Fireproof door deformation detection method and system

Abstract

The invention provides a fireproof door deformation detection method and system, and relates to the technical field of deformation detection, and the method comprises the steps: obtaining the environment data of a target fireproof door, and determining a target environment record; matching a multi-environment detection model based on the target environment record to obtain a target detection model; through the target detection model, deformation detection based on the target detection area sequence is carried out on the target fireproof door, and target deformation data is generated; performing deformation feature analysis on the target deformation data to obtain target deformation features; and performing early warning on the target fireproof door according to the target deformation feature, and repairing the target fireproof door according to the early warning information. According to the method and the device, the technical problem that a fireproof door deformation detection method cannot accurately identify deformation in a special environment due to lack of adaptive adjustment capability for changes of different environmental conditions in the prior art can be solved, and adaptive optimization of the fireproof door deformation detection method in a complex environment is realized; the technical effect of improving the detection accuracy and reliability is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of deformation detection, and in particular to a method and system for detecting deformation of a fire door. Background Art

[0002] As modern buildings have increasingly strict fire safety requirements, fire doors, as important facilities to prevent the spread of fire, have attracted extensive attention for their performance stability and reliability. However, fire doors that have been in complex environments for a long time, such as high humidity, high temperature, salt spray corrosion, etc., may deform differently from those in normal environments, and traditional detection methods often cannot accurately capture these changes in real time.

[0003] At present, the existing fire door deformation detection methods do not consider the impact of different environments on the deformation of fire doors. They often rely on preset environmental thresholds and are difficult to adapt to extreme environmental changes such as high temperature, high humidity and high salt fog. For example, in an environment with heavy salt fog corrosion, the deformation pattern of the fire door may be different from that in a general high temperature environment, and the current detection model may not be able to adaptively adjust the characteristics of the environment, resulting in false alarms or missed alarms. It lacks intelligent learning and adaptive adjustment of deformation patterns in special environments, and cannot optimize the detection strategy according to real-time changes in the environment, thus affecting the accuracy and reliability of detection.

[0004] In summary, the prior art has a technical problem in that the fire door deformation detection method cannot accurately identify the deformation under special circumstances due to the lack of adaptive adjustment capability to changes in different environmental conditions, which further affects the accuracy and reliability of the detection results. Summary of the invention

[0005] The purpose of the present application is to provide a fire door deformation detection method and system to solve the technical problem in the prior art that the fire door deformation detection method cannot accurately identify the deformation under special circumstances due to the lack of adaptive adjustment capability to changes in different environmental conditions, further affecting the accuracy and reliability of the detection results.

[0006] In view of the above problems, the present application provides a fire door deformation detection method and system.

[0007] In a first aspect, the present application provides a fire door deformation detection method, which is implemented through a fire door deformation detection system, including: obtaining environmental data of a target fire door and determining a target environmental record; matching a multi-environment detection model based on the target environmental record to obtain a target detection model; performing deformation detection on the target fire door based on a target detection area sequence through the target detection model to generate target deformation data; performing deformation feature analysis on the target deformation data to obtain target deformation features; issuing an early warning to the target fire door based on the target deformation features, and repairing the target fire door based on the early warning information.

[0008] In the second aspect, the present application also provides a fire door deformation detection system for executing a fire door deformation detection method as described in the first aspect, including: a data acquisition module, the data acquisition module is used to acquire the environmental data of the target fire door and determine the target environment record; a model matching module, the model matching module is used to match the multi-environment detection model based on the target environment record to obtain the target detection model; a deformation detection module, the deformation detection module is used to perform deformation detection on the target fire door based on the target detection area sequence through the target detection model to generate target deformation data; a feature analysis module, the feature analysis module is used to perform deformation feature analysis on the target deformation data to obtain the target deformation feature; an early warning module, the early warning module is used to warn the target fire door according to the target deformation feature, and repair the target fire door according to the early warning information.

[0009] The technical solution provided in the present application has at least the following technical effects or advantages: determining the target environment record by acquiring the environmental data of the target fire door; matching the multi-environment detection model based on the target environment record to obtain the target detection model; performing deformation detection on the target fire door based on the target detection area sequence through the target detection model to generate target deformation data; performing deformation feature analysis on the target deformation data to obtain target deformation features; issuing an early warning to the target fire door based on the target deformation features, and repairing the target fire door based on the early warning information. That is to say, by realizing the adaptive optimization of the fire door deformation detection method in a complex environment, the technical effect of improving detection accuracy and reliability is achieved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 This is a flow chart of a method for detecting deformation of a fire door according to the present application.

[0013] Figure 2 This is a structural schematic diagram of a fire door deformation detection system for the present application.

[0014] Description of reference numerals: Data acquisition module 11, model matching module 12, deformation detection module 13, feature analysis module 14, early warning module 15. DETAILED DESCRIPTION

[0015] The present application provides a fire door deformation detection method and system, which solves the technical problem in the prior art that the fire door deformation detection method cannot accurately identify the deformation in special environments due to the lack of adaptive adjustment capability to changes in different environmental conditions, further affecting the accuracy and reliability of the detection results. The fire door deformation detection method is adaptively optimized in complex environments, achieving the technical effect of improving the detection accuracy and reliability.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides a fire door deformation detection method, which is applied to a fire door deformation detection system and specifically includes the following steps: Step 1: Obtain the environmental data of the target fire door and determine the target environmental record.

[0018] Specifically, the target fire door is the fire door to be subjected to deformation detection. Through sensors or other detection equipment, the historical environmental data of the target fire door is obtained, and environmental information related to the use environment of the target fire door, including temperature, humidity and gas composition (such as salt spray concentration or corrosive gas content), is collected as the target environment record for subsequent analysis.

[0019] Step 2: Match the multi-environment detection model based on the target environment record to obtain the target detection model.

[0020] Specifically, by analyzing the environmental records of the target fire door, we determine the degree of compatibility with multiple detection models, and thus select the detection model that best reflects the characteristics of the environment. The environmental data such as temperature, humidity, and salt spray concentration collected by the sensor are matched with the thresholds in the multi-environment detection model. When the environmental data conforms to multiple models, the most relevant detection model is selected as the target detection model, thereby improving the deformation detection efficiency of the target fire door under the influence of environmental deformation.

[0021] Step three: Using the target detection model, perform deformation detection on the target fire door based on the target detection area sequence to generate target deformation data.

[0022] Specifically, through the target detection model, according to the predetermined detection area sequence, the target fire door is subjected to deformation detection based on the target detection area sequence to generate target deformation data, which means Among them, the target detection model can flexibly adjust the detection strategy according to the input fire door status information. For example, in a high humidity environment, the target detection model may set a priority detection sequence for the hinge area, and perform secondary detection on the door frame and door panel areas. The target detection area sequence is a list of fire door partitions arranged by priority, such as detecting the hinge area, door frame area, and middle area of ​​the door panel in turn. Deformation detection is a process of collecting deformation data through equipment and analyzing its changing trends. For example, a laser rangefinder is used to detect the deformation of the hinge area and generate specific deformation data records, which provides an accurate basis for the status assessment and maintenance decision of the fire door.

[0023] Step 4: Perform deformation feature analysis on the target deformation data to obtain target deformation features.

[0024] Specifically, by deeply processing and analyzing the detected deformation data, specific features that can reflect the deformation pattern and nature of the fire door are extracted. The target deformation data contains specific deformation information, such as the direction of deformation (e.g. bulging outward), the distribution range of deformation (e.g. concentrated in the upper area of ​​the door frame), or the trend of deformation (e.g. continuous increase and no stability). Feature analysis, such as predicting the deformation growth trend through a linear regression model or extracting the most important influencing factors through principal component analysis, provides a clear basis for subsequent judgment and decision-making, and can help identify potential risk areas and formulate corresponding maintenance measures to ensure the performance and safety of fire doors.

[0025] Step 5: issuing an early warning to the target fire door according to the target deformation characteristics, and repairing the target fire door according to the early warning information.

[0026] Specifically, the target fire door is warned based on the target deformation characteristics. The warning is an early warning mechanism to help managers take measures before the deformation affects the normal function of the door. For example, when the deformation of the hinge area is detected to be increasing, a warning message is generated to indicate that the deformation has reached a moderate level and needs attention and repair actions. The warning can be based on the level of deformation, or it can be combined with environmental characteristics (such as high humidity or high temperature) for comprehensive judgment, thereby improving the accuracy and timeliness of the warning.

[0027] After receiving the early warning, specific repair measures are taken to eliminate or alleviate the deformation problem. Repairs include targeted actions such as adjusting the door position, replacing damaged parts or improving environmental conditions to restore the normal function of the fire door while reducing the safety hazards that may be caused by deformation.

[0028] The fire door deformation detection method is applied to a fire door deformation detection system, which can realize adaptive optimization of the fire door deformation detection method in a complex environment, thereby achieving the technical effect of improving detection accuracy and reliability.

[0029] Furthermore, the present application also includes: the multi-environment detection model includes a high temperature detection model, a high humidity detection model and a high salt fog corrosion detection model; the high temperature detection model has a high temperature detection primer, and the high temperature detection primer is a preset high temperature environment threshold; the high humidity detection model has a high humidity detection primer, and the high humidity detection primer is a preset high humidity environment threshold; the high salt fog corrosion detection model has a high salt fog detection primer, and the high salt fog detection primer is a preset high salt fog environment threshold; if the target environment record matches the preset high temperature environment threshold, the preset high humidity environment threshold and the preset high salt fog environment threshold, the match is called to obtain the target detection model.

[0030] Specifically, the multi-environment detection model includes a high temperature detection model, a high humidity detection model, and a high salt spray corrosion detection model, which are respectively obtained through data training using the sensor records of the historical environment and the fire door deformation records of the same type of fire doors as the target fire doors corresponding to the sensor records. For example, the high temperature detection model is obtained through training with the temperature records and deformation records in the sensor records; the high humidity detection model is obtained through training with the humidity records and deformation records in the sensor records; and the high salt spray corrosion detection model is obtained through training with the salt spray records and deformation records in the sensor records. The high temperature detection model, the high humidity detection model, and the high salt spray corrosion detection model are detection methods established for different environmental conditions, respectively, and are used to identify and evaluate the state changes of the target fire door under different special environments. For example, the high-temperature detection model is used to detect the impact of high-temperature environment on the deformation of the target fire door. For example, fire doors in high-temperature areas such as fire are more likely to warp due to material expansion. The high-humidity detection model evaluates the effect of high-humidity environment on deformation. The high-salt spray corrosion detection model is used to analyze the degree of corrosion of the target fire door by high-salt spray. For example, metal door frames in high-salt spray environments may be deformed due to corrosion, thereby ensuring that the causes of deformation can be more accurately identified in specific environments.

[0031] The high temperature detection model has a high temperature detection primer, which is a preset high temperature environment threshold, used to determine whether the temperature environment recorded by the target environment is in a high temperature state by setting a reference temperature value. The preset high temperature environment threshold is set in combination with the temperature resistance of the target fire door material. For example, if the upper limit of the safe working temperature of the target fire door is 50 degrees Celsius, then exceeding 50 degrees Celsius will trigger the high temperature detection of the high temperature detection model.

[0032] The high humidity detection model has a high humidity detection primer, which is a preset high humidity environment threshold, which is used to determine whether the humidity environment recorded by the target environment belongs to a high humidity state by setting a reference humidity value. The preset high humidity environment threshold is set in combination with the hygroscopic performance of the target fire door material and the sealing performance of the target fire door. For example, the critical humidity of the wooden door frame of the target fire door is 80%. When the actual measured humidity reaches 90% for three consecutive hours, the high humidity detection will be triggered. This humidity level will cause the door frame to absorb water and expand by one millimeter over a long period of time (such as a week), thereby significantly affecting the opening and closing function of the door body.

[0033] The high salt spray corrosion detection model has a high salt spray detection primer, which is a preset high salt spray environment threshold, which is used to determine whether the salt spray concentration environment recorded in the target environment belongs to a high salt spray concentration state by setting a reference salt spray concentration value. The preset high salt spray environment threshold is set in combination with the corrosion resistance of the target fire door material. For example, the tolerance concentration of the aluminum alloy material of the target fire door is 5 mg / m3. When the actual environmental concentration exceeds 8 mg / m3, it is identified as a high salt spray environment, and obvious pitting corrosion may occur on the metal surface within a week. In low salt spray concentrations, pitting corrosion takes a month to appear.

[0034] If the target environment record matches the preset high temperature environment threshold, the preset high humidity environment threshold and the preset high salt fog environment threshold, the target detection model is called to match, that is, according to the comparison result of the actual environment data and the set threshold, the most suitable detection model is selected for further analysis. For example, when the environmental record shows that the temperature is 20 degrees Celsius, the humidity is 10% and the salt fog concentration is 8 mg per cubic meter, the effect of salt fog on the deformation of the target fire door is more significant, then the high salt fog corrosion detection model is called first, and other models are used as auxiliary analysis tools.

[0035] By calling appropriate detection methods according to different environmental characteristics, the cause of deformation of the target fire door can be accurately identified and processed, which not only improves the detection efficiency, but also can form targeted maintenance suggestions based on the severity of different environments.

[0036] Furthermore, the present application also includes: dividing the target fire door into areas to obtain target detection areas, the target detection model includes a target area priority, and the target area priority corresponds to the target detection area; performing area matching of the target detection area according to the target area priority to obtain the target detection area sequence; performing deformation detection on the target detection area based on the target detection area sequence to generate the target deformation data.

[0037] Specifically, the target fire door is divided into regions according to its function or structural characteristics, and the surface or structure of the target fire door is divided into several regions to obtain the target detection region, so as to more accurately detect and analyze the status of a specific region. For example, the door panel of the target fire door is divided into four parts: top, bottom, left, and right, while the hinge and door frame are divided into independent regions.

[0038] The target detection model includes a target area priority, which corresponds to the target detection area. That is, the target area priority of the target detection model is used to set a priority for each divided area of ​​the target detection area to indicate the importance of the area or the urgency of detection. For example, for the hinge area of ​​a fire door, since it directly affects the opening and closing function of the door, the priority can be set to one, while the priority of the middle part of the door panel may be lower and set to three. By dividing the priorities, the detection process can be made more targeted and resource waste can be avoided.

[0039] The detection order of the target detection areas is arranged according to the priority of the target areas, so as to form a sequence of target detection areas detected in order of importance. Deformation detection is performed on each area of ​​the target detection area according to the target detection area sequence to generate target deformation data for further analysis.

[0040] Through area division, priority setting, sequence matching and step-by-step detection, efficient and accurate analysis of fire door deformation is achieved. The generated deformation data provides a basis for subsequent processing and a basis for timely repairs.

[0041] Furthermore, the present application also includes: collecting deformation records of fire doors to extract spatial features and obtain spatial features of fire doors; collecting sensor records to extract environmental features and obtain environmental feature vectors, wherein the environmental feature vectors include time features and environmental features; performing feature splicing on the spatial features of the fire door and the environmental feature vectors to obtain fusion features; generating a self-attention mechanism based on the fusion features; and adjusting the priority of the target area according to the self-attention mechanism and feeding back to the target area priority.

[0042] Specifically, the deformation history of the fire door is collected through deformation detection equipment or image processing technology, and the spatial characteristics reflecting the geometric state of the fire door are extracted from it. For example, the displacement of the same type of fire door as the target fire door at the hinge, the bulge on the upper part of the door frame, the overall warping degree of the door body or the regional stress distribution are used to form a specific spatial feature representation.

[0043] The data of the external environment is collected through temperature and humidity sensors, salt spray monitoring equipment, etc., and converted into a feature set containing the time dimension. For example, in a high humidity environment, the sensor records show that the humidity is 95%, and the corresponding timestamp is from 8 am to 8 pm within 24 hours, forming an environmental feature vector.

[0044] The spatial feature and environmental feature vectors of the fire door are spliced ​​to obtain fusion features for subsequent analysis. The splicing process usually combines spatial features (such as deformation size and deformation direction) with environmental features (such as humidity and temperature) into a unified vector, which can reveal the potential relationship between environmental changes and deformation trends.

[0045] The self-attention mechanism in deep learning is used to weight the fused features and assign higher weights to key features. For example, in the fused features, if the spatial features of the door deformation and the features of the high humidity environment are strongly correlated, the features are emphasized, while irrelevant or low-correlated data (such as low salt spray concentration) are assigned lower weights, thereby more accurately identifying the cause of the deformation.

[0046] The detection priority of the target area is dynamically adjusted using the results of the self-attention mechanism, and the new priority information is fed back to the detection system. For example, if the self-attention mechanism shows that the deformation of the hinge area is most correlated with the characteristics of the high humidity environment, the hinge area is set as the first-level priority, while the priorities of other areas are reduced to ensure that detection resources are concentrated on the areas most likely to be affected, thereby improving efficiency and accuracy.

[0047] By collecting and fusing deformation records and environmental data and combining the self-attention mechanism to dynamically optimize regional detection priorities, the self-attention mechanism can quickly adjust the detection strategy and focus on monitoring the hinge area, providing a key basis for timely maintenance.

[0048] Furthermore, the present application also includes: calculating the attention score of the target detection area based on the fusion feature as the scoring basis to generate a target attention score; detecting the target detection area according to the target attention score to complete the self-attention mechanism.

[0049] Specifically, the attention score of the target detection area is calculated based on the fusion feature to generate the target attention score. The spatial features and environmental features of the fire door that change with time in the fusion features are used as the basis for scoring. For example, the higher the impact of environmental data on the deformation of the target fire door over time, the higher the score of the corresponding area in the target detection area. Then, the attention score of each detection area is generated through weighted calculation to obtain the target attention score corresponding to the target detection area. The target attention score reflects the importance of the area under specific conditions, ensuring that more resources are allocated to key areas and improving detection efficiency.

[0050] The target detection area is detected according to the target attention score, the detection priority is determined, and finally the self-attention mechanism is obtained. The higher the target attention score, the higher the detection priority, and vice versa.

[0051] By calculating the attention score through feature fusion and performing detection based on the score, dynamic optimization of detection efficiency is achieved. The self-attention mechanism can dynamically adapt to environmental changes and the status of fire doors, providing accurate and reliable detection results.

[0052] Furthermore, the present application also includes: determining whether the target deformation feature is a global feature or a local feature; if the target deformation feature is the local feature, determining the target detection neighborhood based on the neighborhood area threshold, enhancing the attention score of the target detection neighborhood, and feeding back to the target attention score.

[0053] Specifically, based on the deformation pattern of the fire door, we analyze whether the deformation is a global feature distributed throughout the door body, or a local feature concentrated in certain specific areas. For example, the global feature may be the bending of the entire door panel, while the local feature may be the warping of a small area near the hinge. By distinguishing, we can clarify the scope of the deformation impact, thereby guiding the subsequent detection strategy.

[0054] After determining that the deformation is a local feature, the surrounding area to be detected is further determined according to the set neighborhood threshold range to evaluate whether the deformation has extended to the surrounding area. The neighborhood area refers to the part adjacent to the local deformation area. By increasing the attention weight of the neighborhood detection area, the attention to the neighborhood area is increased, so that the neighborhood area is given priority for more detailed detection, ensuring that resources are concentrated in high-risk areas.

[0055] By judging the range of deformation features and combining the neighborhood threshold and attention scoring enhancement mechanism, we can effectively capture subtle changes in local deformation and its surrounding areas, prioritize detection of these areas, and make an accurate assessment of the deformation expansion trend, thereby improving detection accuracy and efficiency.

[0056] Furthermore, the present application also includes: dividing the deformation degree of the target deformation characteristics to determine the target deformation degree; generating a target repair strategy based on the target deformation degree, and using the target repair strategy as the warning information.

[0057] Specifically, the deformation degree of the target deformation features is divided, classified or graded, and the deformation degree of the target is determined to clarify the severity of the deformation. For example, it includes slight deformation, moderate deformation and severe deformation. The deformation degree can be divided by threshold determination, statistical analysis or expert experience to ensure that the classification results can accurately reflect the potential impact of deformation on the function of the fire door.

[0058] Generate a target repair strategy based on the target deformation degree, use the target repair strategy as early warning information, that is, formulate targeted repair measures, and provide relevant information as early warning to management or maintenance personnel. For example, for minor deformation, it is recommended to observe and record regularly; for moderate deformation, a correction plan can be formulated, such as adjusting hinges or replacing local components; for severe deformation, it is recommended to immediately stop using the fire door and conduct comprehensive repairs.

[0059] By dividing the target deformation characteristics, the complex deformation information is converted into a clear risk level, and a specific repair strategy is generated based on the degree, providing scientific guidance for the maintenance and early warning of fire doors. The dynamic adjustment mechanism can efficiently manage deformation risks and ensure the safety performance of fire doors at all stages.

[0060] In summary, the fire door deformation detection method provided by the present application has the following technical effects: by acquiring the environmental data of the target fire door, the target environment record is determined; based on the target environment record, a multi-environment detection model is matched to obtain a target detection model; through the target detection model, the target fire door is subjected to deformation detection based on a target detection area sequence to generate target deformation data; the target deformation data is subjected to deformation feature analysis to obtain target deformation features; the target fire door is warned according to the target deformation features, and the target fire door is repaired according to the warning information. That is to say, by realizing the adaptive optimization of the fire door deformation detection method in a complex environment, the technical effect of improving detection accuracy and reliability is achieved.

[0061] Embodiment 2: Based on the fire door deformation detection method in the above embodiment, the present application also provides a fire door deformation detection system, see the attached Figure 2 , including: a data acquisition module 11, the data acquisition module 11 is used to acquire the environmental data of the target fire door and determine the target environment record; a model matching module 12, the model matching module 12 is used to match the multi-environment detection model based on the target environment record to obtain the target detection model; a deformation detection module 13, the deformation detection module 13 is used to perform deformation detection on the target fire door based on the target detection area sequence through the target detection model to generate target deformation data; a feature analysis module 14, the feature analysis module 14 is used to perform deformation feature analysis on the target deformation data to obtain the target deformation feature; an early warning module 15, the early warning module 15 is used to warn the target fire door according to the target deformation feature, and repair the target fire door according to the early warning information.

[0062] Furthermore, the fire door deformation detection system is also used for: the multi-environment detection model includes a high temperature detection model, a high humidity detection model and a high salt fog corrosion detection model; the high temperature detection model has a high temperature detection primer, and the high temperature detection primer is a preset high temperature environment threshold; the high humidity detection model has a high humidity detection primer, and the high humidity detection primer is a preset high humidity environment threshold; the high salt fog corrosion detection model has a high salt fog detection primer, and the high salt fog detection primer is a preset high salt fog environment threshold; if the target environment record matches the preset high temperature environment threshold, the preset high humidity environment threshold and the preset high salt fog environment threshold, the match is called to obtain the target detection model.

[0063] Furthermore, the fire door deformation detection system is also used to: divide the target fire door into areas to obtain target detection areas, the target detection model includes target area priorities, and the target area priorities correspond to the target detection areas; perform area matching of the target detection areas according to the target area priorities to obtain the target detection area sequence; perform deformation detection on the target detection areas based on the target detection area sequence to generate the target deformation data.

[0064] Furthermore, the fire door deformation detection system is also used to: collect fire door deformation records to extract spatial features and obtain fire door spatial features; collect sensor records to extract environmental features and obtain environmental feature vectors, wherein the environmental feature vectors include time features and environmental features; perform feature splicing on the fire door spatial features and the environmental feature vectors to obtain fusion features; generate a self-attention mechanism based on the fusion features; and adjust the priority of the target area according to the self-attention mechanism and feed back to the target area priority.

[0065] Furthermore, the fire door deformation detection system is also used to: calculate the attention score of the target detection area based on the fusion feature as the scoring basis to generate a target attention score; detect the target detection area according to the target attention score to complete the self-attention mechanism.

[0066] Furthermore, the fire door deformation detection system is also used to: determine whether the target deformation feature is a global feature or a local feature; if the target deformation feature is the local feature, determine the target detection neighborhood based on the neighborhood area threshold, enhance the attention score of the target detection neighborhood, and feed back to the target attention score.

[0067] Furthermore, the fire door deformation detection system is also used to: classify the target deformation characteristics according to the deformation degree to determine the target deformation degree; generate a target repair strategy based on the target deformation degree, and use the target repair strategy as the warning information.

[0068] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The fire door deformation detection method and specific examples in the aforementioned embodiment one are also applicable to a fire door deformation detection system in this embodiment. Through the aforementioned detailed description of a fire door deformation detection method, those skilled in the art can clearly understand a fire door deformation detection system in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0069] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0070] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for detecting deformation of a fire door, characterized in that: include: Obtain the environmental data of the target fire door and determine the target environmental record; Matching a multi-environment detection model based on the target environment record to obtain a target detection model; By using the target detection model, deformation detection is performed on the target fire door based on the target detection area sequence to generate target deformation data; Performing deformation feature analysis on the target deformation data to obtain target deformation features; The target fire door is warned according to the target deformation feature, and the target fire door is repaired according to the warning information.

2. A fire door deformation detection method according to claim 1, characterized in that: The step of matching the multi-environment detection model based on the target environment record to obtain the target detection model includes: The multi-environment detection model includes a high temperature detection model, a high humidity detection model and a high salt spray corrosion detection model; The high temperature detection model has a high temperature detection primer, and the high temperature detection primer is a preset high temperature environment threshold; The high humidity detection model has a high humidity detection primer, and the high humidity detection primer is a preset high humidity environment threshold; The high salt fog corrosion detection model has a high salt fog detection primer, and the high salt fog detection primer is a preset high salt fog environment threshold; If the target environment record matches the preset high temperature environment threshold, the preset high humidity environment threshold and the preset high salt fog environment threshold, the match is called to obtain the target detection model.

3. A fire door deformation detection method according to claim 1, characterized in that: The target detection model is used to perform deformation detection on the target fire door based on a target detection area sequence to generate target deformation data, including: Divide the target fire door into regions to obtain a target detection region, wherein the target detection model includes a target region priority, and the target region priority corresponds to the target detection region; Performing area matching of the target detection area according to the target area priority to obtain the target detection area sequence; The target detection area is subjected to deformation detection based on the target detection area sequence to generate the target deformation data.

4. A fire door deformation detection method as claimed in claim 3, characterized in that: The target area priority also includes: Collect the deformation records of fire doors to extract spatial features and obtain the spatial features of fire doors; Collect sensor records to extract environmental features and obtain an environmental feature vector, wherein the environmental feature vector includes time features and environmental features; Performing feature splicing on the fire door space feature and the environmental feature vector to obtain a fusion feature; Generate a self-attention mechanism based on the fused features; The priority of the target area is adjusted according to the self-attention mechanism and fed back to the target area priority.

5. A fire door deformation detection method as claimed in claim 4, characterized in that: The generating a self-attention mechanism based on the fusion feature includes: Calculating the attention score of the target detection area based on the fusion feature to generate a target attention score; The target detection area is detected according to the target attention score to complete the self-attention mechanism.

6. A fire door deformation detection method as claimed in claim 5, characterized in that: The target attention score also includes: Determining whether the target deformation feature is a global feature or a local feature; If the target deformation feature is the local feature, the target detection neighborhood is determined based on the neighborhood area threshold, the attention score of the target detection neighborhood is enhanced, and the result is fed back to the target attention score.

7. A fire door deformation detection method according to claim 1, characterized in that: The step of providing an early warning to the target fire door according to the target deformation feature includes: Classifying the deformation degree of the target deformation feature to determine the target deformation degree; A target repair strategy is generated based on the target deformation degree, and the target repair strategy is used as the early warning information.

8. A fire door deformation detection system, characterized in that: The steps for implementing a fire door deformation detection method as described in any one of claims 1 to 7 include: A data acquisition module, the data acquisition module is used to acquire environmental data of a target fire door and determine a target environmental record; A model matching module, the model matching module is used to match the multi-environment detection model based on the target environment record to obtain a target detection model; A deformation detection module, wherein the deformation detection module is used to perform deformation detection on the target fire door based on a target detection area sequence through the target detection model to generate target deformation data; A feature analysis module, the feature analysis module is used to perform deformation feature analysis on the target deformation data to obtain target deformation features; An early warning module is used to warn the target fire door according to the target deformation characteristics and repair the target fire door according to the early warning information.

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