning response level is ≥6, it indicates an emergency early warning level. At this time, an external fire alarm is triggered, an emergency response warning is pushed to all relevant personnel, and the whole area fire linkage mechanism is activated.

[0040] Optionally, the robot inspection scheduling unit dynamically adjusts the inspection strategy by combining the single-point inspection risk comprehensive index and the regional risk diffusion coefficient, specifically as follows:

[0041] Low warning level: The robot inspects according to the preset routine path, with an inspection frequency of once every 2 hours. For single points with a comprehensive risk index of <0.5, the frequency can be appropriately extended to once every 4 hours.

[0042] Medium-level warning: For single points with a comprehensive risk index of >2, the inspection frequency is increased to once every 30 minutes; for areas with a regional risk diffusion coefficient of >1.2, the inspection coverage density is increased.

[0043] High alert level: Robots are given priority for inspection. In high-risk areas with a risk diffusion coefficient > 1.8, the inspection frequency is increased to once every 15 minutes. For single points with a comprehensive risk index > 3, a 24-hour continuous monitoring mode is activated.

[0044] Emergency Warning Level: The robot immediately proceeds to the single point with the highest comprehensive risk index for inspection, continuously collects and transmits real-time data, while avoiding dangerous paths in high-risk areas and guiding emergency personnel to the scene safely.

[0045] Optionally, the fire resource linkage unit combines the single-point inspection risk comprehensive index and the regional risk diffusion coefficient to control fire protection facilities in a coordinated manner, specifically as follows:

[0046] Low alert level: Fire protection facilities remain in normal standby status and undergo regular automatic self-inspections;

[0047] Medium-level warning: For areas with a risk diffusion coefficient > 1.2, pre-start the ventilation system, put the fire pumps into standby mode, and push the location information of fire extinguishers and fire hydrants to the maintenance personnel's terminal simultaneously;

[0048] High alert level: For areas with a risk diffusion coefficient > 1.8, the ventilation system, sprinkler system and gas extinguishing device will be automatically activated, the fire pump will be activated in pressurization mode, and the emergency lighting system will be automatically turned on.

[0049] Emergency warning level: Automatically activate sprinkler systems or gas extinguishing devices in high-risk areas, close ventilation openings in the area, and activate fire broadcasts to guide personnel evacuation.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] I. This invention outputs a comprehensive risk index for single-point inspections through an inspection risk unit. By integrating temperature deviation to reflect the degree of abnormal equipment heating, heat accumulation coefficient to reflect the risk accumulation effect of continuous equipment operation, multi-gas risk contribution factors to integrate the comprehensive influence of combustible gas and toxic gas concentrations, equipment aging coefficient to correlate equipment service life with actual operating time, humidity correction coefficient to reflect the inhibitory or promoting effect of environmental humidity on risk, and dust correction coefficient to reflect the impact of dust accumulation on equipment heat dissipation and fire hazards, etc., a unified risk quantification model is constructed to achieve a comprehensive and accurate assessment of single-point inspection risks. This completely solves the one-sided problem of traditional single-parameter threshold judgment and provides standardized and superimposed basic data support for subsequent regional risk assessment.

[0052] Second, this invention outputs a regional risk diffusion coefficient through a risk diffusion unit. Based on the comprehensive risk index of a single-point inspection, it combines the risk impact area with the spatial coverage of the single-point risk, the risk transmission coefficient, and reflects the risk's ability to be transmitted between different media, the combustible density coefficient, the distribution and load level of combustibles in the area, the ventilation diffusion acceleration ratio, and the promoting effect of environmental ventilation conditions on risk diffusion. Through weighted calculation, it achieves a scientific prediction of the risk diffusion trend in the area, clearly presenting the possibility and scope of risk spreading from a single point to the surrounding area. This solves the problem of situational distortion caused by simply superimposing single-point risk values ​​in traditional regional risk assessments, and provides a precise basis for the forward deployment of regional fire protection resources.

[0053] Third, this invention outputs a global early warning response level through a global early warning response unit, integrates the maximum value of the comprehensive risk index of single-point inspections, reflects the most serious single-point hidden dangers within the system, the regional risk diffusion coefficient, reflects the overall risk diffusion trend, video surveillance coverage, reflects the timeliness and comprehensiveness of risk monitoring, the integrity rate of fire protection facilities, and is used to correlate emergency response capabilities, personnel emergency response difficulty coefficient, and the complexity of personnel evacuation and rescue, etc., to construct a scientific early warning classification model, achieves accurate matching between the global early warning level and the actual risk situation and emergency resource capabilities, solves the subjectivity problem of traditional experience-based early warning classification, and provides clear decision-making basis for robot inspection scheduling, fire protection facility linkage, personnel response deployment, etc., promoting the transformation of fire supervision from passive response to proactive prevention. Attached Figure Description

[0054] Figure 1 This is a block diagram of the system modules of the present invention;

[0055] Figure 2 This is a system flowchart of the fire risk quantification, early warning, decision-making, and hierarchical response and resource scheduling module of the present invention;

[0056] Figure 3 This is a schematic diagram of the operation process of the fire risk quantification and early warning decision-making module of the present invention. Detailed Implementation

[0057] 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 embodiments of the present invention, and not all embodiments. 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.

[0058] Please see Figures 1 to 3This embodiment provides a robot fire inspection and early warning monitoring system, including a multi-source heterogeneous data acquisition module, an inspection data transfer and processing module, a fire risk quantification and early warning decision-making module, and a hierarchical response and resource scheduling module;

[0059] Among them:

[0060] Multi-source heterogeneous data acquisition module:

[0061] This is used to acquire inspection risk data, risk diffusion data, and early warning response correlation data during robot fire inspections, and input the acquired data into the inspection data transfer and processing module;

[0062] Inspection data transfer and processing module:

[0063] It receives inspection risk data, risk diffusion data, and early warning response related data, preprocesses the acquired data, and then inputs it into the fire risk quantification and early warning decision module.

[0064] Further:

[0065] The fire risk quantification and early warning decision-making module includes an inspection risk unit, a risk diffusion unit, and a global early warning response unit.

[0066] The inspection risk unit is based on the real-time equipment temperature, heat accumulation risk coefficient and multi-gas risk contribution factor in the inspection risk data, and performs weighted processing to output a single-point inspection risk comprehensive index.

[0067] The risk diffusion unit is based on the risk impact area, risk transmission coefficient, combustible density coefficient and ventilation diffusion acceleration ratio of the i-th inspection point in the risk diffusion data. It also introduces the single-point inspection risk comprehensive index according to the inspection point and performs weighting processing to output the regional risk diffusion coefficient.

[0068] The global early warning response unit outputs the global early warning response level based on the video surveillance coverage, fire-fighting facility integrity rate, and personnel emergency response difficulty coefficient in the early warning response related data, and introduces the single-point maximum risk comprehensive index and regional risk diffusion coefficient in the region. Combined with weighted processing, the global early warning response level is output.

[0069] Further:

[0070] Tiered response and resource scheduling module:

[0071] It is used to receive the comprehensive risk index of single-point inspection, the regional risk diffusion coefficient and the global early warning response level, so as to realize the implementation of robot fire inspection and early warning supervision;

[0072] The tiered response and resource scheduling module includes:

[0073] Tiered early warning and response unit, robot inspection and dispatch unit, and fire resource linkage unit;

[0074] Firstly, the specific implementation method of the tiered early warning response unit is as follows:

[0075] When 0 ≤ Global Early Warning Response Level < 2, it indicates a low early warning level. At this time, the normal operation status is maintained, and only daily safety inspection logs are generated and pushed to the enterprise safety management department for archiving.

[0076] When 2 ≤ Global Early Warning Response Level MS < 4, it indicates a medium early warning level. At this time, a risk attention warning notification is pushed to the operation and maintenance personnel, along with the location markers of high-risk single points (single point inspection risk comprehensive index > 2) and medium diffusion risk areas (area risk diffusion coefficient > 1.2), and video AI real-time monitoring of key areas is initiated.

[0077] When 4 ≤ Global Early Warning Response Level MS < 6, it indicates a high early warning level. At this time, the internal fire alarm of the enterprise is triggered, the emergency preparedness warning is pushed to the emergency command center, and high diffusion risk areas are marked at the same time (area risk diffusion coefficient > 1.8), and the pre-linkage mode of fire protection facilities is activated.

[0078] When the global early warning response level MS≥6, it indicates an emergency early warning level. At this time, an external fire alarm is triggered (linking with the local fire department), an emergency response warning is pushed to all relevant personnel, and the whole area fire linkage mechanism is activated.

[0079] Secondly: The specific implementation method of the robot inspection and scheduling unit is as follows:

[0080] By combining the comprehensive risk index of single-point inspections and the regional risk diffusion coefficient, the inspection strategy is dynamically adjusted, specifically as follows:

[0081] Low warning level: The robot inspects according to the preset routine path, with an inspection frequency of once every 2 hours. For single points with a comprehensive risk index of <0.5, the frequency can be appropriately extended to once every 4 hours.

[0082] Medium-level warning: For single points with a comprehensive risk index of >2, the inspection frequency is increased to once every 30 minutes. For areas with a risk diffusion coefficient of >1.2, the inspection coverage density is increased, and 3 auxiliary inspection points can be added on the original route.

[0083] High alert level: Robots are given priority for inspection. In high-risk areas with a risk diffusion coefficient > 1.8, the inspection frequency is increased to once every 15 minutes. For single points with a comprehensive risk index > 3, a 24-hour continuous monitoring mode is activated. Robots can stay at fixed points and collect data once every 5 minutes.

[0084] Emergency Warning Level: The robot immediately proceeds to the single point with the highest comprehensive risk index for inspection, continuously collects and transmits real-time data, while avoiding dangerous paths in high-risk areas and guiding emergency personnel to the scene safely.

[0085] Thirdly: The specific implementation method of the fire protection resource linkage unit is as follows:

[0086] By combining the comprehensive risk index of single-point inspections and the regional risk diffusion coefficient, fire protection facilities are controlled in a coordinated manner, specifically as follows:

[0087] Low alert level: Fire protection facilities are kept in normal standby mode and automatically self-checked regularly, i.e., once a day;

[0088] Medium warning level: For areas with a risk diffusion coefficient > 1.2, pre-start the ventilation system, i.e., maintain low wind speed operation, put the fire pump into standby status, and push the location information of fire extinguishers and fire hydrants to the operation and maintenance personnel's terminal simultaneously;

[0089] High alert level: For areas with a risk diffusion coefficient > 1.8, the ventilation system will be automatically activated at maximum wind speed, the sprinkler system and gas extinguishing device will enter pre-start state, the fire pump will start pressurization mode, and the emergency lighting system will be automatically turned on.

[0090] Emergency warning level: Automatically activates sprinkler systems or gas extinguishing devices in high-risk areas, closes ventilation openings in the area to prevent the fire from spreading, activates fire broadcasts to guide personnel evacuation, and can be linked to elevator emergency landing and fireproof roller shutter closure;

[0091] It should also be noted that this robot fire inspection and early warning monitoring system also has a central control system, which in turn has a remote communication control terminal. The remote communication control terminal is used to connect the robot body and the central control system for data communication and monitoring. The remote communication control terminal supports the collaborative monitoring function of multiple robots and can be used to simultaneously connect several robot bodies to realize this robot fire inspection and early warning monitoring system.

[0092] Based on the above, the three units of this system together form a complete risk assessment closed loop of single-point perception, regional diffusion and global decision-making, realizing full-level risk quantification from micro-point to macro-global, solving the problems of scattered monitoring, delayed decision-making and passive response in traditional fire inspection, and building a proactive and intelligent fire early warning and supervision system.

[0093] By performing hierarchical calculations from single points to the global level, false alarms and missed alarms are avoided, and the early warning results are more in line with the actual risk situation.

[0094] It provides enterprises with comprehensive risk quantification data for safety management, shifting the focus from post-event response to pre-event prevention, and improving the overall level of fire safety management.

[0095] refer to Figure 1 , Figure 2 as well as Figure 3 The inspection risk unit is:

[0096] A1. The impact of equipment operating temperature on fire safety is considered by combining the real-time temperature of the equipment, the normal operating temperature of the equipment, and the maximum allowable temperature of the equipment.

[0097] A2. By considering the relationship between the continuous operating time of the equipment and fire safety, the degree of amplification of fire risk due to heat accumulation after continuous operation of the equipment is reflected, so as to calculate the heat accumulation risk coefficient.

[0098] A3. By combining the real-time concentrations of carbon monoxide, methane, and hydrogen sulfide, the contribution of the concentrations of various combustible and toxic gases at the inspection point to the fire risk is comprehensively reflected, so as to calculate the multi-gas risk contribution factor.

[0099] A4. By combining the equipment's operating time with its designed service life, the aging status of the equipment can be reflected, and the equipment aging coefficient can be calculated.

[0100] A5. Calculate the relative humidity of the environment by analyzing the relationship between the relative humidity of the environment around the inspection point and the short-circuit risk of electrical equipment;

[0101] A6. By considering the real-time dust concentration at the inspection point, the impact of environmental dust on fire risk is quantified, thereby calculating the environmental dust concentration coefficient. The parameters output in the above steps are weighted to output a comprehensive risk index for single-point inspection.

[0102] The calculation formula for the inspection risk unit is as follows:

[0103] ;

[0104] in:

[0105] RA is a comprehensive risk index for single-point inspections;

[0106] RAA stands for Real-Time Equipment Temperature, which is the real-time temperature value of the surface or interior of the equipment at the inspection point. It can be obtained by an infrared thermal imaging sensor mounted on the robot. The sensor can be installed at the front end of the robot's inspection arm, 0.5-1.5 meters away from the equipment surface, to perform non-contact temperature measurement and directly output the temperature value in °C. The introduction of RAA reflects the current thermal state of the equipment and is the core basic data for judging whether the equipment is overheating and whether there is a fire hazard.

[0107] RAB is the normal operating temperature of the equipment, that is, the stable operating temperature of the equipment under rated operating conditions. It can be obtained directly from the equipment's technical manual or calculated by averaging the temperature monitoring data of the robot under normal operating conditions for 72 consecutive hours. The normal operating temperature RAB of this equipment is used as the benchmark value for judging temperature anomalies, distinguishing between normal temperature fluctuations and abnormal overheating conditions.

[0108] RAC is the maximum allowable temperature of the equipment, which is the highest temperature threshold of the equipment within its safe operating range. Exceeding this temperature poses a risk of fire or damage to the equipment. It can be obtained from the equipment manufacturer's technical manual or fire protection industry standards. The maximum allowable temperature RAC of this equipment is used to define the boundary of the equipment's temperature safety and to provide a reference upper limit for the normalization calculation of temperature deviation.

[0109] RAD stands for Heat Accumulation Risk Factor, which reflects the amplification of fire risk due to heat accumulation after continuous equipment operation. Its value ranges from 0 to 1; the longer the operating time, the closer the factor is to 1. The calculation formula is as follows:

[0110] ;

[0111] In the above formula, WA represents the continuous operating time of the equipment, which can be obtained from the equipment status monitoring module of the robot inspection system, and the unit is seconds;

[0112] In the above formula, WB represents the inherent thermal time constant of the equipment, which can be obtained from the equipment manufacturer's technical manual or fire protection industry standards.

[0113] The introduction of the heat accumulation risk factor (RAD) compensates for the limitations of simple temperature monitoring. Considering the thermal inertia characteristics of equipment, under the same temperature rise, equipment with a longer continuous operating time has a higher fire risk, making the risk assessment more in line with the laws of thermodynamics.

[0114] RAE (Range Effect) is a multi-gas risk contribution factor used to comprehensively reflect the contribution of the concentrations of various flammable and toxic gases at an inspection point to the fire risk. Its value ranges from 0 to 1.5. It is obtained by real-time data collection from a multi-gas sensor array mounted on the robot, followed by combined calculations. The specific acquisition process is as follows:

[0115] Sensor types and installation: Catalytic combustion combustible gas sensor (can be installed at the front end of the robot inspection arm to monitor combustible gases such as methane and propane), electrochemical sensor (can be installed on the top of the robot to monitor toxic gases such as CO and H2S), infrared sensor (used to monitor CO2 concentration).

[0116] The calculation formula is: ;

[0117] In the above formula, Cco represents the real-time concentration of carbon monoxide, while CSco represents the fire alarm threshold for carbon monoxide gas, which can be obtained from fire protection code documents.

[0118] In the above formula, Cch represents the real-time concentration of methane, while CSch represents the fire alarm threshold for methane gas, which can be obtained from fire protection code documents.

[0119] In the above formula, Ch2 represents the real-time concentration of hydrogen sulfide, while CSh2 represents the fire alarm threshold for hydrogen sulfide gas, which can be obtained from fire protection code documents.

[0120] In the above formula, α, β and γ represent the proportion weight of each gas, respectively. In this embodiment, their values ​​are preset to 0.4, 0.3 and 0.3 respectively.

[0121] It should be noted that the calculation formula for the multi-gas risk contribution factor (RAE) only provides a formula for three possible gas combinations. In actual inspections, different situations may arise due to varying inspection environments. For example, in a certain environment, more attention may be paid to other types of gases. Users can then customize the settings according to the actual application environment and the gas focus. This calculation formula provides one possible implementation method.

[0122] The introduction of the multi-gas risk contribution factor (RAE) integrates the concentration information of multiple gases into a unified dimensionless risk value, avoiding the limitations of single gas monitoring and comprehensively reflecting the potential risks of gas leaks causing fires or explosions.

[0123] RAF is the Equipment Aging Factor, used to reflect the aging status of equipment. Its value ranges from 0 to 1, and the calculation formula is as follows:

[0124] RAF = YA ÷ YB;

[0125] In the above formula, YA represents the equipment's operating time, which can be obtained from the equipment asset management system;

[0126] In the above formula, YB represents the design service life of the equipment, which can be obtained from the equipment manufacturer's technical manual.

[0127] The introduction of this parameter quantifies the impact of equipment aging on fire risk. The higher the degree of aging, the greater the probability of equipment failure causing a fire, providing a reference for risk assessment in terms of equipment status.

[0128] RAG stands for Ambient Relative Humidity, which is the relative humidity of the environment around the inspection point. The value ranges from 0 to 1 (0% to 100%). It can be obtained through the temperature and humidity sensor on the robot. The sensor is installed on the top of the robot and directly outputs the relative humidity value. The introduction of this Ambient Relative Humidity RAG reflects the humidity level of the environment and provides basic data for the correction of short circuit risks of electrical equipment.

[0129] RAH is the environmental dust concentration coefficient, which reflects the degree of impact of environmental dust concentration on equipment risk. The value ranges from 0 to 1, and the calculation formula is RAH = KA ÷ KB. KA represents the real-time dust concentration (obtained by a laser dust sensor mounted on the top of the robot), and KB represents the concentration threshold corresponding to the lower explosive limit of dust, which can be obtained from safety specifications. The introduction of this environmental dust concentration coefficient RAH quantifies the impact of environmental dust on fire risk. The higher the dust concentration, the worse the equipment heat dissipation, and the greater the risk of dust explosion. This provides an environmental dimension reference for risk assessment.

[0130] A1 is the temperature risk weighting coefficient, which is the contribution weight of temperature deviation to the risk of single-point inspection. The value range is 0.3-0.7, and it can be preset by the system administrator according to the equipment type. For example, electrical equipment (motors and transformers, etc.) is preset to 0.5 in this embodiment, pipeline equipment, etc. is preset to 0.3 in this embodiment, and storage equipment, etc. is preset to 0.4 in this embodiment. It is used to reflect the degree of dominance of temperature anomalies on fire risk in different equipment, so that the risk assessment is more in line with the fire characteristics of the equipment and avoids the irrationality of using a uniform assessment standard for all equipment.

[0131] A2 is the weight of the multi-gas risk contribution factor, which ranges from 0.2 to 0.5. It can be preset by the system administrator according to the scenario, such as 0.4 for chemical workshops and 0.2 for electrical rooms. The introduction of this multi-gas risk contribution factor weight A2 reflects the degree of impact of gas leakage on fire risk in different scenarios. In chemical scenarios, gas leakage is the main risk source and has a higher weight, while in electrical scenarios, abnormal temperature is the main risk source and has a lower weight.

[0132] A3 represents the risk weight for equipment aging, which is the contribution weight of the degree of equipment aging to the risk at a single point. The value ranges from 0.1 to 0.3 and can be preset by the system administrator according to the equipment type. For example, the preset value is 0.3 for equipment that has been in operation for more than 10 years and 0.1 for new equipment. The introduction of this weight parameter reflects the impact of equipment aging on fire risk. The insulation and heat dissipation performance of aging equipment decreases, resulting in a higher fire risk. The weight setting makes the risk assessment more in line with the characteristics of the entire life cycle of the equipment.

[0133] A4 represents the environmental humidity correction weight, which is the weight by which environmental humidity corrects for risk. The value ranges from 0 to 0.2 and can be preset by the system administrator. For example, the preset value is 0.2 for humid environments (humidity > 80%) and 0.05 for dry environments. The introduction of this weight parameter reflects the impact of environmental humidity on the insulation performance of electrical equipment. Electrical equipment is more prone to short circuits in high humidity environments, and the risk is higher. By adjusting the weight, the risk assessment can be made more in line with the actual environment.

[0134] A5 is the environmental dust correction weight, which is the correction weight of environmental dust concentration on risk. The value range is 0-0.2 and can be preset by the system administrator. For example, in a dust workshop, it can be preset to 0.2, and in a general office environment, it can be preset to 0.05. The introduction of this weight parameter reflects the impact of environmental dust on the heat dissipation and insulation performance of equipment. In high dust environments, equipment is prone to dust accumulation, resulting in poor heat dissipation and higher risk. By adjusting the weight, the risk assessment can be made more in line with the actual environment.

[0135] Based on the above, this inspection risk unit integrates multi-dimensional discrete monitoring data such as temperature deviation, multi-gas concentration, and equipment aging status obtained from single-point inspections into a unified dimensionless risk index. This eliminates the one-sidedness of judging by a single parameter, provides the system with accurate quantitative basis for single-point risk, solves the subjective problem of judging risk based on experience in traditional inspections, and realizes standardized assessment of single-point risk.

[0136] The single-point inspection risk comprehensive index RA serves as the core input data for the risk diffusion unit and directly guides the dynamic adjustment of the robot's inspection path. Inspection points with high risk indices will be included in the key inspection sequence and their inspection frequency will be increased, while inspection points with low risk indices can have their inspection frequency appropriately reduced, thus optimizing the robot's inspection resource allocation. In addition, the results can be directly used as the trigger for equipment maintenance, providing data support for preventive maintenance of equipment.

[0137] The higher the single-point inspection risk comprehensive index RA result, the more significant the combined impact of risk factors such as temperature deviation, gas concentration, and equipment aging at that inspection point, and the higher the probability of a fire or failure at that single point. The system needs to pay close attention to the dynamic changes at that point.

[0138] The smaller the single-point inspection risk comprehensive index (RA) result, the more likely that all risk factors at the inspection point are within a safe range, the single-point status is stable, the system does not need to invest additional inspection resources, and resources can be allocated to other risk points.

[0139] refer to Figure 1 , Figure 2 as well as Figure 3 The risk diffusion unit is:

[0140] B1. By precisely dividing the inspection order, the comprehensive risk index of the single-point inspection of the i-th inspection point is introduced into this risk diffusion unit.

[0141] B2. Calculate the risk impact area of ​​the i-th inspection point by analyzing the risk diffusion coverage of the i-th inspection point.

[0142] B3. By analyzing the combustible materials around the equipment, we can analyze the ease with which the risk of the i-th inspection point is transmitted to the surrounding area, thereby calculating the risk transmission coefficient of the i-th inspection point, and summing the parameters calculated in the above steps.

[0143] B4. Calculate the combustible density coefficient of the i-th inspection point by analyzing the impact of the density of combustibles around the inspection point on risk diffusion.

[0144] B5. By combining the real-time wind speed of the j-th vent, the effective area of ​​the j-th vent, the wind direction correction coefficient, and the minimum ventilation volume threshold of the area, the influence of the area ventilation conditions on the smoke diffusion speed is reflected, so as to output the ventilation diffusion acceleration ratio, and finally output the area risk diffusion coefficient.

[0145] The calculation formula for the risk diffusion unit is as follows:

[0146] ;

[0147] in:

[0148] ET is the regional risk diffusion coefficient;

[0149] n represents the number of inspection points within the area, i.e., the total number of robot inspection points in the current assessment area. This number can be obtained from the map planning module of the robot inspection system. The number of inspection points is counted based on the preset inspection area boundary to define the coverage of the area risk assessment and ensure that the assessment results reflect the risk status of the entire area.

[0150] RA i The single-point inspection risk comprehensive index of the i-th inspection point, with a value range of 0-3, is introduced as the basic unit data for regional risk assessment, reflecting the risk status of a single inspection point.

[0151] ETA i Let be the risk impact area of ​​the i-th inspection point, i.e., the area where the risk at the i-th inspection point may spread and cover. This can be preset according to the equipment type and scenario, such as the risk impact area of ​​a transformer being 20m². 2 The risk-affected area of ​​the cable tray is 10m². 2 This parameter can be obtained from the equipment attribute library of the robot inspection system. Its introduction reflects the impact range of the risk of a single inspection point on the surrounding area, providing a basis for the weighted average of regional risks.

[0152] ETB iThis is the risk transmission coefficient for the i-th inspection point, reflecting the ease with which the risk at the i-th inspection point is transmitted to the surrounding area. Its value ranges from 0 to 1 and can be preset based on the availability of flammable materials around the equipment. For example, if there are flammable materials such as cardboard boxes and plastics around the equipment, ETB... i It can be 0.9, surrounded by non-combustible materials, ETB i It can be 0.3. The introduction of this parameter reflects the impact of the environment around the inspection point on the spread of risk. The more flammable materials there are, the easier it is for the risk to spread, and the higher the transmission coefficient is.

[0153] ETC i Let be the combustible density coefficient at the i-th inspection point, used to reflect the impact of the density of combustibles around the i-th inspection point on risk diffusion. Its value ranges from 0 to 1, and it is calculated using the following formula: ETC i =UA÷(UB×HI), where UA is the total volume of combustibles within a 10-meter radius of the inspection point (which can be estimated by the robot's vision recognition module), UB is the risk impact area of ​​the inspection point, and HI is the assessment height (e.g., 2 meters). The introduction of this parameter quantifies the impact of combustible density on risk diffusion. The higher the combustible density, the faster the risk diffusion, providing a basis for the correction of regional risk diffusion.

[0154] ETD, or Ventilation Diffusion Acceleration Ratio, reflects the influence of regional ventilation conditions on the rate of smoke diffusion. Its value ranges from 0 to 2, and the calculation formula is as follows:

[0155] ;

[0156] In the above formula, K1 j The real-time wind speed at the j-th vent is obtained by a wind speed sensor installed at the vent, and the unit is m / s.

[0157] In the above formula, K2 j The effective area of ​​the j-th vent can be obtained from the building ventilation system design drawings, and the unit is m. 2 ;

[0158] In the above formula, K3 j This represents the wind direction correction factor, which is 1.2 when the wind is downwind, 0.3 when the wind is against the wind, and 0.8 when the wind is crosswind.

[0159] In the above formula, KSA represents the minimum ventilation threshold for the area, which can be obtained from building fire protection design codes, and the unit is m. 3 / s;

[0160] B1 is the combustible material correction weight, which is the correction weight of the quantity of combustible materials on the risk spread. The value range is 0-0.3, and it can be preset by the system administrator according to the scenario. For example, the preset value is 0.3 for the warehouse scenario and 0.1 for the electrical room. The introduction of this parameter is to reflect the degree of influence of combustible materials on risk spread under different scenarios. In the warehouse scenario, there are more combustible materials, the risk spreads faster, and the weight is higher. In the electrical room, there are fewer combustible materials, and the weight is lower.

[0161] B2 is the ventilation correction weight, which is the correction weight of ventilation conditions on risk diffusion. The value range is 0-0.3. It can be preset by the system administrator according to the scenario. For example, the preset value is 0.3 for underground garages and 0.1 for well-ventilated workshops. It is used to reflect the degree of influence of ventilation on risk diffusion in different scenarios. In underground garages, the ventilation is poor, the smoke does not spread easily, the risk accumulates quickly, and the weight is higher. In well-ventilated scenarios, the smoke spreads easily and the weight is lower.

[0162] Based on the above, this risk diffusion unit combines single-point risk with regional spatial characteristics (risk impact area, combustible material distribution, and ventilation conditions) to upgrade from single-point risk to regional risk situation, solving the problem that isolated assessment of single-point risk cannot reflect the possibility of risk diffusion, and providing the system with a panoramic view of risk at the regional level.

[0163] The regional risk diffusion coefficient serves as the core input data for calculating the global early warning level of the global early warning response unit, and also directly guides the pre-deployment of regional fire-fighting resources: in areas with a high diffusion coefficient, the system can activate fire-fighting equipment in advance (such as turning on the ventilation system and pre-activating fire extinguishing devices); in areas with a low diffusion coefficient, the system maintains the status of conventional fire-fighting equipment and optimizes the response efficiency of fire-fighting resources. In addition, the results can be used to delineate regional risk isolation boundaries and provide a reference for emergency evacuation route planning.

[0164] The larger the regional risk diffusion coefficient ET, the higher the possibility of a single point of risk in the region spreading to the surrounding areas, and the wider the risk coverage. The system needs to expand the regional monitoring range and strengthen the regional linkage of fire protection resources.

[0165] The smaller the regional risk diffusion coefficient ET, the more effectively the spread of single-point risks in the region is limited. The risks are confined to local areas, and the system does not need to mobilize fire-fighting resources on a large scale. It is sufficient to focus on single-point risks.

[0166] refer to Figure 1 , Figure 2 as well as Figure 3 The global early warning response unit is:

[0167] C1. Introduce the maximum risk comprehensive index of a single point within the region into the global early warning response unit to assess the highest single-point inspection risk comprehensive index among all inspection points in the current assessment region. By introducing the regional risk diffusion coefficient into the global early warning response unit, the overall diffusion status of risk within the region can be reflected.

[0168] C2. By analyzing the coverage ratio of video surveillance in the area, the video surveillance coverage rate is calculated to reflect the risk visualization monitoring capability, thereby correcting the early warning response level.

[0169] C3. By analyzing the proportion of intact fire protection facilities in the area, the hardware capabilities for risk management in the area are reflected, and the integrity rate of fire protection facilities is calculated.

[0170] C4. By combining relative personnel density, regional personnel fire training completion rate, average response time of the most recent emergency drill in the region, and average response time of emergency drills in the same industry, the difficulty of personnel emergency response in the region is reflected, and the personnel emergency response difficulty coefficient is calculated to finally output the global early warning response level.

[0171] The calculation formula for the global early warning response unit is as follows:

[0172] ;

[0173] in:

[0174] MS represents the global early warning response level;

[0175] The introduction of the regional risk diffusion coefficient ET reflects the overall diffusion status of risks within the region and is one of the core reference bases for the global early warning level;

[0176] max(RA) is the maximum risk index of a single point in the region, which is the highest single-point inspection risk index among all inspection points in the current assessment region. The introduction of this parameter reflects the most serious single-point risk situation in the region and is one of the core reference bases for the global early warning level.

[0177] MSA stands for Video Surveillance Coverage, which is used to assess the coverage ratio of video surveillance in an area. The value ranges from 0 to 1 and can be obtained from the building security system. It is a statistical measure of the ratio of the coverage area of ​​surveillance cameras in an area to the total area of ​​the area, reflecting the ability of the area to visualize and monitor risks, and providing a basis for the correction of early warning response levels.

[0178] MSB stands for Fire Protection Facilities Integrity Rate, which assesses the proportion of intact fire protection facilities in an area. The value ranges from 0 to 1 and can be obtained from the fire protection facilities management system. It is the ratio of the number of intact fire extinguishers, fire hydrants, and automatic sprinkler systems in the area to the total number of facilities. This parameter is used to reflect the hardware capabilities for risk management in the area and provides a basis for revising the early warning response level.

[0179] MSC is the personnel emergency response difficulty coefficient, used to reflect the difficulty of personnel emergency response within a region. Its value ranges from 0 to 1.5, and the calculation formula is as follows:

[0180] ;

[0181] In the above formula, MEA represents relative personnel density, which can be obtained by dividing the real-time personnel density of the area by the maximum personnel density that the area can accommodate. Personnel density is obtained from video AI recognition system or access control system.

[0182] In the above formula, MEB represents the completion rate of fire safety training for personnel in the area, which can be obtained from the enterprise's safety management system;

[0183] In the above formula, MEC represents the average response time of the region's most recent emergency drill, which can be obtained from the company's safety records;

[0184] In the above formula, MED represents the average response time of emergency drills in the same industry, which can be obtained from fire departments and relevant companies.

[0185] In the above formula, V1, V2 and V3 represent the weighting coefficients of personnel density, training completion rate and emergency drill time, respectively. In this embodiment, the above weighting coefficients are preset to 0.4, 0.4 and 0.3, respectively.

[0186] The introduction of the personnel emergency response difficulty coefficient (MSC) comprehensively reflects the impact of personnel density, fire training, and emergency drill capabilities on risk management in the area. The greater the personnel emergency response difficulty, the higher the warning response level needs to be to ensure that the warning level matches the actual response capability.

[0187] C1 is the maximum risk weight for a single point, which is the contribution weight of the maximum risk of a single point in the region to the global warning level. The value range is 0-1 and can be preset by the system administrator. For example, the preset value is 0.6 for key protection areas (such as data centers) and 0.4 for ordinary areas. This weight parameter is used to reflect the impact of high risk at a single point on the global warning. A single high-risk point in a key area may cause serious consequences, so the weight is higher to ensure that the warning level can reflect the most serious risk situation.

[0188] C2 is the regional risk weight, which is the contribution weight of the overall regional risk to the global early warning level. Its value ranges from 0 to 1, so as to satisfy C1 + C2 = 1. The introduction of this parameter is used to balance the impact of the maximum single-point risk and the overall regional risk on the global early warning, and to ensure that the early warning level takes into account both the most severe single-point risk and the overall risk diffusion situation in the region.

[0189] C3 is the video surveillance coverage correction weight, which is the correction weight of video surveillance coverage on the early warning response level. The value range is 0-0.2, and it can be preset by the system administrator. For example, the preset value is 0.1 for areas with full video coverage and 0.2 for areas without video coverage. The introduction of this parameter reflects the supporting role of video surveillance in risk management. Video coverage areas can more quickly locate risk sources, and the early warning response level can be adjusted appropriately.

[0190] C4 is the adjustment weight for the fire protection facility integrity rate, which is the adjustment weight of the fire protection facility integrity rate to the early warning response level. The value range is 0-0.2, and it can be preset by the system administrator. For example, the area with a fire protection facility integrity rate of 100% can be preset to 0.1, and the area with an integrity rate of less than 60% can be preset to 0.2. The introduction of this parameter reflects the supporting role of fire protection facilities in risk management. Areas with a high fire protection facility integrity rate can handle risks more quickly, and the early warning response level can be adjusted appropriately.

[0191] C5 is the personnel emergency response difficulty correction weight, which is the correction weight of the personnel emergency response difficulty to the warning response level. The value range is 0-0.3, which can be preset by the system administrator. For example, the preset value is 0.3 for densely populated areas and 0.1 for uninhabited areas. This reflects the impact of personnel emergency response capabilities on risk management. Densely populated areas are more difficult to evacuate, and the warning response level needs to be more stringent.

[0192] Based on the above, this global early warning response unit integrates multi-dimensional data such as single-point risk, regional spread trend and emergency resource capabilities (video surveillance, fire-fighting facilities and personnel response) to output a unified global early warning level, which solves the problem of scattered multi-source data that cannot form a basis for decision-making, and provides the system with closed-loop decision support from monitoring to response.

[0193] The global early warning response level (MS) directly triggers the system's tiered emergency response process. High early warning levels activate fire linkage mechanisms (such as automatic alarms, activation of fire extinguishing systems, and notification of emergency personnel), medium early warning levels activate key area monitoring and personnel standby, and low early warning levels maintain routine inspection and monitoring. In addition, the results can serve as core data for safety management reports, providing quantitative basis for enterprise safety decisions.

[0194] The higher the global early warning response level (MS), the more severe the risk situation is across the entire system, with high single-point risk and a high probability of spread. At the same time, the emergency resource capacity is insufficient, and the system needs to activate the highest level of emergency response to fully mobilize resources such as fire fighting and personnel.

[0195] The smaller the global early warning response level (MS), the more controllable the global risk is, the lower the risk at a single point and the less likely it is to spread, the more sufficient the emergency resources are, the more the system maintains its normal operating status, and the less need for additional emergency measures.

[0196] It is worth noting that this embodiment presents an iterative loop, in which the weight coefficients in the inspection risk unit are negatively influenced based on the global early warning response level (MS). This results in a positive influence relationship when the single-point inspection risk comprehensive index (RA) is substituted into the global early warning response unit for calculation, thus forming a cyclical negative influence relationship between the global early warning response unit and the inspection risk unit. This makes the overall system more intelligent. The specific loop process is as follows:

[0197] When the alert level is high, the weight of core risk factors needs to be increased, that is:

[0198] A1 new =min(A1) old ×1.15, 0.7);

[0199] A2 new =min(A2) old ×1.1, 0.5);

[0200] A3 new =max(A3) old ×0.9, 0.1);

[0201] When the alert level is at the medium level, it is necessary to maintain the weight of the core factors, namely:

[0202] A1 new =A1 old ;

[0203] A2 new =min(A2) old ×1.05, 0.5);

[0204] A3 new =min(A3) old ×1.05, 0.3);

[0205] When the alert level is low, the weight of core factors should be appropriately reduced, that is:

[0206] A1 new =max(A1) old ×0.9, 0.3);

[0207] A2 new =max(A2) old ×0.95, 0.2);

[0208] A3 new =min(A3) old ×1.1, 0.3).

[0209] It should be noted that: when the difference in global early warning response level between two consecutive iterations is |MS new -MSold | When <0.1, it indicates that the global warning level is stable, and the iteration terminates at this point, or the change in risk factor weights between two consecutive iterations |A1 new -A1 old | < 0.05, | A2 new -A2 old | < 0.05, | A3 new -A3 old When | < 0.05, it means the weights have adapted to the current global situation and no further adjustments are needed, thus the iteration terminates;

[0210] Traditional inspection risk calculations use manually preset static weights, which cannot adapt to dynamically changing risk scenarios. For example, in a globally low-risk state, highly sensitive static weights can easily lead to false alarms; in a globally high-risk state, low-sensitive static weights can easily lead to missed alarms. An iterative mechanism can adjust the sensitivity of single-point monitoring in real time based on the overall situation, avoiding the inherent defects of static settings.

[0211] A single point of risk can trigger regional spread, escalating into a global high-risk situation. Conversely, in a global high-risk situation, even a minor anomaly at a single point can signal an escalation of risk. An iterative mechanism enables the system to follow the risk transmission path and dynamically adjust its assessment strategy, rather than viewing single-point and global risks in isolation.

[0212] Robotic fire inspection systems need to cope with complex and ever-changing scenarios (such as aging workshop equipment, changes in warehouse goods stacking, etc.). Frequent manual adjustments to weights are costly and time-consuming. An iterative mechanism allows the system to autonomously optimize assessment parameters based on the current risk situation without human intervention, thereby improving the system's intelligence level.

[0213] When the overall risk is high, the inspection risk unit becomes more sensitive to core risk factors (temperature and gas), which can quickly capture minor anomalies at a single point and provide timely warnings of risk escalation. When the overall risk is low, the sensitivity is appropriately reduced to reduce unnecessary high-risk judgments and avoid excessive warnings that could disrupt normal production.

[0214] Through closed-loop iteration, single-point risk assessment and global early warning level form a two-way calibration: the global situation corrects the weight deviation of single-point assessment, and single-point data supports the situation judgment of global early warning, making the risk assessment results more in line with the actual security status and avoiding the deviation under static calculation.

[0215] When a risk emerges in a local area, the global warning level is raised, which in turn increases the monitoring sensitivity of the single point of risk in that area, allowing the system to focus on subtle changes in high-risk areas; when the risk is mitigated, the weights are automatically adjusted back to normal monitoring status to cope with risk changes in different scenarios.

[0216] The iterative mechanism enables automatic parameter optimization, eliminating the need for maintenance personnel to manually adjust the weights of inspection risk units according to different scenarios. This reduces the subjectivity and lag of manual intervention and lowers the long-term maintenance labor costs.

[0217] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A robotic fire inspection, early warning, and monitoring system, characterized in that, This includes a fire risk quantification and early warning decision-making module and a graded response and resource scheduling module; The fire risk quantification and early warning decision-making module is used to receive pre-processed inspection risk data, risk diffusion data, and early warning response correlation data. The fire risk quantification and early warning decision-making module includes: Inspection risk units, risk diffusion units, and global early warning response units; The inspection risk unit is based on the real-time equipment temperature, heat accumulation risk coefficient and multi-gas risk contribution factor in the inspection risk data, and performs weighted processing to output a single-point inspection risk comprehensive index. The risk diffusion unit, based on the risk impact area, risk transmission coefficient, combustible density coefficient, and ventilation diffusion acceleration ratio of the i-th inspection point in the risk diffusion data, introduces a single-point inspection risk comprehensive index according to the inspection point, and performs weighted processing to output the regional risk diffusion coefficient. The global early warning response unit outputs the global early warning response level based on the video surveillance coverage, fire-fighting facility integrity rate, and personnel emergency response difficulty coefficient in the early warning response associated data, and introduces the single-point maximum risk comprehensive index and regional risk diffusion coefficient in the region. Combined with weighted processing, the global early warning response level is output. The hierarchical response and resource scheduling module: It is used to receive the comprehensive risk index of single-point inspection, the regional risk diffusion coefficient, and the global early warning response level, so as to realize the implementation of robot fire inspection, early warning and supervision.

2. The robot-based fire inspection, early warning, and monitoring system according to claim 1, characterized in that: The processing procedure for the inspection risk unit is as follows: A1. The impact of equipment operating temperature on fire safety is considered by combining the real-time temperature of the equipment, the normal operating temperature of the equipment, and the maximum allowable temperature of the equipment. A2. By considering the relationship between the continuous operating time of the equipment and fire safety, the degree of amplification of fire risk due to heat accumulation after continuous operation of the equipment is reflected, so as to calculate the heat accumulation risk coefficient. A3. By combining the real-time concentrations of carbon monoxide, methane, and hydrogen sulfide, the contribution of the concentrations of various combustible and toxic gases at the inspection point to the fire risk is comprehensively reflected, so as to calculate the multi-gas risk contribution factor. A4. By combining the equipment's operating time with its designed service life, the aging status of the equipment can be reflected, and the equipment aging coefficient can be calculated. A5. Calculate the relative humidity of the environment by analyzing the relationship between the relative humidity of the environment around the inspection point and the short-circuit risk of electrical equipment; A6. By considering the real-time dust concentration at the inspection point, the impact of environmental dust on fire risk is quantified, thereby calculating the environmental dust concentration coefficient. The parameters output in the above steps are then weighted to output a comprehensive risk index for single-point inspection.

3. The robot-based fire inspection, early warning, and monitoring system according to claim 2, characterized in that: The processing procedure of the risk diffusion unit is as follows: B1. By precisely dividing the inspection order, the comprehensive risk index of the single-point inspection of the i-th inspection point is introduced into this risk diffusion unit. B2. Calculate the risk impact area of ​​the i-th inspection point by analyzing the risk diffusion coverage of the i-th inspection point. B3. By analyzing the combustible materials around the equipment, we can analyze the ease with which the risk of the i-th inspection point is transmitted to the surrounding area, thereby calculating the risk transmission coefficient of the i-th inspection point, and summing the parameters calculated in the above steps. B4. Calculate the combustible density coefficient of the i-th inspection point by analyzing the impact of the density of combustibles around the inspection point on risk diffusion. B5. By combining the real-time wind speed of the j-th vent, the effective area of ​​the j-th vent, the wind direction correction coefficient, and the minimum ventilation volume threshold of the area, the influence of the area ventilation conditions on the smoke diffusion speed is reflected, so as to output the ventilation diffusion acceleration ratio, and finally output the area risk diffusion coefficient.

4. The robot fire inspection and early warning monitoring system according to claim 3, characterized in that: The processing procedure of the global early warning response unit is as follows: C1. Introduce the maximum risk comprehensive index of a single point within the region into the global early warning response unit to assess the highest single-point inspection risk comprehensive index among all inspection points in the current assessment region. By introducing the regional risk diffusion coefficient into the global early warning response unit, the overall diffusion status of risk within the region can be reflected. C2. By analyzing the coverage ratio of video surveillance in the area, the video surveillance coverage rate is calculated to reflect the risk visualization monitoring capability, thereby correcting the early warning response level. C3. By analyzing the proportion of intact fire protection facilities in the area, the hardware capabilities for risk management in the area are reflected, and the integrity rate of fire protection facilities is calculated. C4. By combining relative personnel density, regional personnel fire safety training completion rate, average response time of the region's most recent emergency drill, and average response time of emergency drills in the same industry, the difficulty level of personnel emergency response in the region is reflected, and the personnel emergency response difficulty coefficient is calculated to finally output the global early warning response level.

5. The robot-based fire inspection, early warning, and monitoring system according to claim 1, characterized in that: The hierarchical response and resource scheduling module includes a hierarchical early warning response unit, a robot inspection and scheduling unit, and a fire resource linkage unit.

6. The robot-based fire inspection, early warning, and monitoring system according to claim 5, characterized in that: The hierarchical early warning response unit is specifically: When 0 ≤ Global Early Warning Response Level < 2, it indicates a low early warning level. At this time, the normal operation status is maintained, and only daily safety inspection logs are generated and pushed to the enterprise safety management department for archiving. When 2 ≤ Global Early Warning Response Level < 4, it indicates a medium early warning level. At this time, a risk attention warning notification is pushed to the operation and maintenance personnel, along with the location markers of high-risk single points and medium-risk diffusion areas, and video AI real-time monitoring of key areas is initiated. When 4 ≤ Global Early Warning Response Level < 6, it indicates a high early warning level. At this time, the internal fire alarm of the enterprise is triggered, the emergency preparedness warning is pushed to the emergency command center, and high diffusion risk areas are marked at the same time, and the pre-linkage mode of fire protection facilities is activated. When the global early warning response level is ≥6, it indicates an emergency early warning level. At this time, an external fire alarm is triggered, an emergency response warning is pushed to all relevant personnel, and the whole area fire linkage mechanism is activated.

7. The robot-based fire inspection, early warning, and monitoring system according to claim 6, characterized in that: The robot inspection scheduling unit dynamically adjusts the inspection strategy by combining the single-point inspection risk comprehensive index and the regional risk diffusion coefficient, specifically as follows: Low warning level: The robot inspects according to the preset routine path, with an inspection frequency of once every 2 hours. For single points with a comprehensive risk index of <0.5, the frequency can be appropriately extended to once every 4 hours. Medium-level warning: For single points with a comprehensive risk index of >2, the inspection frequency is increased to once every 30 minutes; for areas with a regional risk diffusion coefficient of >1.2, the inspection coverage density is increased. High alert level: Robots are given priority for inspection. In high-risk areas with a risk diffusion coefficient > 1.8, the inspection frequency is increased to once every 15 minutes. For single points with a comprehensive risk index > 3, a 24-hour continuous monitoring mode is activated. Emergency Warning Level: The robot immediately proceeds to the single point with the highest comprehensive risk index for inspection, continuously collects and transmits real-time data, while avoiding dangerous paths in high-risk areas and guiding emergency personnel to the scene safely.

8. The robot fire inspection and early warning monitoring system according to claim 7, characterized in that: The fire resource linkage unit combines the single-point inspection risk comprehensive index and the regional risk diffusion coefficient to coordinate and control fire-fighting facilities, specifically as follows: Low alert level: Fire protection facilities remain in normal standby status and undergo regular automatic self-inspections; Medium-level warning: For areas with a risk diffusion coefficient > 1.2, pre-start the ventilation system, put the fire pumps into standby mode, and push the location information of fire extinguishers and fire hydrants to the maintenance personnel's terminal simultaneously; High alert level: For areas with a risk diffusion coefficient > 1.8, the ventilation system, sprinkler system and gas extinguishing device will be automatically activated, the fire pump will be activated in pressurization mode, and the emergency lighting system will be automatically turned on. Emergency warning level: Automatically activate sprinkler systems or gas extinguishing devices in high-risk areas, close ventilation openings in the area, and activate fire broadcasts to guide personnel evacuation.

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