A building fire safety assessment and analysis method and system

Abstract

The present invention discloses a building fire safety assessment and analysis method and system, which specifically relates to the technical field of building fire safety analysis. The method divides the interior of a building into multiple monitoring areas, utilizes signal processing technology to analyze the temperature data of wall materials, identifies and locates temperature anomalies, and immediately analyzes the appearance and structural characteristics of the wall materials once an anomaly is found, evaluates the integrity of the wall material structure, and simultaneously determines the fluctuation of the wall material performance and evaluates the stability of the wall material. Then, the structural integrity and performance stability of the wall materials are comprehensively analyzed, and the degree of aging is evaluated. Based on the evaluation results, the wall materials in different monitoring areas in the building are classified and processed, and the materials that may be abnormally aged are further analyzed and managed. Corresponding treatment measures are taken for each type of material, which can effectively reduce the risk of fire and other safety problems in the building, improve the building fire safety level, and protect people's lives.

Description

Technical Field

[0001] The present invention relates to the technical field of building fire safety analysis, and in particular to a building fire safety assessment and analysis method and system. Background Art

[0002] A building fire safety assessment is a comprehensive and systematic evaluation and analysis of a building's fire safety status. This process includes a review of the building structure, firefighting equipment, escape routes, and fire risk factors to determine the building's ability to respond and safety in the event of a fire. This assessment identifies potential fire hazards and safety risks and provides recommendations for improvements to ensure timely and effective evacuation and firefighting in the event of a fire, minimizing casualties and property losses.

[0003] The shortcomings of the existing technology:

[0004] Over time, wall materials used within buildings may age, and some of these materials become more flammable during this aging process. For example, prolonged exposure to sunlight and oxidation can cause wall materials to become dry and brittle, losing their original fire resistance. This increases the risk of fire spreading rapidly in the event of a fire. Therefore, when assessing building fire safety, failing to accurately analyze the degree of aging within a building's wall materials and prematurely treating these materials can increase the risk of combustion in the event of a fire, increasing the risk of casualties. Summary of the Invention

[0005] The purpose of the present invention is to provide a building fire safety assessment and analysis method and system to address the shortcomings of the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a building fire safety assessment and analysis method, comprising the following steps:

[0007] S1: Divide the interior of the building into n monitoring areas, analyze the wall material temperature data through signal processing technology, and identify and locate temperature anomalies based on the analysis results;

[0008] S2: When abnormal temperature points appear in the wall material, analyze the appearance and structural characteristics of the wall material, determine the overall degree of change in the physical characteristics of the wall material, and evaluate the integrity of the wall material structure;

[0009] S3: Determine the performance fluctuation of the wall material, compare and analyze the original performance data of the material with the real-time performance data, and evaluate the stability of the wall material performance;

[0010] S4: Comprehensively analyze the structural integrity of the wall materials and the stability of the wall material performance to assess the abnormal degree of aging of the wall materials;

[0011] S5: Based on the assessment results, the wall materials in different monitoring areas inside the building are divided into abnormally aged materials, potentially abnormally aged materials, and normally aged materials, and are treated accordingly;

[0012] S6: When there are materials that may be abnormally aged in the monitoring area, the changes in the aging degree of the materials that may be abnormally aged over a period of time are analyzed, and the risks caused by material abnormalities are predicted in advance and managed intelligently.

[0013] In a preferred embodiment, in S1, the temperature data of the wall material is analyzed using signal processing technology, and the temperature data is analyzed using wavelet transform technology to identify and locate temperature anomalies, specifically:

[0014] A temperature sensor is installed in each monitoring area to collect temperature data T(t) in real time, where (t) represents time. The collected temperature data is preprocessed and the preprocessed temperature data is marked as Tclean(t);

[0015] Select the mother wavelet function ψ(t) and perform discrete wavelet transform on the preprocessed temperature data Tclean(t). The specific calculation expression is: ;in, is the wavelet coefficient, a is the scale parameter, b is the translation parameter, and ψ is the mother wavelet function;

[0016] Perform multi-scale wavelet transform to decompose the temperature data into detail and approximation components of different scales. The calculation expression of wavelet decomposition is: ;in, is the approximation coefficient, is the detail coefficient, and are scaling function and wavelet function respectively;

[0017] Wavelet transform decomposes the original temperature data Tclean(t) into approximation and detail components of different scales. The coefficients of the detail components are It reflects the change of the signal at scale j and position k; the obtained wavelet detail coefficient is compared with the wavelet detail coefficient reference threshold. If the wavelet detail coefficient is greater than or equal to the wavelet detail coefficient reference threshold, it is considered There is a temperature anomaly point at this point, and a temperature anomaly signal is generated, and the position of the wall material where the temperature anomaly point exists is marked; if the wavelet detail coefficient is less than the wavelet detail coefficient reference threshold, it is considered There is no abnormal temperature point, and a normal temperature signal is generated at this time, and the wall material is not marked.

[0018] In a preferred embodiment, in S2, the appearance and structural characteristics of the wall material are analyzed to determine the overall degree of change in the physical characteristics of the wall material appearance. Based on the range and depth of corrosion on the wall material surface and signs of material aging, an abnormal corrosion rate index of the wall material surface is obtained to evaluate the integrity of the wall material structure. The method for obtaining the abnormal corrosion rate index is as follows:

[0019] Obtain the time series data of the surface corrosion of the wall material, perform the first-order difference operation on the time series data of the corrosion degree, and obtain the time series data of the corrosion rate. The first-order difference represents the rate of change between adjacent time points. The specific calculation expression is: Where, Indicates at a point in time The first-order difference value of Indicates at a point in time Corrosion degree data at Indicates at a point in time Corrosion degree data at the site;

[0020] Detect abnormal points in the corrosion rate time series. Abnormal points indicate abnormal fluctuations in the material corrosion rate. Calculate the material corrosion rate change value. The specific calculation expression is: ;in, is the mean of the difference values, is the standard deviation of the differences, is the change value of material corrosion rate;

[0021] According to the calculated material corrosion rate change value, the corrosion rate abnormality index is calculated. The specific calculation expression is: ; where N is the number of outliers, is the weight of the i-th outlier, is the corrosion rate anomaly index;

[0022] The obtained corrosion rate anomaly index is compared with the corrosion rate anomaly index reference threshold. If the corrosion rate anomaly index is greater than or equal to the corrosion rate anomaly index reference threshold, a material integrity anomaly signal is generated; if the corrosion rate anomaly index is less than the corrosion rate anomaly index reference threshold, no material integrity anomaly signal is generated.

[0023] In a preferred embodiment, in S3, the original performance data and the real-time performance data of the wall material are compared and analyzed, and the performance deviation fluctuation index of the wall material is obtained according to the performance change trend between the original performance data and the real-time performance data to evaluate the stability of the wall material performance.

[0024] In a preferred embodiment, the method for obtaining the performance deviation fluctuation index is:

[0025] The method for obtaining the performance deviation fluctuation index is as follows: collect the original performance data and real-time performance data of the wall material and establish corresponding data sets respectively, wherein the original performance data set is ; Real-time performance data collection is ;in, , is a positive integer greater than 0; the average values ​​of the original performance data and the real-time performance data are calculated respectively, and are expressed as and ; and calculate the square difference SSX of the original performance data and the square difference SSY of the real-time performance data; calculate the intra-group sum of squares between the square difference of the real-time performance data and the square difference of the original performance data, the intra-group sum of squares represents the degree of fluctuation within the original performance data and the real-time performance data, and is obtained by summing the square differences of each group of data; and calculate the inter-group sum of squares between the square difference of the real-time performance data and the square difference of the original performance data, the inter-group sum of squares represents the degree of difference between the original performance data and the real-time performance data; calculate the ratio of the inter-group sum of squares to the intra-group sum of squares, compare the degree of difference between the groups with the degree of fluctuation within the groups, and use it to calculate the performance deviation fluctuation index;

[0026] Calculate the ratio of the sum of squares between groups to the sum of squares within groups, compare the degree of difference between groups and the degree of fluctuation within groups, and use it to calculate the performance deviation fluctuation index. The specific calculation expression is: Where, is the performance deviation fluctuation index;

[0027] The obtained performance deviation fluctuation index of the wall material is compared with the performance deviation fluctuation index reference threshold. If the performance deviation fluctuation index of the wall material is greater than or equal to the performance deviation fluctuation index reference threshold, a material performance abnormality signal is generated; if the performance deviation fluctuation index of the wall material is less than the performance deviation fluctuation index reference threshold, no material performance abnormality signal is generated.

[0028] In a preferred embodiment, in S4, the integrity of the wall material structure and the stability of the wall material performance are comprehensively analyzed, specifically:

[0029] The corrosion rate anomaly index and the performance deviation fluctuation index are normalized, and the abnormal assessment coefficient of the aging degree of the wall material is calculated based on the normalized corrosion rate anomaly index and performance deviation fluctuation index.

[0030] In a preferred embodiment, in S5, based on the evaluation results, the wall materials in different monitoring areas inside the building are divided into abnormally aged materials, potentially abnormally aged materials, and normally aged materials, specifically:

[0031] Comparing the obtained abnormality assessment coefficient of the degree of aging of the wall material with a gradient threshold, where the gradient threshold includes a first threshold and a second threshold, and the first threshold is less than the second threshold, and comparing the abnormality assessment coefficient of the degree of aging of the wall material with the first threshold and the second threshold respectively;

[0032] If the abnormal evaluation coefficient of the aging degree of the wall material is greater than the second threshold, it is classified as abnormal aging material, and the area of ​​the abnormal aging material is marked as an abnormal monitoring area;

[0033] If the abnormality assessment coefficient of the aging degree of the wall material is greater than or equal to the first threshold and less than or equal to the second threshold, it is classified as a possible abnormal aging material, and the area of ​​the possible abnormal aging material is marked as a possible abnormal monitoring area;

[0034] If the abnormal evaluation coefficient of the aging degree of the wall material is less than the first threshold, it is classified as normal aging material, and the area of ​​the normal aging material is marked as a normal monitoring area.

[0035] In a preferred embodiment, in S6, when there is a possible abnormally aged material in the monitoring area, that is, the abnormal assessment coefficient of the aging degree of the wall material is greater than or equal to the first threshold and less than or equal to the second threshold within a period of time, the abnormal assessment coefficient of the aging degree of the wall material in a subsequent period of time is collected, and a corresponding data set is established. The abnormal assessment coefficients of the aging degree of the wall material in the data set are analyzed, the mean of the abnormal assessment coefficients of the aging degree of the wall material in the data set is calculated, and a smoothing coefficient F is determined;

[0036] Abnormal assessment coefficient of the aging degree of wall materials obtained at time point T , use the exponential smoothing formula to calculate the abnormal assessment coefficient fitting value. The specific calculation expression is: Where, is the fitted value of the abnormal assessment coefficient, for Abnormal evaluation coefficient at the moment;

[0037] The calculated abnormal assessment coefficient fitting value As The abnormal assessment coefficient of the prediction at each moment is continuously updated, and a smooth abnormal assessment coefficient prediction trend line is drawn according to the calculated fitting value sequence. The abnormal assessment coefficient of the aging degree of the wall material obtained in real time from the data set is also used to draw a smooth abnormal assessment coefficient real-time trend line, and the two smooth trend lines are compared and analyzed.

[0038] In a preferred embodiment, the values ​​of the predicted trend line and the real-time trend line at time point T are analyzed. If the predicted anomaly assessment coefficient on the predicted trend line is greater than or equal to the safety threshold, and at the same time, the real-time anomaly assessment coefficient on the real-time trend line is greater than or equal to the safety threshold, a first-level warning signal is issued; if the predicted anomaly assessment coefficient on the predicted trend line and the real-time anomaly assessment coefficient on the real-time trend line meet that one is greater than or equal to the safety threshold and the other is less than the safety threshold, a second-level warning signal is issued; if the predicted anomaly assessment coefficient on the predicted trend line is less than the safety threshold, and at the same time, the real-time anomaly assessment coefficient on the real-time trend line is also less than the safety threshold, no warning signal is issued.

[0039] The present invention also provides a building fire safety assessment and analysis system, which includes a signal processing module, an appearance and structure feature analysis module, a performance fluctuation analysis module, a comprehensive analysis module, a material classification module, and a prediction and warning module;

[0040] Signal processing module: Divides the interior of the building into n monitoring areas, analyzes the wall material temperature data through signal processing technology, and identifies and locates temperature anomalies based on the analysis results;

[0041] Appearance and structural characteristics analysis module: When abnormal temperature points appear in the wall material, the appearance and structural characteristics of the wall material are analyzed to determine the overall degree of change in the physical characteristics of the wall material appearance and evaluate the integrity of the wall material structure;

[0042] Performance Fluctuation Analysis Module: Determines the performance fluctuation of wall materials, compares and analyzes the original performance data of the materials with the real-time performance data, and evaluates the stability of the wall material performance;

[0043] Comprehensive analysis module: conducts comprehensive analysis on the structural integrity and performance stability of wall materials, and evaluates the abnormal degree of aging of wall materials;

[0044] Material classification module: Based on the assessment results, the wall materials in different monitoring areas inside the building are divided into abnormally aged materials, potentially abnormally aged materials, and normally aged materials, and are treated accordingly;

[0045] Prediction and early warning module: When there are materials that may be abnormally aged in the monitoring area, the aging degree changes of the materials that may be abnormally aged over a period of time are analyzed, and the risks caused by material abnormalities are predicted in advance and managed intelligently.

[0046] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0047] 1. By analyzing the temperature data and performance of wall materials, this invention can identify and locate potential aging issues early on, effectively preventing safety risks such as fire. Secondly, categorizing materials with varying degrees of aging facilitates the rational allocation of resources, prioritizing abnormally aged materials with higher potential risks, thereby maximizing safety and resource utilization efficiency. Most importantly, through intelligent management and prediction mechanisms, timely measures can be taken to address potential changes in risks, effectively reducing the probability of fire and ensuring safety within buildings.

[0048] 2. Through early prediction and intelligent management, this invention significantly reduces the probability of fire, effectively avoiding safety hazards caused by aging wall materials. Furthermore, the timely treatment of abnormally aged materials and increased monitoring frequency further enhance the ability to perceive and respond to potential risks, providing reliable technical support for building fire safety management. Overall, the implementation of this solution not only improves the level of building fire safety but also provides building managers with intelligent management tools, making internal building safety management more precise and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0050] Figure 1 Flow chart of the method of the present invention.

[0051] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] Example 1:

[0054] See also Figure 1 As shown, the building fire safety assessment and analysis method described in this embodiment includes the following steps:

[0055] S1: Divide the interior of the building into n monitoring areas, analyze the wall material temperature data through signal processing technology, and identify and locate temperature anomalies based on the analysis results;

[0056] S2: When abnormal temperature points appear in the wall material, analyze the appearance and structural characteristics of the wall material, determine the overall degree of change in the physical characteristics of the wall material, and evaluate the integrity of the wall material structure;

[0057] S3: Determine the performance fluctuation of the wall material, compare and analyze the original performance data of the material with the real-time performance data, and evaluate the stability of the wall material performance;

[0058] S4: Comprehensively analyze the structural integrity of the wall materials and the stability of the wall material performance to assess the abnormal degree of aging of the wall materials;

[0059] S5: Based on the assessment results, the wall materials in different monitoring areas inside the building are divided into abnormally aged materials, potentially abnormally aged materials, and normally aged materials, and are treated accordingly;

[0060] S6: When there are materials that may be abnormally aged in the monitoring area, the changes in the aging degree of the materials that may be abnormally aged over a period of time are analyzed, and the risks caused by material abnormalities are predicted in advance and managed intelligently.

[0061] In S1, the interior of the building is divided into n monitoring areas. The wall material temperature data is analyzed through signal processing technology. Based on the analysis results, the temperature anomalies are identified and located. Specifically:

[0062] The entire building interior is divided into n independent monitoring zones, each of which is monitored and evaluated individually. Temperature sensors are installed on the walls within each monitoring zone to monitor real-time temperature changes within the wall material. Temperature sensors can include infrared temperature sensors and thermocouples, capable of accurately detecting even small temperature changes.

[0063] The collected temperature data is analyzed using signal processing technology, and the temperature data is analyzed using wavelet transform technology to identify and locate temperature anomalies. Specifically:

[0064] Temperature sensors are installed in each monitoring area to collect real-time temperature data T(t), where (t) represents time;

[0065] The collected temperature data is preprocessed, including noise removal, missing data filling, etc. The preprocessed temperature data is marked as Tclean(t).

[0066] Select a suitable mother wavelet function ψ(t), such as Daubechies wavelet, Haar wavelet, etc.

[0067] The pre-processed temperature data Tclean(t) is subjected to discrete wavelet transform. The specific calculation expression is: ;in, is the wavelet coefficient, a is the scale parameter, b is the translation parameter, and ψ is the mother wavelet function.

[0068] Perform multi-scale wavelet transform to decompose the temperature data into detail (high frequency) and approximation (low frequency) components of different scales. The calculation expression of wavelet decomposition is: ;in, is the approximation coefficient, is the detail coefficient, and are scaling function and wavelet function respectively.

[0069] Wavelet transform decomposes the original temperature data Tclean(t) into approximation and detail components of different scales. The coefficients of the detail components are It reflects the change of the signal at a specific scale j and position k. These detail coefficients can reveal sudden changes or anomalies in the original signal. For example, in temperature data, if the temperature at a certain point in time suddenly rises or falls, the related wavelet detail coefficients will deviate significantly from normal values.

[0070] The obtained wavelet detail coefficient is compared with the wavelet detail coefficient reference threshold. If the wavelet detail coefficient is greater than or equal to the wavelet detail coefficient reference threshold, it is considered There is a temperature anomaly point at this point, and a temperature anomaly signal is generated, and the position of the wall material where the temperature anomaly point exists is marked; if the wavelet detail coefficient is less than the wavelet detail coefficient reference threshold, it is considered There is no abnormal temperature point, and a normal temperature signal is generated at this time, and the wall material is not marked.

[0071] It should be noted here that Indicates the time and location of abnormal wall material temperature. These abnormal points can be determined by consulting the sensor layout diagram. The corresponding physical location, where m is a positive integer greater than 0. For example, The corresponding sensor is on the north wall of room A. The corresponding sensor is on the east wall of room B.

[0072] Through wavelet transform technology, not only can the time of temperature anomalies be identified, but also the specific location of temperature anomalies in the building can be determined in combination with sensor location information, providing accurate information support for building fire safety assessment and management.

[0073] S2: When abnormal temperature points appear in the wall material, analyze the appearance and structural characteristics of the wall material to determine the overall degree of change in the physical characteristics of the wall material appearance and evaluate the integrity of the wall material structure.

[0074] Conduct on-site inspections at locations where temperature anomalies are identified. Use high-resolution cameras, microscopes, or other image acquisition equipment to capture and record detailed images of the wall material surface, obtaining high-definition image data of the material's appearance and structure.

[0075] Analyze the collected image data, focusing on physical changes in the material's appearance, such as cracks, discoloration, bulging, flaking, and corrosion. Image processing and analysis techniques are used to extract the texture, morphology, and color characteristics of the material's surface, identifying any abnormal physical changes. For example, image edge detection can identify cracks and flaking, while color analysis can detect discoloration and corrosion.

[0076] Compare the current material appearance and structural characteristics with the standard characteristics under normal conditions to determine whether the material's physical characteristics have changed significantly. Quantitative indicators of abnormal changes, such as crack length, spalling area, and discoloration, are established to assess the extent of the change.

[0077] Analyze the appearance and structural characteristics of the wall material to determine the overall degree of change in the physical characteristics of the wall material appearance. According to the scope and depth of the wall material surface corrosion and the signs of material aging, obtain the abnormal corrosion rate index of the wall material surface to evaluate the integrity of the wall material structure. The method for obtaining the abnormal corrosion rate index is as follows:

[0078] Obtain time series data on the surface corrosion of wall materials, including indicators such as corrosion degree, corrosion range, and corrosion depth at different time points;

[0079] Perform the first-order difference operation on the time series data of corrosion degree to obtain the time series data of corrosion rate. The first-order difference represents the rate of change of adjacent time points and can reflect the changing trend of corrosion rate. The specific calculation expression is: Where, Indicates at a point in time The first-order difference value of Indicates at a point in time Corrosion degree data at Indicates at a point in time Corrosion degree data at the site;

[0080] Detect abnormal points in the corrosion rate time series. Abnormal points may indicate sudden changes or abnormal fluctuations in the material corrosion rate. Calculate the material corrosion rate change value. The specific calculation expression is: ;in, is the mean of the difference values, is the standard deviation of the differences, is the change value of material corrosion rate;

[0081] According to the calculated material corrosion rate change value, the corrosion rate abnormality index is calculated. The specific calculation expression is: ; where N is the number of outliers, is the weight of the i-th outlier, which can be set according to the importance of the outlier. is the corrosion rate anomaly index.

[0082] The obtained corrosion rate anomaly index is compared with the corrosion rate anomaly index reference threshold. If the corrosion rate anomaly index is greater than or equal to the corrosion rate anomaly index reference threshold, it means that the signs of material aging are more obvious and the integrity of the material structure is lower. At this time, a material integrity anomaly signal is generated to indicate the necessity of repair or replacement; if the corrosion rate anomaly index is less than the corrosion rate anomaly index reference threshold, it means that the signs of material aging are less obvious and the integrity of the material structure is higher. At this time, no material integrity anomaly signal is generated.

[0083] When the corrosion rate anomaly index is larger, it indicates that the structural integrity of the wall material is lower, that is, the material is more susceptible to corrosion and damage, and more timely maintenance or replacement measures are needed.

[0084] S3: Determine the performance fluctuation of the wall material, compare and analyze the original performance data and real-time performance data of the material, and evaluate the stability of the wall material performance.

[0085] Obtain raw performance data for wall materials. This data is collected during material production, installation, or initial evaluation. This data represents the material's initial performance, potentially including strength, wear resistance, and fire resistance. Also, obtain real-time performance data for wall materials. This data is collected during building operation, regular maintenance, or periodic evaluations. Obtaining current material performance through real-time monitoring or regular inspections can reveal changes in the material during use.

[0086] Comparing and analyzing raw and real-time performance data is primarily to observe differences and trends. If the real-time performance data differs significantly from the raw data, or if there are significant fluctuations, this indicates a possible problem or instability in the material's performance.

[0087] Compare and analyze the original performance data and real-time performance data of the wall material. According to the performance change trend between the original performance data and the real-time performance data, the performance deviation fluctuation index of the wall material is obtained to evaluate the stability of the wall material performance. The method for obtaining the performance deviation fluctuation index is as follows:

[0088] The method for obtaining the performance deviation fluctuation index is as follows: collect the original performance data and real-time performance data of the wall material and establish corresponding data sets respectively, wherein the original performance data set is ; Real-time performance data collection is ;in, , is a positive integer greater than 0; the average values ​​of the original performance data and the real-time performance data are calculated respectively, and are expressed as and ; and calculate the square difference SSX of the original performance data and the square difference SSY of the real-time performance data; calculate the intra-group sum of squares between the square difference of the real-time performance data and the square difference of the original performance data, the intra-group sum of squares represents the degree of fluctuation within the original performance data and the real-time performance data, which is obtained by summing the square differences of each group of data, and calculate the inter-group sum of squares between the square difference of the real-time performance data and the square difference of the original performance data, the inter-group sum of squares represents the degree of difference between the original performance data and the real-time performance data; calculate the ratio of the inter-group sum of squares to the intra-group sum of squares, compare the degree of difference between the groups with the degree of fluctuation within the groups, and use it to calculate the performance deviation fluctuation index.

[0089] The obtained performance deviation fluctuation index of the wall material is compared with the performance deviation fluctuation index reference threshold. If the performance deviation fluctuation index of the wall material is greater than or equal to the performance deviation fluctuation index reference threshold, it means that the performance data fluctuation of the wall material is large and the stability of the wall material performance is lower. At this time, a material performance abnormality signal is generated; if the performance deviation fluctuation index of the wall material is less than the performance deviation fluctuation index reference threshold, it means that the performance data fluctuation of the wall material is small and the stability of the wall material performance is higher. At this time, no material performance abnormality signal is generated.

[0090] A higher performance deviation fluctuation index indicates more pronounced fluctuations in the wall material's performance—that is, significant variations in the material's performance over a period of time. This suggests the wall material's performance is unstable and subject to significant fluctuations. Therefore, a higher performance deviation fluctuation index indicates less stable wall material performance.

[0091] Large performance fluctuations can lead to significant differences in a building's performance at different points in time, impacting its overall stability and safety. For example, in emergency situations such as fire, wall materials with large performance fluctuations may exhibit inconsistent performance, impacting the building's fire resistance and the safety of escape routes.

[0092] S4: Comprehensively analyze the structural integrity of the wall materials and the stability of the wall material performance to assess the abnormal degree of aging of the wall materials.

[0093] The corrosion rate anomaly index and the performance deviation fluctuation index are normalized, and the abnormal assessment coefficient of the aging degree of the wall material is calculated based on the normalized corrosion rate anomaly index and performance deviation fluctuation index.

[0094] For example, the present invention can use the following formula to calculate the abnormal evaluation coefficient of the aging degree of the wall material, and the calculation expression is: Where, is the abnormal assessment coefficient, is the corrosion rate anomaly index, is the performance deviation fluctuation index, are the proportional coefficients of the corrosion rate anomaly index and the performance deviation fluctuation index, respectively, and ;

[0095] It can be seen from the calculation expression that the corrosion rate anomaly index and the performance deviation fluctuation index are both positively correlated with the anomaly assessment coefficient. Moreover, as the corrosion rate anomaly index and the performance deviation fluctuation index increase, the anomaly assessment coefficient also gradually increases, that is, the possibility of abnormal aging of the wall material is higher.

[0096] S5: Based on the assessment results, the wall materials in different monitoring areas inside the building are divided into abnormally aged materials, potentially abnormally aged materials, and normally aged materials, and are treated accordingly.

[0097] Comparing the obtained abnormality assessment coefficient of the degree of aging of the wall material with a gradient threshold, where the gradient threshold in this application includes a first threshold and a second threshold, and the first threshold is less than the second threshold, and comparing the abnormality assessment coefficient of the degree of aging of the wall material with the first threshold and the second threshold respectively;

[0098] If the abnormal assessment coefficient of the aging degree of the wall material is greater than the second threshold, it indicates that the aging degree of the wall material is more likely to be abnormal. The wall material is classified as abnormally aged and needs to be replaced in time to prevent further aging from causing structural problems or fire safety hazards. The area with abnormally aged materials is marked as an abnormal monitoring area.

[0099] If the abnormality assessment coefficient of the wall material aging degree is greater than or equal to the first threshold and less than or equal to the second threshold, it indicates that the wall material aging degree is likely to be abnormal. The wall material is classified as a possible abnormal aging material, and the area with the possible abnormal aging material is marked as a possible abnormal monitoring area. At the same time, the monitoring frequency of these areas is increased to ensure that any signs of deterioration can be discovered as early as possible.

[0100] If the abnormal assessment coefficient of the aging degree of the wall material is less than the first threshold, it means that the possibility of abnormal aging degree of the wall material is lower, and it is classified as normal aging material. Routine maintenance and care work will continue to be carried out on the normal aging material to ensure that its performance remains within a safe range, and the area with normal aging material will be marked as a normal monitoring area.

[0101] In this embodiment, the interior of a building is divided into multiple monitoring areas, and the temperature data of the wall materials is analyzed using signal processing technology to identify and locate temperature anomalies. When a temperature anomaly occurs, the appearance and structural characteristics of the wall material are analyzed to evaluate its structural integrity. The original performance data and real-time performance data of the material are compared and analyzed to determine its performance stability. This data is comprehensively analyzed to evaluate the abnormal degree of aging of the wall material. Based on the evaluation results, the wall materials are divided into abnormally aged materials, possibly abnormally aged materials, and normally aged materials. Abnormally aged materials need to be replaced in a timely manner and marked as abnormal monitoring areas; possibly abnormally aged materials need to increase the monitoring frequency and be marked as possibly abnormal monitoring areas; normally aged materials continue to undergo routine maintenance and are marked as normal monitoring areas to ensure that their performance remains within a safe range.

[0102] Example 2:

[0103] S6: When there are materials that may be abnormally aged in the monitoring area, the changes in the aging degree of the materials that may be abnormally aged over a period of time are analyzed, and the risks caused by material abnormalities are predicted in advance and managed intelligently.

[0104] When analyzing the changes in the aging degree of materials that may have abnormally aged over a period of time, the exponential smoothing method is used to fit the performance data of materials that may have abnormally aged over a period of time, analyze the changing trend of the aging degree, and perform intelligent management, specifically:

[0105] When there are possible abnormally aged materials in the monitoring area, that is, the abnormal assessment coefficient of the aging degree of the wall material is greater than or equal to the first threshold and less than or equal to the second threshold within a period of time, the abnormal assessment coefficient of the aging degree of the wall material in a subsequent period of time is collected, and a corresponding data set is established, and the abnormal assessment coefficient of the aging degree of the wall material in the data set is analyzed.

[0106] Calculate the mean of the abnormal assessment coefficients of the wall material aging degree in the data set and determine the smoothing coefficient F. The choice of the smoothing coefficient F needs to be determined according to the specific situation. It usually takes a value between 0 and 1. The larger the value, the greater the weight given to past data.

[0107] Abnormal assessment coefficient of the aging degree of wall materials obtained at time point T , use the exponential smoothing formula to calculate the abnormal assessment coefficient fitting value. The specific calculation expression is: Where, is the fitted value of the abnormal assessment coefficient, for Abnormal evaluation coefficient at the moment;

[0108] The calculated abnormal assessment coefficient fitting value As The abnormal assessment coefficient is predicted at each moment, and the fitting value of the abnormal assessment coefficient is continuously updated. According to the calculated fitting value sequence, a smooth abnormal assessment coefficient prediction trend line is drawn, and a smooth abnormal assessment coefficient real-time trend line is also drawn for the abnormal assessment coefficient of the aging degree of the wall material obtained in real time from the data set. The two smooth trend lines are compared and analyzed to determine the changing direction and speed of the aging degree of the possible abnormal aging materials.

[0109] The values ​​of the predicted trend line and the real-time trend line at time point T are analyzed. If the predicted anomaly assessment coefficient on the predicted trend line is greater than or equal to the safety threshold, and the real-time anomaly assessment coefficient on the real-time trend line is also greater than or equal to the safety threshold, a level 1 warning signal is issued. It is considered that the aging risk of the material at that time point is high and urgent repair or replacement is required to prevent further deterioration and structural problems or fire safety hazards.

[0110] If the predicted anomaly assessment coefficient on the predicted trend line and the real-time anomaly assessment coefficient on the real-time trend line meet the requirement that one is greater than or equal to the safety threshold and the other is less than the safety threshold, a secondary warning signal is issued. It is considered that the aging risk of the material at that point in time is medium. Based on the aging trend, the monitoring frequency is appropriately adjusted and the monitoring density is increased to ensure that any changes are discovered in a timely manner.

[0111] If the predicted anomaly assessment coefficient on the predicted trend line is less than the safety threshold, and at the same time the real-time anomaly assessment coefficient on the real-time trend line is also less than the safety threshold, no warning signal is issued at this time, and it is considered that the aging risk of the material at this point in time is low, and routine inspections and maintenance continue, maintaining normal inspection frequency and maintenance plans.

[0112] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and relevant personnel can make corresponding handling according to the level of the warning signal.

[0113] In this embodiment, when there are materials with possible abnormal aging in the monitoring area, an exponential smoothing method is used to fit their performance data, analyze the changing trend of the aging degree, and perform intelligent management. First, the abnormal evaluation coefficients of the wall materials over a period of time are collected, their mean is calculated, and the smoothing coefficient is determined. The fitting value of the abnormal evaluation coefficient is calculated using the exponential smoothing formula, and a smoothed prediction trend line and real-time trend line are drawn. If at time point t, the abnormal evaluation coefficients on the prediction trend line and the real-time trend line are both greater than or equal to the safety threshold, a first-level warning signal is issued, requiring emergency repair or replacement; if one is greater than or equal to the safety threshold and the other is less than, a second-level warning signal is issued, and the monitoring frequency is appropriately adjusted; if both are less than the safety threshold, no warning signal is issued, and routine inspection and maintenance continue. This method achieves early prediction and intelligent management of the risks of aging materials.

[0114] Example 3:

[0115] See also Figure 2 As shown, the building fire safety assessment and analysis system described in this embodiment includes a signal processing module, an appearance and structure feature analysis module, a performance fluctuation analysis module, a comprehensive analysis module, a material classification module, and a prediction and warning module;

[0116] Signal processing module: Divides the interior of the building into n monitoring areas, analyzes the wall material temperature data through signal processing technology, and identifies and locates temperature anomalies based on the analysis results;

[0117] Appearance and structural characteristics analysis module: When abnormal temperature points appear in the wall material, the appearance and structural characteristics of the wall material are analyzed to determine the overall degree of change in the physical characteristics of the wall material appearance and evaluate the integrity of the wall material structure;

[0118] Performance Fluctuation Analysis Module: Determines the performance fluctuation of wall materials, compares and analyzes the original performance data of the materials with the real-time performance data, and evaluates the stability of the wall material performance;

[0119] Comprehensive analysis module: conducts comprehensive analysis on the structural integrity and performance stability of wall materials, and evaluates the abnormal degree of aging of wall materials;

[0120] Material classification module: Based on the assessment results, the wall materials in different monitoring areas inside the building are divided into abnormally aged materials, potentially abnormally aged materials, and normally aged materials, and are treated accordingly;

[0121] Prediction and early warning module: When there are materials that may be abnormally aged in the monitoring area, the aging degree changes of the materials that may be abnormally aged over a period of time are analyzed, and the risks caused by material abnormalities are predicted in advance and managed intelligently.

[0122] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0123] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0124] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0125] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0126] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A building fire safety assessment and analysis method, characterized by: The following steps are involved: S1: Divide the interior of the building into n monitoring areas, analyze the wall material temperature data through signal processing technology, and identify and locate temperature anomalies based on the analysis results; S2: When abnormal temperature points appear in the wall material, analyze the appearance and structural characteristics of the wall material, determine the overall degree of change in the physical characteristics of the wall material, and evaluate the integrity of the wall material structure; S3: Determine the performance fluctuation of the wall material, compare and analyze the original performance data of the material with the real-time performance data, and evaluate the stability of the wall material performance; Specifically, it includes: comparing and analyzing the original performance data and real-time performance data of the wall material, obtaining the performance deviation fluctuation index of the wall material based on the performance change trend between the original performance data and the real-time performance data, and evaluating the stability of the wall material performance; The method for obtaining the performance deviation fluctuation index is as follows: collect the original performance data and real-time performance data of the wall material and establish corresponding data sets respectively, where the original performance data set is X= ; Real-time performance data set is Y= ;in, , is a positive integer greater than 0; the average values ​​of the original performance data and the real-time performance data are calculated respectively, and are expressed as and ; and calculate the square difference SSX of the original performance data and the square difference SSY of the real-time performance data; calculate the intra-group sum of squares between the square difference of the real-time performance data and the square difference of the original performance data, the intra-group sum of squares represents the degree of fluctuation within the original performance data and the real-time performance data, and is obtained by summing the square differences of each group of data; and calculate the inter-group sum of squares between the square difference of the real-time performance data and the square difference of the original performance data, the inter-group sum of squares represents the degree of difference between the original performance data and the real-time performance data; calculate the ratio of the inter-group sum of squares to the intra-group sum of squares, compare the degree of difference between the groups with the degree of fluctuation within the groups, and use it to calculate the performance deviation fluctuation index; The obtained performance deviation fluctuation index of the wall material is compared with the performance deviation fluctuation index reference threshold. If the performance deviation fluctuation index of the wall material is greater than or equal to the performance deviation fluctuation index reference threshold, a material performance abnormality signal is generated. If the performance deviation fluctuation index of the wall material is less than the performance deviation fluctuation index reference threshold, no material performance abnormality signal is generated. S4: Comprehensively analyze the structural integrity of the wall materials and the stability of the wall material performance to assess the abnormal degree of aging of the wall materials; S5: Based on the assessment results, the wall materials in different monitoring areas inside the building are divided into abnormally aged materials, potentially abnormally aged materials, and normally aged materials, and are treated accordingly; S6: When there are materials that may be abnormally aged in the monitoring area, the changes in the aging degree of the materials that may be abnormally aged over a period of time are analyzed, and the risks caused by material abnormalities are predicted in advance and managed intelligently.

2. A building fire safety assessment and analysis method according to claim 1, characterized in that: In S1, the temperature data of the wall material is analyzed using signal processing technology. The temperature data is analyzed using wavelet transform technology to identify and locate temperature anomalies. Specifically: A temperature sensor is installed in each monitoring area to collect temperature data T(t) in real time, where (t) represents time. The collected temperature data is preprocessed and the preprocessed temperature data is marked as Tclean(t); Select the mother wavelet function ψ(t) and perform discrete wavelet transform on the preprocessed temperature data Tclean(t). The specific calculation expression is: ;in, is the wavelet coefficient, a is the scale parameter, b is the translation parameter, and ψ is the mother wavelet function; Perform multi-scale wavelet transform to decompose the temperature data into detail and approximation components of different scales. The calculation expression of wavelet decomposition is: ;in, is the approximation coefficient, is the detail coefficient, and are scaling function and wavelet function respectively; Wavelet transform decomposes the original temperature data Tclean(t) into approximation and detail components of different scales. The coefficients of the detail components are It reflects the change of the signal at scale j and position k; the obtained wavelet detail coefficient is compared with the wavelet detail coefficient reference threshold. If the wavelet detail coefficient is greater than or equal to the wavelet detail coefficient reference threshold, it is considered There is a temperature anomaly point at this point, and a temperature anomaly signal is generated, and the position of the wall material where the temperature anomaly point exists is marked; if the wavelet detail coefficient is less than the wavelet detail coefficient reference threshold, it is considered There is no abnormal temperature point, and a normal temperature signal is generated at this time, and the wall material is not marked.

3. A building fire safety assessment and analysis method according to claim 1, characterized in that: In S2, the appearance and structural characteristics of the wall material are analyzed to determine the overall degree of change in the physical characteristics of the wall material appearance. Based on the scope and depth of the wall material surface corrosion and the signs of material aging, the corrosion rate anomaly index of the wall material surface is obtained to evaluate the integrity of the wall material structure. The corrosion rate anomaly index is obtained as follows: Obtain the time series data of the surface corrosion of the wall material, perform the first-order difference operation on the time series data of the corrosion degree, and obtain the time series data of the corrosion rate. The first-order difference represents the rate of change of adjacent time points. The specific calculation expression is: difference value =Degree of corrosion -Corrosion level ; In the formula, the difference value Indicates the first-order difference value at time point s, the degree of corrosion Represents the corrosion degree data at time point s, the corrosion degree Indicates at a point in time Corrosion degree data at the site; Detect abnormal points in the corrosion rate time series. Abnormal points indicate abnormal fluctuations in the material corrosion rate. Calculate the material corrosion rate change value. The specific calculation expression is: ;in, is the mean of the difference values, is the standard deviation of the differences, is the change value of material corrosion rate; According to the calculated material corrosion rate change value, the corrosion rate abnormality index is calculated. The specific calculation expression is: ; where N is the number of outliers, is the weight of the i-th outlier, is the corrosion rate anomaly index; The obtained corrosion rate anomaly index is compared with the corrosion rate anomaly index reference threshold. If the corrosion rate anomaly index is greater than or equal to the corrosion rate anomaly index reference threshold, a material integrity anomaly signal is generated; if the corrosion rate anomaly index is less than the corrosion rate anomaly index reference threshold, no material integrity anomaly signal is generated.

4. A building fire safety assessment and analysis method according to claim 1, characterized in that: In S4, the integrity of the wall material structure and the stability of the wall material performance are comprehensively analyzed, specifically: The corrosion rate anomaly index and the performance deviation fluctuation index are normalized, and the abnormal assessment coefficient of the aging degree of the wall material is calculated based on the normalized corrosion rate anomaly index and performance deviation fluctuation index.

5. A building fire safety assessment and analysis method according to claim 4, characterized in that: In S5, based on the assessment results, the wall materials in different monitoring areas within the building are divided into abnormally aged materials, potentially abnormally aged materials, and normally aged materials. Specifically: Comparing the obtained abnormality assessment coefficient of the degree of aging of the wall material with a gradient threshold, where the gradient threshold includes a first threshold and a second threshold, and the first threshold is less than the second threshold, and comparing the abnormality assessment coefficient of the degree of aging of the wall material with the first threshold and the second threshold respectively; If the abnormal evaluation coefficient of the aging degree of the wall material is greater than the second threshold, it is classified as abnormal aging material, and the area of ​​the abnormal aging material is marked as an abnormal monitoring area; If the abnormality assessment coefficient of the aging degree of the wall material is greater than or equal to the first threshold and less than or equal to the second threshold, it is classified as a possible abnormal aging material, and the area of ​​the possible abnormal aging material is marked as a possible abnormal monitoring area; If the abnormal evaluation coefficient of the aging degree of the wall material is less than the first threshold, it is classified as normal aging material, and the area of ​​the normal aging material is marked as a normal monitoring area.

6. A building fire safety assessment and analysis method according to claim 1, characterized in that: In S6, when there is a possible abnormally aged material in the monitoring area, that is, the abnormal assessment coefficient of the aging degree of the wall material is greater than or equal to the first threshold and less than or equal to the second threshold within a period of time, the abnormal assessment coefficient of the aging degree of the wall material in a subsequent period of time is collected, and a corresponding data set is established. The abnormal assessment coefficients of the aging degree of the wall material in the data set are analyzed, the mean of the abnormal assessment coefficients of the aging degree of the wall material in the data set is calculated, and a smoothing coefficient F is determined; Abnormal assessment coefficient of the aging degree of wall materials obtained at time point T , use the exponential smoothing formula to calculate the abnormal assessment coefficient fitting value. The specific calculation expression is: Where, is the fitted value of the abnormal assessment coefficient, is the abnormal evaluation coefficient at time k-1; The calculated abnormal assessment coefficient fitting value As the predicted abnormality assessment coefficient at time k+1, the abnormality assessment coefficient fitting value is continuously updated. According to the calculated fitting value sequence, a smooth abnormality assessment coefficient prediction trend line is drawn. In addition, a smooth abnormality assessment coefficient real-time trend line is also drawn for the abnormality assessment coefficient of the wall material aging degree obtained in real time from the data set. The two smooth trend lines are compared and analyzed.

7. A building fire safety assessment and analysis method according to claim 6, characterized in that: The values ​​of the predicted trend line and the real-time trend line at time point T are analyzed. If the predicted anomaly assessment coefficient on the predicted trend line is greater than or equal to the safety threshold, and at the same time, the real-time anomaly assessment coefficient on the real-time trend line is greater than or equal to the safety threshold, a first-level warning signal is issued; if the predicted anomaly assessment coefficient on the predicted trend line and the real-time anomaly assessment coefficient on the real-time trend line meet that one is greater than or equal to the safety threshold and the other is less than the safety threshold, a second-level warning signal is issued; if the predicted anomaly assessment coefficient on the predicted trend line is less than the safety threshold, and at the same time, the real-time anomaly assessment coefficient on the real-time trend line is also less than the safety threshold, no warning signal is issued.

8. A building fire safety assessment and analysis system, for implementing a building fire safety assessment and analysis method according to any one of claims 1 to 7, characterized in that: It includes signal processing module, appearance and structure feature analysis module, performance fluctuation analysis module, comprehensive analysis module, material classification module and prediction and warning module; Signal processing module: Divides the interior of the building into n monitoring areas, analyzes the wall material temperature data through signal processing technology, and identifies and locates temperature anomalies based on the analysis results; Appearance and structural characteristics analysis module: When abnormal temperature points appear in the wall material, the appearance and structural characteristics of the wall material are analyzed to determine the overall degree of change in the physical characteristics of the wall material appearance and evaluate the integrity of the wall material structure; Performance Fluctuation Analysis Module: Determines the performance fluctuation of wall materials, compares and analyzes the original performance data of the materials with the real-time performance data, and evaluates the stability of the wall material performance; Comprehensive analysis module: conducts comprehensive analysis on the structural integrity and performance stability of wall materials, and evaluates the abnormal degree of aging of wall materials; Material classification module: Based on the assessment results, the wall materials in different monitoring areas inside the building are divided into abnormally aged materials, potentially abnormally aged materials, and normally aged materials, and are treated accordingly; Prediction and early warning module: When there are materials that may be abnormally aged in the monitoring area, the aging degree changes of the materials that may be abnormally aged over a period of time are analyzed, and the risks caused by material abnormalities are predicted in advance and managed intelligently.

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