Safety house safety assessment system based on AI

Abstract

The invention relates to the technical field of house safety detection, in particular to an AI-based safety house safety evaluation system, which comprises a data acquisition module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module is used for acquiring wall crack data, foundation data and environment data of a safety house; the crack feature analysis module is used for analyzing crack features; the settlement characteristic analysis module is used for analyzing the settlement characteristics of the foundation according to the settlement amount of the foundation collected in the monitoring period; the foundation feature analysis module is used for analyzing foundation features; the safety evaluation module is used for evaluating the safety of the safe house according to the analysis results of the crack characteristics, the foundation settlement characteristics and the foundation characteristics in the monitoring period; and the environment state monitoring module is used for adjusting the safety assessment process of the safe house according to the freezing and thawing cycle times, the environment temperature and the wall body temperature collected in the monitoring period. According to the invention, the accuracy of safety assessment of the safe house is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of house safety detection, and in particular to an AI-based safe house safety assessment system. Background Art

[0002] With the acceleration of urbanization, the safety and stability of buildings have gradually attracted people's attention. Especially under soft foundations and extreme climatic conditions, the structural integrity of buildings may face severe challenges. Existing safety assessment methods often rely on manual inspections and regular assessments, which are difficult to achieve real-time monitoring and rapid response, and there are potential risks.

[0003] Chinese patent publication number CN110969372A discloses a housing safety appraisal method, which is applied in the background. The background establishes a housing model in combination with the construction drawings, service life and repair status of the house to be appraised to reflect the ideal use of the house; uses detection equipment to detect data and transmits it to the background; the background sets a simulation value and substitutes it into the house model, compares the calculated data with the acquired data, and optimizes and corrects the house model according to the comparison result; the background associates the acquired data with the corrected house model, and combines it with relevant specifications pre-stored in the background for rating the danger of the house to generate a simulation model for obtaining the house appraisal result; the background returns the obtained house appraisal result to the detection equipment of the on-site staff for display; it can be seen that when evaluating the safety of the house, this scheme only analyzes the service life and repair status of the house, and there is a problem of low accuracy in the house safety assessment. Summary of the invention

[0004] The object of the present invention is to provide an AI-based safe house safety assessment system to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: An AI-based safe house security assessment system, including: Data collection module, used to collect data on cracks in the safe house walls, foundation data, and environmental data; A crack feature analysis module, for analyzing crack width features, crack length features and crack depth features according to the crack width, crack length and crack depth collected during the monitoring period, and for analyzing crack features according to the analysis results of the crack width features, the analysis results of the crack length features and the analysis results of the crack depth features during the monitoring period; The settlement characteristic analysis module is used to analyze the foundation settlement characteristics according to the foundation settlement amount collected during the monitoring period; The foundation characteristic analysis module is used to analyze the foundation characteristics according to the foundation soil moisture and foundation pore water pressure collected during the monitoring period; The safety assessment module is used to assess the safety of the safe house based on the analysis results of crack characteristics, foundation settlement characteristics and foundation characteristics during the monitoring period, and output the assessment results to the user; The environmental status monitoring module is used to adjust the safety assessment process of the safe house according to the number of freeze-thaw cycles, ambient temperature and wall temperature collected during the monitoring period.

[0006] Further, the crack feature analysis module includes a width feature analysis unit, which is used to analyze the crack width feature according to the crack width collected during the monitoring period to obtain the crack width feature KT; The crack feature analysis module also includes a length feature analysis unit, which is used to analyze the crack length feature according to the crack length collected during the monitoring period to obtain the crack length feature CT; The crack feature analysis module further includes a depth feature analysis unit, which is used to analyze the crack depth feature according to the crack depth collected during the monitoring period to obtain the crack depth feature ST.

[0007] Furthermore, the crack feature analysis module also includes a crack feature analysis unit, which analyzes the crack features according to the analysis results KT of the crack width features, the analysis results CT of the crack length features and the analysis results ST of the crack depth features within the monitoring period to obtain the crack features LT.

[0008] Furthermore, the settlement characteristic analysis module is used to analyze the foundation settlement characteristics according to the foundation settlement amount collected during the monitoring period to obtain the foundation settlement characteristics JT.

[0009] Furthermore, the foundation characteristic analysis module includes a humidity analysis unit, which is used to compare the soil humidity s0 collected during the monitoring period with the preset humidity s1, and analyze the abnormality of the soil humidity according to the comparison result, and the abnormality of the soil humidity includes normal and abnormal; The humidity analysis unit analyzes the soil humidity characteristics according to the analysis result of the soil humidity anomaly within the monitoring period to obtain the soil humidity characteristics R1.

[0010] Furthermore, the foundation characteristic analysis module also includes a pressure analysis unit, which is used to compare the foundation pore water pressure y0 collected during the monitoring period with the preset pressure y1, and analyze the abnormality of the foundation pore water pressure according to the comparison result, and the abnormality of the foundation pore water pressure includes normal and abnormal; The pressure analysis unit analyzes the pore water pressure characteristics according to the analysis results of the abnormality of the foundation pore water pressure during the monitoring period to obtain the pore water pressure characteristics R2.

[0011] Furthermore, the foundation characteristic analysis module further includes a foundation analysis unit, and the foundation analysis unit analyzes the foundation characteristics according to the analysis results of the soil moisture characteristics and the pore water pressure characteristics within the monitoring period to obtain the foundation characteristics DT.

[0012] Furthermore, the safety assessment module is used to assess the safety of the safe house according to the analysis results of the crack characteristics, foundation settlement characteristics and foundation characteristics within the monitoring period, wherein: If LT+JT+DT≤u0, the safety assessment module determines that the safety of the safe house in the current monitoring period is normal; If LT+JT+DT>u0, the safety assessment module determines that the safety of the safe house in the current monitoring period is abnormal, and u0 is a preset safety factor.

[0013] Furthermore, the environment status monitoring module includes a first analysis unit, which is used to analyze the temperature status of the safe house according to the ambient temperature t1 and the wall temperature t2 collected during the monitoring period, and adjust the evaluation process of the safety of the safe house according to the analysis results, wherein: If |t1-t2|≤△t, the first analysis unit determines that the temperature of the safe house is normal; If |t1-t2|>△t, the first analysis unit determines that the temperature of the safe house is abnormal. When g3 / G≤α, the first analysis unit determines that the temperature state of the safe house in the current monitoring period is normal and does not make any adjustments. When g3 / G>α, the first analysis unit determines that the temperature state of the safe house in the current monitoring period is abnormal and adjusts the preset safety factor by u1. Among them, g3 is the duration of abnormal temperature in the safe house during the monitoring period, α is the preset abnormal proportion, △t is the preset temperature difference, and G is the duration of the monitoring period.

[0014] Furthermore, the environmental status monitoring module also includes a second analysis unit, which is used to compare the number of freeze-thaw cycles d0 collected during the monitoring period with the preset number of cycles d1, and adjust the analysis process of the temperature status of the safe house according to the comparison result. If the number of freeze-thaw cycles does not meet the threshold, the preset abnormality ratio is adjusted to α1.

[0015] The beneficial effects of the present invention are as follows: The AI-based safe house safety assessment system of this embodiment ensures that the safety of the safe house is scientifically assessed by comprehensively collecting key parameters such as wall cracks, foundation settlement, soil moisture, pore water pressure and environmental data. Through the systematic analysis of crack characteristics, settlement characteristics and foundation characteristics, potential safety hazards can be discovered in a timely manner and accidents can be effectively prevented. In addition, the introduction of the environmental status monitoring module optimizes the assessment of the impact of temperature and freeze-thaw cycles on buildings, and improves the accuracy and reliability of safety assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0017] Figure 1 Schematic diagram of the structure of the AI-based safe house safety assessment system of this embodiment.

[0018] Figure 2 Schematic diagram of the structure of the crack feature analysis module of this embodiment.

[0019] Figure 3 Schematic diagram of the structure of the foundation feature analysis module of this embodiment.

[0020] Figure 4 Schematic diagram of the structure of the environmental status monitoring module of this embodiment. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0022] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0023] See also Figure 1 As shown, it is a schematic diagram of the structure of the AI-based safe house safety assessment system of this embodiment, and the system includes: The data acquisition module is used to collect the data of cracks in the wall of the safe house, the foundation data and the environmental data. The data of cracks in the wall of the safe house includes the crack width, crack length and crack depth. The foundation data includes the foundation soil moisture, foundation pore water pressure and foundation settlement. The environmental data includes the ambient temperature, wall temperature and the number of freeze-thaw cycles. The foundation pore water pressure is the pressure generated by the pore water in the foundation soil. It can be understood that in this embodiment, no specific limitation is made on the collection method of the data of cracks in the wall of the safe house, the foundation data and the environmental data. Those skilled in the art can freely set it as long as the data of cracks in the wall of the safe house, the foundation data and the environmental data are satisfied. The collection requirements of foundation data and environmental data are sufficient, wherein the wall crack data of the safe house can be collected by a laser displacement sensor, the soil moisture of the foundation can be collected by a humidity sensor, the pore water pressure of the foundation can be obtained by a pressure sensor, the foundation settlement is the settlement value of each monitoring point that can be collected by a settlement monitoring sensor, one monitoring point can be set at each of the four corners of the building foundation, and 4 additional monitoring points can be set in the middle, edge and inclined part of the foundation, the number of freeze-thaw cycles is the number of times the ambient temperature around the safe house repeatedly alternates at 0°C, and the environmental data can be collected by a temperature sensor.

[0024] Please continue reading Figure 1 As shown, the system also includes: A crack feature analysis module is connected to the data acquisition module, and is used to analyze the crack width feature, crack length feature and crack depth feature according to the crack width, crack length and crack depth collected during the monitoring period, and is also used to analyze the crack features according to the analysis results of the crack width feature, the analysis results of the crack length feature and the analysis results of the crack depth feature during the monitoring period.

[0025] It is understandable that the present embodiment does not specifically limit the setting of the monitoring period, and those skilled in the art may freely set it as long as the setting requirements of the monitoring period are met, wherein the monitoring period may be set to 30 days, 50 days, etc.

[0026] See also Figure 2 As shown, the crack feature analysis module includes: The width characteristic analysis unit is used to analyze the crack width characteristic according to the crack width collected during the monitoring period to quantify the abnormal degree of the crack width. The width characteristic analysis unit sets the crack width characteristic as KT and sets: ; Among them, A(i,jz) is the jzth crack width data of the ith crack collected during the monitoring period, jz is the number of crack data of the ith crack collected during the monitoring period, Y1 is the preset width threshold, A(i,js) is the first crack width data of the ith crack collected during the monitoring period, T(i,jz) is the time corresponding to the collection of the jzth crack data of the ith crack during the monitoring period, T(i,js) is the time corresponding to the collection of the first crack data of the ith crack during the monitoring period, T(i,jz)-T(i,js) is the time difference between the two data collections, in h, C1 is the preset width growth rate, and I is the number of cracks collected during the monitoring period; by analyzing the width characteristics of the cracks and quantifying the abnormality of the crack width, the accuracy of the crack feature analysis is improved, thereby improving the accuracy of the safety assessment of the safe house.

[0027] It can be understood that, in this embodiment, there is no specific limitation on the setting of the preset width threshold and the preset width growth rate, and those skilled in the art can set them freely as long as the setting requirements of the preset width threshold and the preset width growth rate are met, among which, the optimal value of Y1 is 1mm, and the optimal value of C1 is 0.05mm / h.

[0028] Please continue reading Figure 2 As shown, the crack feature analysis module also includes: The length feature analysis unit is used to analyze the crack length feature according to the crack length collected during the monitoring period to quantify the abnormality of the crack length. The length feature analysis unit sets the crack length feature as CT and sets: ; Among them, B(i,jz) is the jzth crack length data of the ith crack collected during the monitoring period, Y2 is the preset length threshold, B(i,js) is the first crack length data of the ith crack collected during the monitoring period, and C2 is the preset length growth rate; by analyzing the length characteristics of the cracks and quantifying the abnormal degree of the crack length, the accuracy of the crack feature analysis is improved, thereby improving the accuracy of the safe house safety assessment.

[0029] It can be understood that, in this embodiment, there is no specific limitation on the setting of the preset width threshold and the preset width growth rate, and those skilled in the art can set them freely as long as the setting requirements of the preset width threshold and the preset width growth rate are met, among which, the optimal value of Y2 is 300mm, and the optimal value of C2 is 0.5mm / h.

[0030] Please continue reading Figure 2 As shown, the crack feature analysis module also includes: A depth feature analysis unit is used to analyze the crack depth feature according to the crack depth collected during the monitoring period to quantify the abnormality of the crack depth. The depth feature analysis unit sets the crack depth feature as ST and sets: ; Among them, C(i,jz) is the jzth crack depth data of the ith crack collected during the monitoring period, Y3 is the preset depth threshold, C(i,js) is the first crack depth data of the ith crack collected during the monitoring period, and C3 is the preset depth growth rate; by analyzing the depth characteristics of the cracks and quantifying the abnormal degree of the crack depth, the accuracy of the crack feature analysis is improved, thereby improving the accuracy of the safe house safety assessment.

[0031] It can be understood that the present embodiment does not specifically limit the setting of the preset width threshold and the preset width growth rate, and those skilled in the art can freely set them as long as the setting requirements of the preset width threshold and the preset width growth rate are met, among which the optimal value of Y3 is 20 mm, and the optimal value of C3 is 0.1 mm / h.

[0032] Please continue reading Figure 2 As shown, the crack feature analysis module also includes: A crack feature analysis unit is connected to the width feature analysis unit, the length feature analysis unit and the depth feature analysis unit. The crack feature analysis unit analyzes the crack feature according to the analysis results of the crack width feature, the crack length feature and the crack depth feature within the monitoring period. The crack feature analysis unit sets the crack feature as LT, and sets: LT=x1×KT+x2×CT+x3×ST, wherein x1 is the width weight, x2 is the length weight, x3 is the depth weight, and x1+x2+x3=1; by combining the crack width, length and depth features for comprehensive evaluation, the nature and risk of the crack can be more comprehensively understood, thereby improving the accuracy of the safety assessment of the safe house.

[0033] It can be understood that the present embodiment does not specifically limit the setting of each weight, and those skilled in the art can freely set it as long as the setting requirements of each weight are met, among which the best value of x1 is 0.35, the best value of x2 is 0.2, and the best value of x3 is 0.45.

[0034] Please continue reading Figure 1 As shown, the system also includes: A settlement feature analysis module, which is connected to the data crack feature analysis module, and is used to analyze the foundation settlement features according to the foundation settlement amount collected during the monitoring period, wherein: The settlement characteristic analysis module sorts the foundation settlement of each monitoring point in descending order, and sets the maximum value of the foundation settlement to Pmax, and sets the minimum value of the foundation settlement to Pmin; The settlement characteristic analysis module sets the foundation settlement characteristic as JT, sets JT=(Pmax-Pmin) / L, and L is the preset settlement threshold; by monitoring and analyzing the foundation settlement, the uniformity and deviation of the foundation settlement are effectively measured, providing an important basis for judging the stability of the foundation, thereby improving the accuracy of the safety assessment of the safe house.

[0035] It can be understood that the present embodiment does not specifically limit the setting of the preset sedimentation threshold, and those skilled in the art can freely set it as long as the setting requirements of the preset sedimentation threshold are met, wherein the optimal value of L is 30 mm.

[0036] Please continue reading Figure 1 As shown, the system also includes: The foundation characteristic analysis module is connected to the settlement characteristic analysis module and is used to analyze the foundation characteristics according to the foundation soil moisture and foundation pore water pressure collected during the monitoring period.

[0037] See also Figure 3 As shown, the foundation feature analysis module includes: The humidity analysis unit is used to compare the soil humidity s0 collected during the monitoring period with the preset humidity s1, and analyze the abnormality of the soil humidity according to the comparison result to timely discover abnormal changes in the soil state, wherein: If s0≤s1, the humidity analysis unit determines that the soil humidity is normal; If s0>s1, the humidity analysis unit determines that the soil humidity is abnormal; The humidity analysis unit analyzes the soil moisture characteristic R1 according to the analysis result of soil moisture anomaly during the monitoring period, and sets R1=g1 / G, wherein g1 is the duration of soil moisture anomaly during the monitoring period, and G is the duration of the monitoring period; by analyzing the soil moisture characteristics during the monitoring period to quantify the abnormal degree of foundation soil moisture, the accuracy of foundation characteristic analysis is improved, thereby improving the accuracy of safe house safety assessment.

[0038] It is understandable that the setting of the preset humidity is not specifically limited in this embodiment, and those skilled in the art can set it freely as long as the setting requirements of the preset humidity are met, wherein the best value of s1 is 15%.

[0039] Please continue reading Figure 3 As shown, the foundation feature analysis module also includes: The pressure analysis unit is used to compare the foundation pore water pressure y0 collected during the monitoring period with the preset pressure y1, and analyze the abnormality of the foundation pore water pressure according to the comparison result, wherein: If y0≤y1, the pressure analysis unit determines that the foundation pore water pressure is normal; If y0>y1, the pressure analysis unit determines that the foundation pore water pressure is abnormal; The pressure analysis unit analyzes the pore water pressure characteristic R2 according to the analysis results of the abnormality of the foundation pore water pressure during the monitoring period, and sets R2=g2 / G, wherein g2 is the duration of the abnormal foundation pore water pressure during the monitoring period; by analyzing the pore water pressure characteristics to quantify the abnormality of the foundation pore water pressure, potential dangers such as landslides and foundation instability are prevented, thereby improving the accuracy of the safety assessment of the safe house.

[0040] It can be understood that the setting of the preset pressure is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as the setting requirements of the preset pressure are met, wherein the optimal value of y1 is 20 kPa.

[0041] Please continue reading Figure 3 As shown, the foundation feature analysis module also includes: A foundation analysis unit is connected to the humidity analysis unit and the pressure analysis unit. The foundation analysis unit analyzes the foundation characteristics according to the analysis results of the soil humidity characteristics and the pore water pressure characteristics within the monitoring period. The foundation analysis unit sets the foundation characteristics as DT, and sets DT=w1×R1+w2×R2, wherein w1 is the humidity weight, w2 is the pressure weight, and w1+w2=1; through the comprehensive characteristic analysis of soil humidity and pore water pressure, the foundation characteristics are analyzed to provide more accurate data support for the safety assessment, thereby improving the accuracy of the safety assessment of the safe house.

[0042] It is understandable that the present embodiment does not specifically limit the setting of each weight, and those skilled in the art can freely set it as long as the setting requirements of each weight are met, where the best value of w1 is 0.4, and the best value of w2 is 0.6.

[0043] Please continue reading Figure 1 As shown, the system also includes: A safety assessment module is connected to the foundation feature analysis module, and is used to assess the safety of the safe house according to the crack features, foundation settlement features and foundation feature analysis results within the monitoring period, and output the assessment results to the user, wherein: If LT+JT+DT≤u0, the safety assessment module determines that the safety of the safe house in the current monitoring period is normal; If LT+JT+DT>u0, the safety assessment module determines that the safety of the safe house in the current monitoring period is abnormal, and u0 is the preset safety factor; by combining the comprehensive analysis of crack characteristics, settlement characteristics and foundation characteristics, the accuracy of the safety assessment is improved, which helps to judge the safety status of the safe house and provide a reliable decision-making basis.

[0044] It can be understood that the setting of the preset safety factor is not specifically limited in this embodiment, and those skilled in the art can set it freely as long as the setting requirements of the preset safety factor are met, wherein the optimal value of u0 is 0.24.

[0045] Please continue reading Figure 1 As shown, the system also includes: The environmental status monitoring module is connected to the safety assessment module, and is used to adjust the safety assessment process of the safe house according to the number of freeze-thaw cycles, ambient temperature and wall temperature collected during the monitoring period.

[0046] Please continue reading Figure 4 As shown, the environmental status monitoring module includes: The first analysis unit is used to analyze the temperature state of the safe house according to the ambient temperature t1 and the wall temperature t2 collected during the monitoring period, and adjust the evaluation process of the safety of the safe house according to the analysis results to reduce the impact of abnormal temperature state on the safety evaluation of the safe house, wherein: If |t1-t2|≤△t, the first analysis unit determines that the temperature of the safe house is normal; If |t1-t2|>△t, the first analysis unit determines that the temperature of the safe house is abnormal. When g3 / G≤α, the first analysis unit determines that the temperature state of the safe house in the current monitoring period is normal and does not make any adjustments. When g3 / G>α, the first analysis unit determines that the temperature state of the safe house in the current monitoring period is abnormal and sets the preset safety factor to u1, setting: u1=u0×[1-(g3 / G-α) 2 ]; Among them, g3 is the duration of abnormal temperature in the safe house during the monitoring period, α is the preset abnormal proportion, and △t is the preset temperature difference. By monitoring the temperature difference between the environment and the wall, the accuracy of the temperature state analysis of the safe house is improved, thereby reducing the impact of abnormal temperature state on the safety assessment of the safe house, thereby improving the accuracy of the safety assessment of the safe house.

[0047] It can be understood that, in this embodiment, the setting of the preset temperature difference and the preset abnormality ratio is not specifically limited, and those skilled in the art can set them freely, as long as the setting requirements of the preset temperature difference and the preset abnormality ratio are met, wherein the optimal value of △t is 5°C, and the optimal value of α is 0.15.

[0048] Please continue reading Figure 4 As shown, the environmental status monitoring module includes: The second analysis unit is connected to the first analysis unit, and the second analysis unit is used to compare the number of freeze-thaw cycles d0 collected during the monitoring period with the preset number of cycles d1, and adjust the analysis process of the temperature state of the safe house according to the comparison result to reduce the influence of the abnormal number of freeze-thaw cycles on the temperature state analysis of the safe house, wherein: If d0≤d1, the second analysis unit determines that the number of freeze-thaw cycles meets the threshold and does not make any adjustment; If d0>d1, the second analysis unit determines that the number of freeze-thaw cycles does not meet the threshold, and sets the preset abnormal proportion to α1, setting α1=α×exp[-(d0-d1) / (d0+d1)]; by monitoring the number of freeze-thaw cycles, the changes in the environment in which the building is located can be understood in a timely manner, thereby reducing the impact of the abnormal number of freeze-thaw cycles on the temperature status analysis of the safe house, thereby improving the accuracy of the safety status assessment of the safe house.

[0049] Specifically, the number of freeze-thaw cycles collected during the monitoring period described in this embodiment is the sum of the historical freeze-thaw cycles of the safe house at the end of the monitoring period. It can be understood that in this implementation, no specific limitation is made to the setting of the preset number of cycles, and technical personnel in this field can set it freely as long as the setting requirements of the preset number of cycles are met. Among them, the optimal value of d1 is 50 times.

[0050] Specifically, the AI-based safe house safety assessment system described in this embodiment is suitable for shelters, residences and public facilities in mountainous areas with complex climatic conditions. It monitors changes in walls, foundations and the environment to detect potential dangers in a timely manner.

[0051] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. An AI-based safe house safety assessment system, characterized in that: include: Data collection module, used to collect data on cracks in the safe house walls, foundation data, and environmental data; A crack feature analysis module, for analyzing crack width features, crack length features and crack depth features according to the crack width, crack length and crack depth collected during the monitoring period, and for analyzing crack features according to the analysis results of the crack width features, the analysis results of the crack length features and the analysis results of the crack depth features during the monitoring period; The settlement characteristic analysis module is used to analyze the foundation settlement characteristics according to the foundation settlement amount collected during the monitoring period; The foundation characteristic analysis module is used to analyze the foundation characteristics according to the foundation soil moisture and foundation pore water pressure collected during the monitoring period; The safety assessment module is used to assess the safety of the safe house based on the analysis results of crack characteristics, foundation settlement characteristics and foundation characteristics during the monitoring period, and output the assessment results to the user; The environmental status monitoring module is used to adjust the safety assessment process of the safe house according to the number of freeze-thaw cycles, ambient temperature and wall temperature collected during the monitoring period.

2. The AI-based safe house safety assessment system according to claim 1 is characterized in that: The crack feature analysis module includes a width feature analysis unit, which is used to analyze the crack width feature according to the crack width collected during the monitoring period to obtain the crack width feature KT; The crack feature analysis module also includes a length feature analysis unit, which is used to analyze the crack length feature according to the crack length collected during the monitoring period to obtain the crack length feature CT; The crack feature analysis module further includes a depth feature analysis unit, which is used to analyze the crack depth feature according to the crack depth collected during the monitoring period to obtain the crack depth feature ST.

3. The AI-based safe house safety assessment system according to claim 2 is characterized in that: The crack feature analysis module further includes a crack feature analysis unit, which analyzes the crack feature according to the analysis result KT of the crack width feature, the analysis result CT of the crack length feature and the analysis result ST of the crack depth feature within the monitoring period to obtain the crack feature LT.

4. The AI-based safe house safety assessment system according to claim 3 is characterized in that: The settlement characteristic analysis module is used to analyze the foundation settlement characteristics according to the foundation settlement amount collected during the monitoring period to obtain the foundation settlement characteristics JT.

5. The AI-based safe house safety assessment system according to claim 4 is characterized in that: The foundation feature analysis module includes a humidity analysis unit, which is used to compare the soil humidity s0 collected during the monitoring period with the preset humidity s1, and analyze the abnormality of the soil humidity according to the comparison result, and the abnormality of the soil humidity includes normal and abnormal; The humidity analysis unit analyzes the soil humidity characteristics according to the analysis result of the soil humidity anomaly within the monitoring period to obtain the soil humidity characteristics R1.

6. The AI-based safe house safety assessment system according to claim 5, characterized in that: The foundation characteristic analysis module also includes a pressure analysis unit, which is used to compare the foundation pore water pressure y0 collected during the monitoring period with the preset pressure y1, and analyze the abnormality of the foundation pore water pressure according to the comparison result. The abnormality of the foundation pore water pressure includes normal and abnormal; The pressure analysis unit analyzes the pore water pressure characteristics according to the analysis results of the abnormality of the foundation pore water pressure during the monitoring period to obtain the pore water pressure characteristics R2.

7. The AI-based safe house safety assessment system according to claim 6, characterized in that: The foundation characteristic analysis module further includes a foundation analysis unit, which analyzes the foundation characteristics according to the analysis results of the soil moisture characteristics and the pore water pressure characteristics within the monitoring period to obtain the foundation characteristics DT.

8. The AI-based safe house safety assessment system according to claim 7, characterized in that: The safety assessment module is used to assess the safety of the safe house according to the analysis results of crack characteristics, foundation settlement characteristics and foundation characteristics during the monitoring period, wherein: If LT+JT+DT≤u0, the safety assessment module determines that the safety of the safe house in the current monitoring period is normal; If LT+JT+DT>u0, the safety assessment module determines that the safety of the safe house in the current monitoring period is abnormal, and u0 is a preset safety factor.

9. The AI-based safe house safety assessment system according to claim 8, characterized in that: The environmental status monitoring module includes a first analysis unit, which is used to analyze the temperature status of the safe house according to the ambient temperature t1 and the wall temperature t2 collected during the monitoring period, and adjust the evaluation process of the safety of the safe house according to the analysis results, wherein: If |t1-t2|≤△t, the first analysis unit determines that the temperature of the safe house is normal; If |t1-t2|>△t, the first analysis unit determines that the temperature of the safe house is abnormal. When g3 / G≤α, the first analysis unit determines that the temperature state of the safe house in the current monitoring period is normal and does not make any adjustments. When g3 / G>α, the first analysis unit determines that the temperature state of the safe house in the current monitoring period is abnormal and adjusts the preset safety factor by u1. Among them, g3 is the duration of abnormal temperature in the safe house during the monitoring period, α is the preset abnormal proportion, △t is the preset temperature difference, and G is the duration of the monitoring period.

10. The AI-based safe house safety assessment system according to claim 9, characterized in that: The environmental status monitoring module also includes a second analysis unit, which is used to compare the number of freeze-thaw cycles d0 collected during the monitoring period with the preset number of cycles d1, and adjust the analysis process of the temperature status of the safe house according to the comparison result. If the number of freeze-thaw cycles does not meet the threshold, the preset abnormal proportion is adjusted to α1.

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