Electrical fire fighting internet of things monitoring method and system

Abstract

The invention discloses an electrical fire fighting Internet of Things monitoring method and system, relates to the technical field of electrical safety and fire early warning, and aims to solve the problem that wiring or stable power supply cannot be realized in special scenes such as specified cultural relics and buildings, and realizes self-sustaining operation of a passive multi-mode sensor through a composite energy collection mode of temperature difference, vibration and light energy. A low-power-consumption mode is intelligently switched when power supply is insufficient, interference factors are collected, temperature and leakage current thresholds are dynamically adjusted by calculating the composite interference factors, the fire risk probability is analyzed and prediction errors are evaluated by combining a lightweight fire prediction model and a local loss function, and the evidence storage frequency is adjusted according to the risk probability based on the evaluation result. The problems of power supply, accurate monitoring, risk assessment, data management and the like in special scenes are effectively solved, the accuracy and reliability of electrical fire monitoring are remarkably improved, and the system has important significance in guaranteeing electrical safety of places such as cultural relics and buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical safety and fire warning, and in particular to an electrical fire firefighting Internet of Things monitoring method and system. Background Art

[0002] With economic development, electricity load has increased and the electricity usage situation has become complicated. Electrical fire accidents have occurred frequently, especially on the end-user side, where the situation is severe. In the field of cultural relics protection, cultural relics buildings are mostly civil structures with low fire resistance levels and great hidden dangers in the use of fire and electricity. Traditional electrical fire monitoring methods have many disadvantages. Therefore, there is an urgent need to innovate monitoring methods to improve the level of electrical fire prevention and control.

[0003] Existing technologies, such as the invention patent application with publication number CN118587833A, disclose a fire warning system based on electrical informatization, including an electrical behavior analysis module, an abnormal behavior identification module, an environmental factor analysis module, a comprehensive judgment and warning module, and a feedback control module. Among them, the electrical behavior analysis module is used to collect parameters of electrical equipment, the abnormal behavior identification module analyzes whether the behavior of electrical equipment deviates from the normal mode, the environmental factor analysis module analyzes the impact of environmental factors on electrical safety, the comprehensive judgment and warning module makes a comprehensive judgment to determine whether there is a fire risk, and the feedback optimization module provides feedback information to the electrical behavior analysis module for optimization. Through intelligent analysis and adaptive learning, the early identification and accurate warning of electrical fire risks are effectively realized, while the false alarm rate is significantly reduced, providing a long-term and effective electrical safety management solution.

[0004] There are at least the following technical problems with the above solution: 1. The above solution lacks a sensor power supply solution for special scenarios, which will make it difficult to deploy the system and maintain stable operation in places such as cultural relics buildings where wiring cannot be laid or stable power supply cannot be provided. Since self-sustaining power supply methods such as composite energy harvesting are not considered, once in an environment with limited traditional power supply, the sensor will not be able to work normally due to lack of power, causing the entire fire warning system to fail, greatly limiting the application scenarios and scope of application of the system.

[0005] The above scheme lacks comprehensive monitoring of environmental interference factors and a dynamic threshold adjustment mechanism, which will greatly reduce the accuracy of fire risk assessment. Since the above scheme only monitors temperature, humidity and smoke concentration, it does not pay attention to interference factors such as current harmonic distortion rate and light scattering particle concentration that may affect the sensor monitoring accuracy, and there is no corresponding threshold adjustment strategy. In actual complex environments, these interference factors can easily cause data misjudgment, making it difficult for the system to accurately distinguish between normal and abnormal states of electrical equipment, thereby generating false warnings or missing important fire hazards, reducing the reliability and warning effectiveness of the system.

[0006] The above solution lacks a dynamic adjustment strategy for the frequency of evidence storage based on the probability of fire risk, which will lead to inefficient utilization of data storage resources. It also lacks a mechanism for flexibly adjusting the frequency of evidence storage. Evidence is stored at a fixed frequency in both high-risk and low-risk scenarios. In high-risk scenarios, key data may be lost due to untimely evidence storage, and in low-risk scenarios, storage space is wasted, making it difficult to meet the data management needs in actual fire monitoring. Summary of the Invention

[0007] The purpose of the present invention is to provide an electrical fire firefighting Internet of Things monitoring method and system, which solves the problems existing in the background technology.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an electrical fire firefighting Internet of Things monitoring method, including: S1, multimodal sensor deployment: when wiring cannot be laid or stable power supply cannot be provided in a designated cultural relic building, the self-sustaining operation of the sensor is achieved through a composite energy collection method.

[0009] S2. Environmental adaptive signal processing: When the sensor achieves self-sustaining operation, it collects the corresponding interference factors in the designated cultural relic building and then evaluates whether it is necessary to adjust the assessment threshold corresponding to the fire impact parameter.

[0010] S3. Fire risk assessment: When it is necessary to adjust the assessment thresholds corresponding to the fire impact parameters, analyze the corresponding fire risk probability in the designated cultural relic building based on the adjusted temperature thresholds and leakage current thresholds.

[0011] S4. Emergency decision-making assessment: Adjust the corresponding evidence storage frequency in the designated cultural relic building based on the corresponding fire risk probability in the designated cultural relic building.

[0012] In a second aspect, the present invention provides an electrical fire protection Internet of Things monitoring system, comprising: a multimodal sensor deployment module, which is used to achieve self-sustaining operation of the sensor through a composite energy collection method when wiring cannot be laid or a stable power supply cannot be provided in a designated cultural relic building.

[0013] The environmental adaptive signal processing module is used to collect the corresponding interference factors in the designated cultural relic building when the sensor achieves self-sustaining operation, and then evaluate whether it is necessary to adjust the assessment threshold corresponding to the fire impact parameter.

[0014] The fire risk assessment module is used to analyze the corresponding fire risk probability in a specified cultural relic building based on the adjusted temperature threshold and leakage current threshold when the assessment threshold corresponding to the fire impact parameter needs to be adjusted.

[0015] The emergency decision-making assessment module is used to adjust the corresponding evidence storage frequency in the designated cultural relic building according to the corresponding fire risk probability in the designated cultural relic building.

[0016] The beneficial effects of the present invention are: 1. An electrical fire and firefighting Internet of Things monitoring method and system provided by the embodiment of the present invention, in the process of sensor deployment and power supply, adopts a composite energy collection method of temperature difference collection, vibration collection and light energy collection, which is conducive to solving the problem of being unable to lay wires or provide stable power supply in special scenarios such as designated cultural relics buildings, and realizes the self-sustaining operation of passive multimodal sensors. This power supply method gets rid of the dependence on traditional power grids, greatly broadens the application scope of the monitoring system, and enables the system to work stably in complex environments such as ancient buildings and remote areas. When the total power supply power is insufficient, power is allocated in low power mode according to the importance of each sensor, which is conducive to ensuring the normal operation of key sensors and ensuring the effectiveness and reliability of monitoring data acquisition under limited energy conditions.

[0017] 2. In the embodiment of the present invention, during the environmental signal processing process, the composite interference factor is calculated to evaluate whether the assessment threshold of the fire impact parameter needs to be adjusted, which is beneficial to improving the accuracy and adaptability of fire risk assessment. Traditional monitoring methods often ignore these interference factors, which can easily lead to misjudgment or missed judgment. The present invention fully considers the impact of complex environments on monitoring data, and dynamically adjusts the temperature threshold and leakage current threshold according to the actual interference situation, so that the monitoring system can accurately identify fire hazards in different environments and reduce the false alarm and missed alarm rates.

[0018] 3. In the fire risk assessment process, the embodiment of the present invention uses a lightweight fire prediction model to analyze the fire risk probability and uses a local loss function to evaluate the prediction error, which is beneficial to improving the scientific nature and reliability of fire risk assessment, reducing the consumption of computing resources, improving computing efficiency, and facilitating the rapid processing of large amounts of data and outputting accurate risk probabilities.

[0019] 4. In the emergency decision-making process, the embodiment of the present invention adjusts the corresponding evidence storage frequency in the designated cultural relic building according to the fire risk probability, which is conducive to optimizing the configuration of data storage resources. When the fire risk is low, the evidence storage frequency is reduced to avoid wasting storage space. When the fire risk is high, the evidence storage frequency is increased to ensure that key data can be fully recorded, which is convenient for tracing and analyzing the cause after a fire accident occurs. The dynamic adjustment of the evidence storage frequency reduces data storage costs and improves data management efficiency while ensuring data integrity and validity. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 Schematic diagram of the implementation steps of the present invention.

[0022] Figure 2 This is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] See also Figure 1 As shown, the present invention provides an electrical fire firefighting Internet of Things monitoring method, which includes: S1, multimodal sensor deployment: when wiring cannot be laid or stable power supply cannot be provided in a designated cultural relic building, the self-sustaining operation of the sensor is achieved through a composite energy collection method.

[0025] In a specific embodiment, the self-sustaining operation of the sensor is achieved through a composite energy collection method, and the specific process is as follows: the composite energy collection method includes temperature difference collection, vibration collection and light energy collection, and a passive multimodal sensor is deployed in a designated cultural relic building. The self-sustaining operation of the passive multimodal sensor is achieved through temperature difference collection, vibration collection and light energy collection, and the temperature difference power generation power, vibration energy conversion power and light energy supplement power corresponding to the temperature difference collection method, vibration collection method and light energy collection method are calculated respectively, and the total power supply power is obtained by adding the temperature difference power generation power, vibration energy conversion power and light energy supplement power. , query the minimum operating power required by each sensor in the passive multimodal sensor from the database, and accumulate the required minimum total operating power ,when Greater than When , it indicates that the passive multimodal sensor does not need to enter low power mode. Less than or equal to When , it indicates that the passive multimodal sensor needs to enter low power consumption mode to achieve self-sustaining operation of the passive multimodal sensor deployed in the designated cultural relic building.

[0026] It should be noted that the temperature difference power generation The calculation formula is: ,in Expressed as the set Seebeck coefficient, The temperature difference between the inside and outside of a designated cultural relic building. The area of thermoelectric modules corresponding to the power generation equipment for thermoelectric power generation, and the power of vibration energy conversion The calculation formula is: ,in Expressed as the stiffness of the piezoelectric material corresponding to the piezoelectric sensor, and They represent the corresponding amplitude and vibration frequency in the designated cultural relic building collected by the vibration sensor, and the light energy supplement power The calculation formula is: ,in and are the photovoltaic efficiency and light intensity corresponding to photovoltaic power generation, is the area of photovoltaic panels corresponding to photovoltaic power generation.

[0027] It should also be noted that the temperature inside and outside the designated cultural relic building is monitored in real time through the deployed temperature sensors. The temperature sensors include but are not limited to thermocouples and resistance temperature detectors. The Seebeck coefficient is a material property of the thermoelectric module, which represents the voltage generated by the material per unit temperature difference. The Seebeck coefficient is an inherent property of the material and depends on the type of thermoelectric material. The Seebeck coefficient is obtained by consulting the material data sheet or through experimental measurement. The area of the thermoelectric module of the thermoelectric power generation system and the area of the photovoltaic panel of the photovoltaic power generation system are based on the design specifications and standards of the equipment. Specifically, the area of the thermoelectric module of the thermoelectric power generation equipment determines the power generation capacity of the module under certain temperature difference conditions, while the area of the photovoltaic panel determines the power conversion efficiency and output power of the photovoltaic panel under specific lighting conditions. Therefore, these can be directly obtained by consulting the specification manual of the equipment or the information provided by the manufacturer. and , and It is determined during equipment design based on theoretical models and actual application requirements. The stiffness of piezoelectric materials is determined by the inherent properties of the material and is obtained through experiments or by consulting material manuals. If the stiffness of a piezoelectric material is 10 Newtons per meter, this means that for every Newton of force applied, the piezoelectric material will displace 0.1 meters. Photovoltaic efficiency and light intensity can be directly obtained from the power management platform that controls photovoltaic power generation.

[0028] In a specific embodiment, the passive multimodal sensor needs to be in low power consumption mode. The specific process is as follows: when the passive multimodal sensor needs to be in low power consumption mode, the importance of each sensor in the passive multimodal sensor is queried from the database, and based on the minimum operating power required by each sensor, the sensors are sorted from large to small according to importance, the minimum operating power corresponding to each sensor is added in sequence according to the sorting, and the sum of the minimum operating powers obtained by each addition is added. Compare, if the total is The total minimum operating power corresponding to the sensors is less than , accumulated to the The sum of the minimum operating power corresponding to the sensors is greater than , the first The sensor includes the The operating power required for each sensor, is the number corresponding to each sensor in the passive multimodal sensor, The value of is a positive integer.

[0029] It should be noted that "various sensors" refer to the different types of sensors included in the passive multimodal sensor system. Passive sensors generally do not require an external power supply, but instead obtain energy by capturing environmental changes such as temperature, humidity, light, and vibration. Various sensors include but are not limited to temperature and humidity sensors, vibration sensors, light sensors, pressure sensors, and leakage current sensors.

[0030] It should also be noted that the "importance of each sensor in the passive multimodal sensor" refers to the importance of different sensors based on their impact and contribution to the monitoring target, such as the safety of cultural relics buildings and fire risks. For example, vibration sensors are very important for the safety of cultural relics building structures, so they are of higher importance. Temperature and humidity sensors are more critical to environmental monitoring, but in some cases, they are of lower importance.

[0031] It should also be noted that, assuming there are 10 sensors in total, and the sensors are ranked from most important to least important, the minimum operating power corresponding to the top five sensors is 1 watt, 2 watts, 3 watts, 4 watts, and 5 watts. The total minimum operating power of the fourth sensor is 12 watts, which is less than 10 watts. The total minimum operating power of the fifth sensor is 15 watts, which is greater than If the power consumption is 12 watts, the operating power required by the first four sensors including the fourth sensor is allocated first.

[0032] S2. Environmental adaptive signal processing: When the sensor achieves self-sustaining operation, it collects the corresponding interference factors in the designated cultural relic building and then evaluates whether it is necessary to adjust the assessment threshold corresponding to the fire impact parameter.

[0033] In a specific embodiment, the interference factors corresponding to the designated cultural relic building are collected, and the specific process is as follows: the interference factors include the current harmonic distortion rate and the light scattering particle concentration, each collection time point is set, and the current signal of the power grid or electrical appliance at each collection time point is monitored by a current sensor. The edge computing device uses a fast Fourier transform to convert the collected current signal at each collection time point, and then converts the current signal at each collection time point that exists as a time domain signal into a frequency domain signal, extracts each harmonic component from the frequency domain signal, and then calculates the current harmonic distortion rate of the current signal corresponding to each collection time point.

[0034] The light scattering intensity corresponding to each collection time point is obtained by a laser particle counter, and the light scattering particle concentration corresponding to each collection time point is obtained after conversion.

[0035] It should be noted that the calculation process of "calculating the current harmonic distortion rate of the current signal corresponding to each acquisition time point" is achieved by using fast Fourier transform and total harmonic distortion calculation, which is a widely used method in the existing technology. Frequency domain analysis is performed through fast Fourier transform to calculate the harmonic components and obtain the harmonic distortion rate. It is a common signal analysis method in the power system, mainly used to detect power quality problems, such as current harmonic pollution, and will not be elaborated on here.

[0036] It should also be noted that devices such as current sensors and laser particle counters are powered through an additional power supply interface. The conversion of light-scattering particle concentration is achieved by comparing the light-scattering intensity value collected by the laser particle counter with a pre-calibrated calibration curve. The light-scattering intensity reflects the degree of laser scattering by the particles. After measurement by the sensor, it is converted into the corresponding particle concentration through the calibration curve, such as the relationship between the light-scattering intensity and particle concentration of particles of different particle sizes. For example, assuming that the calibration curve shows that when the light-scattering intensity is 10 units, the particle concentration is 100 per cubic centimeter, if the collected intensity is 12 units, then according to the curve, the particle concentration is 120 per cubic centimeter.

[0037] In a specific embodiment, the evaluation of whether the evaluation threshold corresponding to the fire impact parameter needs to be adjusted is performed as follows: by calculating the formula: , get the collection time point of the specified cultural relic building Corresponding composite interference factor ,in is the number corresponding to each collection time point, The value is a positive integer. and Represented as the collection time point The corresponding current harmonic distortion rate and light scattering particle concentration, and They are the weight factors corresponding to the set current harmonic distortion rate and the weight factors corresponding to the light scattering particle concentration.

[0038] Query the preset composite interference factor threshold value of the designated cultural relic building from the database, and Compared with the composite interference factor threshold, if If it is greater than or equal to the composite interference factor threshold, it indicates that the evaluation threshold corresponding to the fire impact parameter needs to be adjusted. Otherwise, it indicates that the evaluation threshold corresponding to the fire impact parameter does not need to be adjusted. The evaluation threshold adjustment corresponding to the fire impact parameter includes temperature threshold adjustment and leakage current threshold adjustment.

[0039] It should be noted that the preset composite interference factor threshold corresponding to the designated cultural relic building is used as the basis for evaluating whether it is necessary to adjust the evaluation threshold corresponding to the fire impact parameter. The setting of the composite interference factor threshold is carried out by combining historical data analysis, building type, environmental factors and expert evaluation. This setting process is the same as the environmental noise threshold setting method in the prior art. For example, in the intelligent building management system, a reasonable noise threshold is set by monitoring the historical operating data and environmental conditions of the equipment to determine whether the equipment is in an abnormal working state. Therefore, it will not be elaborated here.

[0040] In the embodiment of the present invention, during the environmental signal processing process, the composite interference factor is calculated to evaluate whether the evaluation threshold of the fire impact parameter needs to be adjusted, which is beneficial to improving the accuracy and adaptability of fire risk assessment. Traditional monitoring methods often ignore these interference factors, which can easily lead to misjudgment or missed judgment. The present invention fully considers the impact of complex environments on monitoring data, and dynamically adjusts the temperature threshold and leakage current threshold according to the actual interference situation, so that the monitoring system can accurately identify fire hazards in different environments and reduce the false alarm and missed alarm rates.

[0041] S3. Fire risk assessment: When it is necessary to adjust the assessment thresholds corresponding to the fire impact parameters, analyze the corresponding fire risk probability in the designated cultural relic building based on the adjusted temperature thresholds and leakage current thresholds.

[0042] In a specific embodiment, the evaluation threshold value corresponding to the fire impact parameter needs to be adjusted. The specific adjustment process is as follows: query the corresponding basic temperature threshold value in the specified cultural relic building from the database , and then adjust the formula through the temperature threshold: , obtained at the collection time point Corresponding adjusted temperature threshold ,in is the set humidity adjustment coefficient, Represented by the humidity sensor at the collection time point The ambient humidity, is the set benchmark value, is the humidity scaling factor set, It is expressed as the temperature change rate corresponding to the set historical time period.

[0043] The upper limit threshold of leakage current corresponding to each electrical device in the specified cultural relic building in the initial state and the lower limit threshold of leakage current corresponding to the set time period are queried from the database and recorded as and , through the leakage current threshold adjustment formula: , obtained at the collection time point Corresponding to Adjusted leakage current threshold of electrical equipment ,in is the number corresponding to each electrical equipment, The value is a positive integer. For the setting The aging factor corresponding to each electrical equipment.

[0044] It should be noted that Indicates the percentage by which the temperature threshold will increase for every unit increase in humidity. This is obtained through experiments or historical data. For example, through experimental data, it is found that when the humidity increases, the temperature threshold needs to be adjusted by 5% to keep the system stable. The value of is 0.05, It is a standard humidity level that represents the humidity state of the equipment in a normal environment. For example, if the normal humidity level in a designated heritage building is 60%, then It is set to 60. This value is usually derived from the design requirements of the equipment or the environmental standards of cultural relics buildings. It is set from the technical specifications of the equipment, cultural relics protection requirements or conventional environmental conditions. If there is no specific standard, it is set based on historical data or the experience of cultural relics buildings. It is used to adjust the range of influence of humidity on temperature threshold, indicating the influence of humidity deviation on temperature threshold. The setting is obtained by modeling the relationship between humidity and temperature, or adjusting it according to industry standards, or obtaining it through simulation experiments or regression analysis of historical data. For example, a study shows that every 10% change in humidity will affect the temperature threshold by 2°C, so Will be set to 2.

[0045] It should also be noted that after the set time period is from the initial operation time to the collection time point The length of time between Used to indicate The aging degree of the equipment Increased device usage time, The insulation performance of the equipment will be reduced, resulting in increased leakage current. pass The usage time of the device is calculated dynamically, for example, if If the device is used for 10 hours, pass To calculate, yes The aging time constant of the equipment is obtained through experimental data, The closer it is to 1, the newer the device is. The closer it is to 0, the older the device.

[0046] It should be noted that in cultural relics buildings, high humidity may accelerate the corrosion of materials such as wood and paper and reduce the insulation of the materials. This is related to the potential danger of rising temperatures. Therefore, when the humidity rises, it may be necessary to lower the temperature threshold to increase the sensitivity of fire warning.

[0047] In a specific embodiment, the analysis of the fire risk probability corresponding to the specified cultural relic building is carried out as follows: the fire risk data corresponding to the specified cultural relic building is obtained from the database, the fire risk data is used as a training set for training a lightweight fire prediction model, and then the lightweight fire prediction model is used to predict the fire risk probability corresponding to the specified cultural relic building, and the local loss function is used to evaluate whether the prediction error corresponding to the fire risk probability in the specified cultural relic building meets the requirements.

[0048] It should be noted that fire risk data includes various factors that affect fire risk, such as sensor data such as temperature, humidity, leakage current and current harmonics. The specific process of predicting the corresponding fire risk probability in a specified cultural relic building through a lightweight fire prediction model has been described in the patent with announcement number CN117745053A and will not be elaborated here.

[0049] In a specific embodiment, the local loss function is used to evaluate whether the prediction error corresponding to the fire risk probability in the designated cultural relic building meets the requirements. The specific process is as follows: the actual label and the predicted value of the lightweight fire prediction model corresponding to each time in the historical set time period are queried from the database, and are respectively recorded as and , evaluated by the local loss function formula: , calculate the loss function value corresponding to the fire risk probability in the specified cultural relic building ,in Represents the corresponding time points within the historical set time period, that is, the index of the historical time step. , is the total number of time steps, is a positive integer, is the set regularization coefficient, These are the parameters corresponding to the set lightweight fire prediction model.

[0050] It should be noted that Used to control the complexity of the model and prevent overfitting, It is determined through experience or cross-validation and selected through experimentation or tuning, for example, by setting different Value, Observation The impact on model performance, and then select the best value, in machine learning, It is the set of all parameters of the lightweight fire prediction model, which determines the prediction results of the lightweight fire prediction model. For example, in the linear regression model, Including weights and bias terms, that is, the coefficients of features, in neural networks, It contains the weights and biases of each layer in the network, which are continuously adjusted during the training process through optimization algorithms, such as gradient descent. Initially, the values of the parameters are set randomly, and then, during the training process, the algorithm continuously updates these parameters by minimizing the loss function until the model achieves optimal performance.

[0051] In the fire risk assessment process, the embodiment of the present invention uses a lightweight fire prediction model to analyze the fire risk probability and uses a local loss function to evaluate the prediction error, which is beneficial to improving the scientific nature and reliability of fire risk assessment, reducing the consumption of computing resources, improving computing efficiency, and facilitating the rapid processing of large amounts of data and outputting accurate risk probabilities.

[0052] S4. Emergency decision-making assessment: Adjust the corresponding evidence storage frequency in the designated cultural relic building based on the corresponding fire risk probability in the designated cultural relic building.

[0053] In a specific embodiment, the frequency of evidence storage in a designated cultural relic building is adjusted as follows: the fire risk probability in the designated cultural relic building is predicted based on a lightweight fire prediction model, and the calculation formula is: , get the evidence frequency of the fire risk probability corresponding to the specified cultural relic building ,in It represents the lower limit of the frequency of evidence storage corresponding to the preset designated cultural relic building. is the weighted coefficient between the set fire risk probability and the evidence storage frequency, It is expressed as the fire risk probability corresponding to the designated cultural relic building.

[0054] It should be noted that It is usually set to a constant of 1 or greater to ensure that the assessment factor will not be 0 or negative even when the fire risk is very low. The setting can refer to the security standards in similar fields or be based on the actual needs of the system. For example, if the minimum protection level of the system is 1, it is set to 1. The system will be adjusted based on historical data or experiments. By analyzing historical fire accident data or conducting system simulation tests, the impact of fire risk probability on system safety decisions will be determined. If the probability of fire is high, the system will increase For example, if historical data shows that when the probability of fire risk increases by 10%, the response capacity of the blocking measures increases by 2%, then set It is 0.2, that is, for every 10% increase in fire probability, the blocking factor increases by 2%.

[0055] In the emergency decision-making process, the embodiment of the present invention adjusts the corresponding evidence storage frequency in the designated cultural relic building according to the fire risk probability, which is conducive to optimizing the configuration of data storage resources. When the fire risk is low, the evidence storage frequency is reduced to avoid the waste of storage space. When the fire risk is high, the evidence storage frequency is increased to ensure that key data can be fully recorded, which is convenient for tracing and analyzing the cause after the fire accident occurs. The dynamic adjustment of the evidence storage frequency reduces data storage costs and improves data management efficiency while ensuring data integrity and validity.

[0056] See also Figure 2 The electrical fire fighting Internet of Things monitoring system shown includes the following modules: a multimodal sensor deployment module, an environment adaptive signal processing module, a fire risk assessment module, an emergency decision assessment module and a database.

[0057] The multimodal sensor deployment module is connected to the environment adaptive signal processing module and the database respectively, the environment adaptive signal processing module is connected to the fire risk assessment module and the database respectively, and the fire risk assessment module is connected to the emergency decision assessment module and the database respectively.

[0058] The multimodal sensor deployment module is used to achieve self-sustaining operation of sensors through composite energy harvesting when wiring or stable power supply is not possible in designated cultural relic buildings.

[0059] The environmental adaptive signal processing module is used to collect the corresponding interference factors in the designated cultural relic building when the sensor achieves self-sustaining operation, and then evaluate whether it is necessary to adjust the assessment threshold corresponding to the fire impact parameter.

[0060] The fire risk assessment module is used to analyze the corresponding fire risk probability in a specified cultural relic building based on the adjusted temperature threshold and leakage current threshold when the assessment threshold corresponding to the fire impact parameter needs to be adjusted.

[0061] The emergency decision-making assessment module is used to adjust the corresponding evidence storage frequency in the designated cultural relic building according to the corresponding fire risk probability in the designated cultural relic building.

[0062] The database is used to store the minimum operating power required by each sensor in the passive multimodal sensor, the importance of each sensor in the passive multimodal sensor, the preset composite interference factor threshold corresponding to the designated cultural relic building, the basic temperature threshold corresponding to the designated cultural relic building, the upper leakage current threshold corresponding to each electrical device in the designated cultural relic building in the initial state and the lower leakage current threshold corresponding to the device after running for a set time period, the fire risk data corresponding to the designated cultural relic building, and the actual label corresponding to each time in the historical set time period and the predicted value of the lightweight fire prediction model. An embodiment of the present invention provides an electrical fire and firefighting Internet of Things monitoring method and system. During the sensor deployment and power supply process, a composite energy collection method of temperature difference collection, vibration collection, and light energy collection is adopted. This helps to solve the problem of being unable to lay cables or provide a stable power supply in special scenarios such as designated cultural relics buildings, and realizes the self-sustaining operation of passive multimodal sensors. This power supply method gets rid of the dependence on traditional power grids, greatly broadens the application scope of the monitoring system, and enables the system to operate stably in complex environments such as ancient buildings and remote areas. When the total power supply is insufficient, power is allocated in low-power mode according to the importance of each sensor, which helps to ensure the normal operation of key sensors and guarantee the effectiveness and reliability of monitoring data collection under limited energy conditions.

[0063] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. A method for monitoring electrical fires using the Internet of Things, characterized in that: include: S1. Multimodal sensor deployment: When wiring or stable power supply is not possible within a designated cultural relic building, self-sustaining operation of the sensor is achieved through composite energy harvesting. S2, Environmental Adaptive Signal Processing: When the sensor achieves self-sustaining operation, it collects the corresponding interference factors in the designated cultural relic building and then evaluates whether the assessment threshold corresponding to the fire impact parameter needs to be adjusted; S3. Fire risk assessment: When it is necessary to adjust the assessment thresholds corresponding to the fire impact parameters, analyze the corresponding fire risk probability in the designated cultural relic building based on the adjusted temperature thresholds and leakage current thresholds; S4. Emergency decision-making assessment: Adjust the corresponding evidence storage frequency in the designated cultural relic building based on the corresponding fire risk probability in the designated cultural relic building.

2. The electrical fire protection Internet of Things monitoring method according to claim 1, characterized in that: The self-sustaining operation of the sensor is achieved by the composite energy harvesting method. The specific process is as follows: The composite energy collection method includes temperature difference collection, vibration collection and light energy collection. Passive multimodal sensors are deployed in designated cultural relics buildings. The self-sustaining operation of the passive multimodal sensors is achieved through temperature difference collection, vibration collection and light energy collection. The temperature difference power generation power, vibration energy conversion power and light energy supplement power corresponding to the temperature difference collection method, vibration collection method and light energy collection method are calculated respectively. The total power supply power is obtained by adding the temperature difference power generation power, vibration energy conversion power and light energy supplement power. , query the minimum operating power required by each sensor in the passive multimodal sensor from the database, and accumulate the required minimum total operating power ,when Greater than When , it indicates that the passive multimodal sensor does not need to enter low power mode. Less than or equal to When , it indicates that the passive multimodal sensor needs to enter low power consumption mode to achieve self-sustaining operation of the passive multimodal sensor deployed in the designated cultural relic building.

3. The electrical fire fighting Internet of Things monitoring method according to claim 2, characterized in that: The passive multimodal sensor needs to be in low power consumption mode. The specific process is as follows: When the passive multimodal sensor needs to enter low power mode, the importance of each sensor in the passive multimodal sensor is queried from the database. Based on the minimum operating power required by each sensor, the sensors are sorted from the most important to the least important. The minimum operating power corresponding to each sensor is added in order according to the sorting order, and the sum of the minimum operating powers obtained by each addition is added. Compare, if the total is The total minimum operating power corresponding to the sensors is less than , accumulated to the The sum of the minimum operating power corresponding to the sensors is greater than , the first The sensor includes the The operating power required for each sensor, is the number corresponding to each sensor in the passive multimodal sensor, The value of is a positive integer.

4. The electrical fire fighting Internet of Things monitoring method according to claim 3, characterized in that: The specific process of collecting the corresponding interference factors in the designated cultural relic building is as follows: Interference factors include current harmonic distortion rate and light-scattering particle concentration. Each collection time point is set, and the current signal of the power grid or electrical appliance at each collection time point is monitored by a current sensor. The edge computing device uses fast Fourier transform to transform the current signal collected at each collection time point, and then converts the current signal at each collection time point, which exists as a time domain signal, into a frequency domain signal. The harmonic components are extracted from the frequency domain signal, and the current harmonic distortion rate of the current signal corresponding to each collection time point is calculated. The light scattering intensity corresponding to each collection time point is obtained by a laser particle counter, and the light scattering particle concentration corresponding to each collection time point is obtained after conversion.

5. The electrical fire fighting Internet of Things monitoring method according to claim 4, characterized in that: The specific process of evaluating whether to adjust the assessment threshold corresponding to the fire impact parameter is as follows: By calculation formula: , get the collection time point of the specified cultural relic building Corresponding composite interference factor ,in is the number corresponding to each collection time point, The value is a positive integer. and Represented as the collection time point The corresponding current harmonic distortion rate and light scattering particle concentration, and are the weight factors corresponding to the set current harmonic distortion rate and the weight factors corresponding to the light scattering particle concentration; Query the preset composite interference factor threshold value of the designated cultural relic building from the database, and Compared with the composite interference factor threshold, if If it is greater than or equal to the composite interference factor threshold, it indicates that the evaluation threshold corresponding to the fire impact parameter needs to be adjusted. Otherwise, it indicates that the evaluation threshold corresponding to the fire impact parameter does not need to be adjusted. The evaluation threshold adjustment corresponding to the fire impact parameter includes temperature threshold adjustment and leakage current threshold adjustment.

6. The electrical fire fighting Internet of Things monitoring method according to claim 5, characterized in that: The assessment threshold corresponding to the fire impact parameter needs to be adjusted. The specific adjustment process is as follows: Query the corresponding basic temperature threshold in the specified cultural relic building from the database , and then adjust the formula through the temperature threshold: , obtained at the collection time point Corresponding adjusted temperature threshold ,in is the set humidity adjustment coefficient, Represented by the humidity sensor at the collection time point The ambient humidity, is the set benchmark value, is the humidity scaling factor, It is expressed as the temperature change rate corresponding to the set historical time period; The upper limit threshold of leakage current corresponding to each electrical device in the specified cultural relic building in the initial state and the lower limit threshold of leakage current corresponding to the set time period are queried from the database and recorded as and , through the leakage current threshold adjustment formula: , obtained at the collection time point Corresponding to Adjusted leakage current threshold of electrical equipment ,in is the number corresponding to each electrical equipment, The value is a positive integer. For the setting The aging factor corresponding to each electrical equipment.

7. The electrical fire protection Internet of Things monitoring method according to claim 6, characterized in that: The analysis specifies the fire risk probability within a cultural relic building. The specific process is as follows: The fire risk data corresponding to the specified cultural relic building is obtained from the database, and the fire risk data is used as the training set for training the lightweight fire prediction model. The lightweight fire prediction model is then used to predict the corresponding fire risk probability in the specified cultural relic building, and the local loss function is used to evaluate whether the prediction error corresponding to the corresponding fire risk probability in the specified cultural relic building meets the requirements.

8. The electrical fire fighting Internet of Things monitoring method according to claim 7, characterized in that: The local loss function is used to evaluate whether the prediction error corresponding to the fire risk probability in the specified cultural relic building meets the requirements. The specific process is as follows: The actual labels and the predicted values of the lightweight fire prediction model corresponding to each time in the historical set time period are queried from the database and recorded as and , evaluated by the local loss function formula: , calculate the loss function value corresponding to the fire risk probability in the specified cultural relic building ,in Represents the corresponding time points within the historical set time period, that is, the index of the historical time step. , is the total number of time steps, is a positive integer, is the set regularization coefficient, These are the parameters corresponding to the set lightweight fire prediction model.

9. The electrical fire fighting Internet of Things monitoring method according to claim 8, characterized in that: The specific process of adjusting the frequency of evidence storage in a designated cultural relic building is as follows: The fire risk probability corresponding to the specified cultural relic building is predicted based on the lightweight fire prediction model, and the calculation formula is: , get the evidence frequency of the fire risk probability corresponding to the specified cultural relic building ,in It represents the lower limit of the frequency of evidence storage corresponding to the preset designated cultural relic building. is the weighted coefficient between the set fire risk probability and the evidence storage frequency, It is expressed as the fire risk probability corresponding to the designated cultural relic building.

10. An electrical fire fire Internet of Things monitoring system for executing the electrical fire fire Internet of Things monitoring method according to any one of claims 1 to 9, characterized in that: Includes the following modules: A multimodal sensor deployment module, designed to enable self-sustaining sensor operation through hybrid energy harvesting when wiring or stable power supply is unavailable within designated heritage buildings; The environmental adaptive signal processing module is used to collect the corresponding interference factors in the designated cultural relic building when the sensor achieves self-sustaining operation, and then evaluate whether the assessment threshold corresponding to the fire impact parameter needs to be adjusted; The fire risk assessment module is used to analyze the fire risk probability within a designated cultural relic building based on the adjusted temperature threshold and leakage current threshold when the assessment threshold corresponding to the fire impact parameter needs to be adjusted; The emergency decision-making assessment module is used to adjust the corresponding evidence storage frequency in the designated cultural relic building according to the corresponding fire risk probability in the designated cultural relic building.

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