Door magnetic risk assessment method, system and device

Abstract

The invention relates to the technical field of door magnet data processing, and provides a door magnet risk assessment method, system and device, and the method comprises the steps: obtaining the magnetic field intensity data of a door magnet, the opening and closing time sequence characteristics of a door body and the current environment data, carrying out the threshold comparison of the magnetic field intensity data, and obtaining an analysis result; and carrying out conjoint analysis on the analysis result and the door opening and closing time sequence characteristics to obtain opening state judgment data and opening time sequence characteristic data, fusing the opening state judgment data and the opening time sequence characteristic data with the current environment data to generate a communication signal, transmitting the communication signal to a preset cloud multi-dimensional decision engine, and generating a risk level. Through a multi-layer data analysis and fusion technology, in combination with cooperative work of magnetic field intensity data, door body opening and closing time sequence characteristics, environment data and a cloud decision engine, high-precision monitoring and risk assessment of the state of the fire exit door can be realized, and the problems of relatively high false alarm rate and relatively low risk in an existing door magnetic risk assessment mode are improved. And the problem of lack of accurate distinguishing of various states is solved.

Description

Technical Field

[0001] This application relates to the technical field of door magnetic data processing, and in particular, to a method, system and device for door magnetic risk assessment. Background Art

[0002] With the acceleration of the urbanization process, the safety of fire-fighting facilities in high-rise buildings, large commercial complexes and crowded places faces severe challenges. As a core facility for isolating the spread of fire, ensuring the evacuation of personnel and property safety in case of emergencies such as fires, maintaining the normally closed state of fire doors is crucial.

[0003] In related technical means, door magnetic monitoring technology mainly relies on magnetic field sensors and battery-driven alarm devices to monitor the opening and closing state of the door body and trigger alarm signals in case of abnormalities. These technologies can achieve basic monitoring of the state of fire doors in some application scenarios, usually using the change of magnetic field intensity as the basis for judging the opening and closing of the door body. In addition, the existing technology also conducts risk assessment through a simple door magnetic alarm system, which is usually limited to the perception of the on-off state and fails to effectively combine other environmental data to improve the monitoring accuracy and the intelligent decision-making ability of the system.

[0004] For the above technical solutions, although the existing systems can effectively trigger alarms under certain conditions, especially providing instant feedback when the door body is abnormally opened, the false alarm rates of these methods are relatively high, and there is a lack of accurate distinction for multiple states. For example, it is difficult to accurately judge the common compliant half-open state and the illegal always-open state in the existing technology, which affects the reliability and effectiveness of the judgment. Summary of the Invention

[0005] In order to improve the problems of high false alarm rate and lack of accurate distinction for multiple states in the existing door magnetic risk assessment methods, this application provides a method, system and device for door magnetic risk assessment.

[0006] The present invention provides a method for door magnetic risk assessment, and the assessment method includes: obtaining the magnetic field intensity data, the opening and closing time sequence characteristics of the door body and the current environmental data of the door magnetic, comparing the magnetic field intensity data with a threshold to obtain the magnetic field attenuation amount, and calculating the opening angle of the door body by using the magnetic field attenuation amount; judging the opening state of the door body based on the opening angle of the door body to obtain an analysis result, and jointly analyzing the analysis result and the opening and closing time sequence characteristics of the door body to obtain the opening state judgment data and the opening time sequence characteristic data; fusing the opening state judgment data, the opening time sequence characteristic data and the current environmental data to generate a communication signal; preprocessing the communication signal and transmitting the preprocessed communication signal to a preset cloud multi-dimensional decision engine to generate a corresponding risk level.

[0007] As a preferred solution, the steps of obtaining the magnetic field intensity data of the door magnetic, the opening and closing timing characteristics of the door body, and the current environmental data, comparing the magnetic field intensity data with a threshold value to obtain a magnetic field attenuation amount, and calculating the opening angle of the door body by using the magnetic field attenuation amount include: deploying a number of magnetic induction chips on the contact surface between the door frame and the door body, collecting the magnetic field intensity data by using each magnetic induction chip, and recording the change times and change times of each magnetic induction chip to generate the opening and closing timing characteristics of the door body, and obtaining the current environmental data by using a sensor; calculating the magnetic field intensity change gradient of the door body based on the magnetic field intensity data, comparing the magnetic field intensity change gradient with a preset magnetic field change threshold value to obtain a magnetic field attenuation amount; performing gradient analysis on the magnetic field attenuation amount to obtain displacement data and attenuation data, and synchronously mapping the displacement data and the attenuation data to generate opening change data; calculating the opening angle of the door body according to the opening change data and the displacement data.

[0008] As a preferred solution, the steps of performing gradient analysis on the magnetic field attenuation amount to obtain displacement data and attenuation data, and synchronously mapping the displacement data and the attenuation data to generate opening change data include: using the differential fitting method to extract the gradient mutation segment of the magnetic field attenuation amount, generating a magnetic field change gradient sequence and fluctuation window data based on the gradient mutation segment, fusing the magnetic field change gradient sequence with time tag data, extracting the mutation dense section, and constructing a multi-scale time segment data group based on the mutation dense section; wherein, the time tag data refers to the synchronous mapping data formed by the time stamp sequence recorded by the magnetic induction chip and the time point of the door body state change; performing time series difference modeling on the multi-scale time segment data group to obtain the door body movement trend information and the delay response characteristics, and constructing a dynamic filtering model by using the fluctuation window data; filtering the door body movement trend information by using the dynamic filtering model to obtain attenuation data and displacement data, and synchronously mapping the displacement data and the attenuation data to generate opening change data.

[0009] As a preferred solution, the calculation formula for calculating the opening angle of the door body according to the opening change data and the displacement data is as follows: Wherein, represents the opening angle of the door body, represents the moment of the displacement data, represents the opening change data at the moment of the unit time opening increment.

[0010] As a preferred scheme, the step of judging the opening state of the door body based on the door body opening angle, obtaining an analysis result, jointly analyzing the analysis result and the door body opening and closing timing characteristics to obtain opening state judgment data and opening timing characteristic data includes: when the door body opening angle is 0°, the door body opening state is regarded as a fully closed state; when the door body opening angle is >0° and ≤15°, the door body opening state is regarded as a compliant half-open state; when the door body opening angle is ≥30°, the door body opening state is regarded as an illegal normally open state; when the opening state judgment is completed, generating an analysis result, jointly analyzing the analysis result and the door body opening and closing timing characteristics to obtain opening state judgment data and opening timing characteristic data.

[0011] As a preferred solution, the step of jointly analyzing the analysis results and the door opening and closing timing characteristics to obtain the opening state judgment data and the opening timing characteristic data includes: performing state trend clustering and angle threshold segmentation processing on the analysis results to obtain the state label sequence and angle change interval data of the door body, periodically reconstructing the change frequency data in the door opening and closing timing characteristics to obtain a periodic feature set and a transition window; performing time domain registration on the angle change interval data and the periodic feature set to obtain a state evolution trajectory, dynamically segmenting the state evolution trajectory to obtain a behavior pattern sequence. columns and abnormal candidate segments; using the behavior pattern sequence and the state label sequence to establish a cross probability matrix, and generating a state switching judgment vector based on the cross probability matrix; performing time deviation fitting on the transition window, dynamically overlapping the fitting result with the abnormal candidate segment, and obtaining a state uncertainty index; fusing the state switching judgment vector with the state uncertainty index to generate opening state judgment data, performing hierarchical structure combination and time series pattern matching on the behavior pattern sequence and the state evolution trajectory to obtain a dynamic structure sequence, and using the dynamic structure sequence to generate opening time series feature data.

[0012] As a preferred solution, the step of fusing the opening state judgment data, the opening sequence feature data, and the current environmental data to generate a communication signal includes: performing state sequence deconstruction and frequency feature extraction on the opening state judgment data to obtain the state fluctuation frequency and the state transition category, so as to construct a state distribution matrix; performing segmented fitting and time window resampling on the opening sequence feature data to extract an angle evolution sequence; performing time-varying superposition on the state distribution matrix and the angle evolution sequence to generate a state time sequence coupling vector; performing principal component extraction on the current environmental data to obtain an interference parameter group, performing nested fusion on the interference parameter group and the state time sequence coupling vector to obtain an environmental adjustment matrix, performing fuzzy interval mapping on the environmental adjustment matrix to obtain a feedback smoothing coefficient; using the feedback smoothing coefficient to perform weighted adjustment on the state distribution matrix to obtain a communication factor set, and constructing an asynchronous coding stream based on the communication factor set to generate a communication signal.

[0013] As a preferred solution, the step of preprocessing the communication signal and transmitting the preprocessed communication signal to a preset cloud multi-dimensional decision engine to generate a corresponding risk level includes: performing noise filtering and signal enhancement on the communication signal to obtain a preprocessed communication signal, and using a channel selection algorithm to identify the optimal channel for the current communication; based on the optimal channel, inputting the preprocessed communication signal into a preset cloud multi-dimensional decision engine for risk level analysis to obtain a risk level.

[0014] This application also provides a door magnetic risk assessment system, including: an acquisition module, configured to acquire the magnetic field intensity data of the door magnetic, the door opening and closing sequence features, and the current environmental data, perform threshold comparison on the magnetic field intensity data to obtain the magnetic field attenuation amount, and calculate the door opening angle using the magnetic field attenuation amount; an analysis module, configured to judge the opening state of the door based on the door opening angle to obtain an analysis result, and perform joint analysis on the analysis result and the door opening and closing sequence features to obtain the opening state judgment data and the opening sequence feature data; a fusion module, configured to fuse the opening state judgment data, the opening sequence feature data, and the current environmental data to generate a communication signal; a generation module, configured to preprocess the communication signal and transmit the preprocessed communication signal to a preset cloud multi-dimensional decision engine to generate a corresponding risk level.

[0015] This application also provides a door magnetic risk assessment device, including a memory and a processor, where the memory stores a computer program that can run on the processor, and when the processor executes the computer program, it implements the intelligent fire door state monitoring method described above.

[0016] Compared with the prior art, the present application has the following beneficial effects: low false alarm rate and high precision in classification. Through multi-layer data analysis and fusion technology, combined with the collaborative work of magnetic field intensity data, door opening and closing timing characteristics, environmental data, and the cloud decision engine, high-precision monitoring and risk assessment of the fire door state can be achieved. By comparing the threshold of the magnetic field intensity data, the opening angle of the door body can be calculated and the opening state can be judged. Through the joint analysis of the opening state judgment data and the opening timing characteristic data, the accuracy of the state judgment is further improved. When generating the communication signal, weighted fusion is carried out in combination with the environmental data, which not only provides real-time information on the door magnetic state, but also enhances the stability and reliability of the signal transmission. The preprocessed communication signal is transmitted to the cloud decision engine, and using multi-dimensional data support, a risk level is generated, greatly improving the intelligence and emergency response ability of the fire door state monitoring, and improving the problem that the existing door magnetic risk assessment method has a relatively high false alarm rate and lacks precise discrimination of multiple states. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] The structures, ratios, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those skilled in this technology to understand and read, and are not used to limit the limited conditions under which the present invention can be implemented. Therefore, they do not have a substantial technical meaning. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.

[0019] Figure 1 is a schematic flow chart of the door magnetic risk assessment method provided by an embodiment of the present invention; Figure 2 is a schematic block diagram of the structure of the door magnetic risk assessment system provided by an embodiment of the present invention; Figure 3 is a schematic block diagram of the structure of the door magnetic risk assessment device provided by an embodiment of the present invention.

[0020] DESCRIPTION OF THE REFERENCE NUMERALS 10. Door magnetic risk assessment system; 11. Acquisition module; 12. Analysis module; 13. Fusion module; 14. Generation module; 20. Door magnetic risk assessment device; 21. Memory; 22. Processor. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0022] The flowchart shown in the accompanying drawings is only an example, and does not necessarily include all contents and operations / steps, nor does it necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, combined or partially merged, so the actual execution order may change according to the actual situation.

[0023] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0024] It should be further understood that the term " / and" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0025] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and through specific embodiments.

[0026] Embodiment 1: As Figure 1 shown, the present application provides a method for evaluating the risk of a door magnetic sensor. The evaluation method includes steps S100 to S400.

[0027] Step S100: Obtain the magnetic field intensity data of the door magnetic sensor, the timing characteristics of the door opening and closing, and the current environmental data, compare the magnetic field intensity data with a threshold to obtain the magnetic field attenuation amount, and calculate the opening angle of the door body using the magnetic field attenuation amount.

[0028] In this step, multiple magnetic induction chips deployed on the contact surface between the door frame and the door body collect the magnetic field intensity data. These chips judge the opening and closing state of the door body according to the real-time changing magnetic field intensity. Specifically, each magnetic induction chip is responsible for monitoring the local magnetic field intensity of the door body. By calculating the attenuation amount of the magnetic field intensity, the change in the opening of the door body can be judged, and further the opening angle of the door body can be deduced using this attenuation amount. By comparing with a preset threshold, different opening states of the door body can be identified, and the opening angle of the door body can be accurately calculated.

[0029] For example, when the door opening is ≤15°, the change in magnetic field strength is small and the magnetic field attenuation is low; when the door opening increases, the magnetic field strength changes more significantly, resulting in a greater magnetic field attenuation. This change is used to infer the specific opening angle of the door.

[0030] Step S200, judging the opening state of the door based on the door opening angle, obtaining an analysis result, and jointly analyzing the analysis result and the door opening and closing timing characteristics to obtain opening state judgment data and opening timing characteristic data.

[0031] In this step, the door opening angle is judged to determine the door opening state. Specifically, when the door opening angle is 0°, the door state is judged to be fully closed; when the opening angle is greater than 0° and less than or equal to 15°, it is judged to be a compliant half-open state; when the door opening angle is greater than or equal to 30°, it is judged to be a non-compliant normally open state. Afterwards, the analysis result is jointly analyzed with the door opening and closing timing feature data to obtain the opening state judgment data and the opening timing feature data for subsequent evaluation and decision-making.

[0032] For example, if the door opening angle is 0°, the door will be judged to be completely closed, indicating that the door magnetic device is in normal working condition; if the door opening angle is greater than 30°, it will be judged as a normally open state and an alarm will be triggered. By jointly analyzing the opening state judgment data and the timing feature data, the state of the door magnetic can be evaluated more accurately.

[0033] Step S300: Fusing the opening state judgment data, the opening time series characteristic data and the current environment data to generate a communication signal.

[0034] In this step, the opening state judgment data and the opening time series characteristic data are fused with the current environmental data (such as temperature, humidity, smoke and other environmental data). Specifically, by weighting these data, a more accurate communication signal can be generated. The fusion process of these data takes into account the state of the door magnet and the changes in the surrounding environment, so that the generated signal is not only based on the switch state of the door magnet, but also includes the influence of environmental factors, thereby enhancing the judgment ability.

[0035] For example, when the ambient temperature is high, it will affect the normal operation of the door sensor. The communication signal will be adjusted according to the environmental data to ensure accurate signal transmission and response.

[0036] Step S400: pre-process the communication signal, and transmit the pre-processed communication signal to a preset cloud-based multi-dimensional decision engine to generate a corresponding risk level.

[0037] In this step, the generated communication signal is preprocessed, mainly including noise filtering and signal enhancement. Specifically, during the preprocessing process, the noise generated by external interference is removed, and the effective part of the signal is enhanced to ensure the clarity and accuracy of data transmission. The preprocessed signal will be transmitted to the cloud multi-dimensional decision-making engine for risk assessment. The cloud engine uses the existing data model, combines the magnetic door status, environmental data and historical data to generate the final risk level, helping the management personnel to understand the safety status of the fire door in a timely manner.

[0038] For example, if the door is in an illegal open state for a long time and there is high temperature or smoke in the surrounding environment, this signal will be transmitted to the cloud after preprocessing. The cloud decision-making engine will calculate a relatively high fire risk level for the door by combining this information.

[0039] In this embodiment, through the acquisition of the magnetic field intensity data of the magnetic door, the opening and closing time sequence characteristics of the door body, and the current environmental data, the magnetic field intensity data is compared with a threshold value to calculate the magnetic field attenuation amount, and then the opening angle of the door body is calculated by using the magnetic field attenuation amount. Then, based on the opening angle of the door body, the opening state of the door body is judged, and this analysis result is jointly analyzed with the opening and closing time sequence characteristics of the door body to obtain the opening state judgment data and the opening time sequence characteristic data. Then, the opening state judgment data, the opening time sequence characteristic data, and the current environmental data are further fused to generate a communication signal. Finally, the generated communication signal is preprocessed, and the preprocessed communication signal is transmitted to a preset cloud multi-dimensional decision-making engine to generate the final risk level. Through multi-dimensional data fusion and comprehensive analysis, the accuracy and response efficiency of fire door monitoring are significantly improved. First, by combining the magnetic field intensity data and the opening angle, the different opening states of the door body can be accurately distinguished, significantly reducing the false alarm rate caused by misjudgment in the traditional technology. Second, the joint analysis of the opening and closing time sequence characteristics of the door body and the environmental data provides more comprehensive information for risk assessment, avoiding the blind area brought by a single data source in the existing technology. In addition, the combination of the preprocessing of the communication signal and the cloud decision-making engine enhances the stability and intelligent decision-making ability in complex environments, improves the accuracy of risk assessment, and improves the problem of high false alarm rate and lack of accurate distinction of multiple states in the existing magnetic door risk assessment method.

[0040] Embodiment 2: In step S100, several magnetic induction chips are deployed on the contact surface between the door frame and the door body. The magnetic field intensity data is collected by each magnetic induction chip, and the change times and change times of each magnetic induction chip are recorded to generate the opening and closing time sequence characteristics of the door body, and the current environmental data is obtained by using a sensor.

[0041] By deploying a number of magnetic induction chips at the contact surface between the door frame and the door body, magnetic field intensity data is collected by each magnetic induction chip, and the change times and change times of each magnetic induction chip are recorded to generate the timing characteristics of the door body opening and closing. Specifically, each magnetic induction chip is responsible for detecting the magnetic field change in the contact area between the door body and the door frame. By continuously tracking the change of the magnetic field intensity, the change data at each time point is recorded, and these data are transmitted to the processing unit in real time to generate the timing characteristics of the door body opening and closing, finally providing an accurate basis for calculating the opening angle of the door body. The state of the door body can be analyzed by real-time tracking of these data, so as to obtain the dynamic change of the opening angle.

[0042] For example, during the process of the door body gradually opening from a fully closed state, each magnetic induction chip records the corresponding magnetic field intensity changes at different time points. Through these changing data, a timing characteristic map of the door body opening and closing can be constructed, providing key time series data for subsequent calculation of the magnetic field attenuation amount and judgment of the opening angle.

[0043] Calculate the magnetic field intensity change gradient of the door body based on the magnetic field intensity data, and compare the magnetic field intensity change gradient with a preset magnetic field change threshold to obtain the magnetic field attenuation amount.

[0044] By performing differential calculation on the magnetic field intensity data, the magnetic field intensity change gradient of the door body is obtained. Specifically, by comparing the magnetic field intensities collected at different time points, the magnetic field change rate between two points is calculated. Then, these change rates are compared with a preset magnetic field change threshold. If the change rate exceeds the threshold, it is considered that the door body has opened or closed, and thus the magnetic field attenuation amount is deduced. Through this process, the opening degree and opening and closing dynamics of the door body can be accurately judged.

[0045] For example, assume that between time and , the magnetic field intensity changes from 500 G to 450 G, and the change rate is 50 G. If the preset threshold is 40 G, it will be considered that the door body has an opening or closing action between and , and the corresponding magnetic field attenuation amount is calculated as 50 G.

[0046] Perform gradient analysis on the magnetic field attenuation amount to obtain displacement data and attenuation data, and perform synchronous mapping on the displacement data and the attenuation data to generate opening degree change data.

[0047] By performing gradient analysis on the magnetic field attenuation, displacement data and attenuation data are obtained. Specifically, the gradient analysis method is used to further refine the change of the magnetic field attenuation, and the displacement data and attenuation data at each moment are obtained. Then, the displacement data and the attenuation data are synchronously mapped, and further the opening degree change data of the door body is deduced. Through this method, every detail in the opening and closing process of the door body can be captured very precisely, and the dynamic change data of the door body opening degree is generated.

[0048] For example, during the gradient analysis process, if there is a sudden change in the magnetic field attenuation at a certain moment, the actual displacement of the door body will be calculated through this mutation point, and further the change of the opening degree of the door body will be deduced, so as to ensure that the actual state of the door body can be judged in time in any opening and closing state.

[0049] Among them, the steps of performing gradient analysis on the magnetic field attenuation to obtain displacement data and attenuation data, and synchronously mapping the displacement data and the attenuation data to generate opening degree change data include: using the differential fitting method to extract the gradient mutation section of the magnetic field attenuation, generating a magnetic field change gradient sequence and fluctuation window data based on the gradient mutation section, fusing the magnetic field change gradient sequence with time tag data, extracting the mutation dense section, and constructing a multi-scale time segment data group based on the mutation dense section; among them, the time tag data refers to the synchronous mapping data formed by the time stamp sequence recorded by the magnetic induction chip and the time point of the door body state change.

[0050] By using the differential fitting method to extract the gradient mutation section of the magnetic field attenuation, the mutation points in the magnetic field change can be accurately captured, and these mutation sections indicate the critical moments of the door body opening and closing. Specifically, by analyzing these mutation sections, a magnetic field change gradient sequence and fluctuation window data can be generated, and these data are helpful to judge the opening and closing speed of the door body and its change trend. In addition, fusing the magnetic field change gradient sequence with time tag data can accurately synchronize the state of the door body from multiple time points, so as to generate a multi-scale time segment data group, and further improve the recognition accuracy of the door body opening degree change.

[0051] For example, if the magnetic field attenuation changes violently in a short time, the change mutation section is extracted by the differential fitting method, and the corresponding gradient sequence is generated. Combining with the time tag data, these data are used to accurately determine the opening degree change of the door body, while ensuring the time synchronization of the data, and finally generating accurate opening degree change data.

[0052] Perform time series difference modeling on the multi-scale time segment data group to obtain the door body movement trend information and delay response characteristics, and use the fluctuation window data to construct a dynamic filtering model.

[0053] By performing time-series difference modeling on the multi-scale time segment data set, the moving trend of the door body can be dynamically modeled. Specifically, difference calculations are carried out according to the change trend of each time segment to infer the opening and closing trend and delay response characteristics of the door body. This modeling process helps to identify the state change characteristics of the door body and provides accurate input data for subsequent dynamic filtering. At the same time, by using the fluctuating window data to construct a dynamic filtering model, the influence of environmental interference on the door magnetic signal can be reduced without losing accuracy.

[0054] For example, through time-series difference modeling, if the opening and closing process of the door body shows a slow change trend, the response characteristics of the door body will be adjusted accordingly to ensure accurate data is captured in a timely manner when the state of the door body changes. At the same time, through the fluctuating window model, interference signals caused by external factors (such as wind force, vibration, etc.) can be effectively filtered out.

[0055] The moving trend information of the door body is filtered by using the dynamic filtering model to obtain attenuation data and displacement data, and the displacement data and attenuation data are synchronously mapped to generate opening degree change data.

[0056] The moving trend information of the door body is filtered by the dynamic filtering model. Specifically, the dynamic filtering method is applied to remove error data caused by environmental changes or equipment noise, so as to ensure the accuracy of the displacement and attenuation data of the door body. The attenuation data and displacement data obtained after filtering will be synchronously mapped, and through this mapping relationship, more accurate opening degree change data will be generated, providing stable and reliable input for subsequent opening degree angle calculation.

[0057] For example, during the opening and closing process of the door body, if external vibration or electromagnetic interference affects the magnetic field change, the dynamic filtering model will effectively filter out these interferences, maintain the accuracy of the data, and ensure that the generated opening degree change data can accurately reflect the actual state of the door body.

[0058] The opening degree angle of the door body is calculated based on the opening degree change data and displacement data.

[0059] Among them, the calculation formula for calculating the opening degree angle of the door body based on the opening degree change data and displacement data is as follows: Among them, represents the opening degree angle of the door body, represents the time of the displacement data, represents the opening degree change data at the time of the unit time opening degree increment.

[0060] By combining the opening degree change data and the displacement data, the opening angle of the door body is calculated using a formula. Specifically, the opening angle of the door body is calculated by a formula, which combines the real-time data of displacement and opening degree change and can accurately reflect the opening situation of the door body.

[0061] For example, assuming the displacement data of the door body is 5 mm and the unit time increment of the opening degree change data is 2 mm, the calculated opening angle is a specific value, thereby confirming the opening and closing state of the door body.

[0062] In step S200, when the opening angle of the door body is 0°, the opening state of the door body is regarded as the fully closed state; when the opening angle of the door body > 0° and ≤ 15°, the opening state of the door body is regarded as the compliant semi-open state; when the opening angle of the door body ≥ 30°, the opening state of the door body is regarded as the illegal fully open state.

[0063] Through the precise calculation based on the opening angle of the door body, the opening state of the door body can be accurately judged. Specifically, if the opening angle of the door body is 0°, the state of the door body will be marked as the fully closed state, indicating that the door body is in the normal closed state and there is no risk. If the opening angle of the door body is greater than 0° and less than or equal to 15°, the door body will be marked as the compliant semi-open state, which is suitable for some temporary needs, such as ventilation or personnel access. If the opening angle of the door body is greater than or equal to 30°, it is considered that the door body is in the illegal fully open state, and this situation usually requires immediate alarm handling to prevent the ineffective isolation of the fire source when a fire occurs.

[0064] For example, assuming the opening angle of the door body is 0°, the door body will be automatically marked as the closed state; when the opening angle is 12°, it will be recognized as the compliant semi-open state and this state is allowed to exist, but if the opening angle is 35°, it will be marked as the illegal fully open state and the corresponding alarm will be triggered.

[0065] After the opening state judgment is completed, an analysis result is generated, and the analysis result and the opening and closing timing characteristics of the door body are jointly analyzed to obtain the opening state judgment data and the opening timing characteristic data.

[0066] By jointly analyzing the opening state judgment data and the opening and closing timing characteristic data of the door body, the changing trend of the door body state can be evaluated more precisely. Specifically, the opening state judgment data of the door body provides the current opening information of the door body, while the timing characteristic data provides the change history of the opening and closing state of the door body. By combining these two, it is possible to capture whether there are abnormal opening and closing behaviors of the door body, and further generate the opening state judgment data and the timing characteristic data for risk assessment.

[0067] For example, if the frequency of the opening state change of the door body is detected to be high and there are frequent violations of the normally open state, after joint analysis, it will be possible to identify potential safety hazards of the door body and provide a basis for subsequent decision-making.

[0068] Among them, the steps of jointly analyzing the analysis result and the opening and closing time sequence characteristics of the door body to obtain the opening state judgment data and the opening time sequence characteristic data include: performing state trend clustering and angle threshold segmentation processing on the analysis result to obtain the state label sequence and the angle change interval data of the door body, and performing periodic reconstruction on the change frequency data in the opening and closing time sequence characteristics of the door body to obtain the periodic feature set and the transition window.

[0069] By performing state trend clustering on the analysis result, different states of the door body opening (such as fully closed, compliant half-open, and illegally normally open) are classified. Specifically, through angle threshold segmentation processing, multiple state label sequences can be generated according to different ranges of the opening angle (for example, 0°-15°, 15°-30°, etc.), and then the opening and closing states of the door body can be refined and analyzed. At the same time, performing periodic reconstruction on the change frequency data in the opening and closing time sequence characteristics of the door body can identify the regular patterns of the door body opening and closing, generate the periodic feature set and the transition window for further analysis.

[0070] For example, when the door body is in the compliant half-open state, a specific label will be assigned to this state, and its opening and closing cycle will be reconstructed to obtain the periodic feature set. For example, if the door body opens and closes once every 3 hours and the total opening and closing time is less than 15 minutes, it will be marked as the compliant half-open state and judged whether it meets the safety requirements according to the set threshold.

[0071] Perform time-domain registration on the angle change interval data and the periodic feature set to obtain the state evolution trajectory, and perform dynamic segment splitting on the state evolution trajectory to obtain the behavior pattern sequence and the abnormal candidate segment.

[0072] By performing time-domain registration on the angle change interval data and the periodic feature set, it is possible to accurately align the state change data at different time points. Specifically, through time-domain registration of the door body opening angle change interval, different time periods of the door body opening and closing state change can be corresponding to a continuous state evolution trajectory, and dynamic segment splitting is performed on this trajectory, so as to obtain the behavior pattern sequence and the abnormal candidate segment. This process helps to identify the normal behavior pattern and potential abnormal behavior of the door body.

[0073] For example, if the door body frequently changes its state (from fully closed to half-open to normally open) at a certain moment, the abnormal candidate segment can be identified through dynamic segment splitting, such as the door body being in the illegally normally open state for a long time, thereby providing data support for subsequent risk assessment.

[0074] Establish a cross - probability matrix using the behavior pattern sequence and the state label sequence, and generate a state transition decision vector based on the cross - probability matrix.

[0075] By using the cross - data of the behavior pattern sequence and the state label sequence, a cross - probability matrix can be established to judge the switching probability between different states. Specifically, by calculating the transition frequencies between different states (such as closed, compliant semi - open, and non - compliant fully open), a state transition decision vector can be generated. This decision vector is used to identify whether there are frequent state transitions or abnormal state changes, and then to judge whether there are risks in the door body.

[0076] For example, if it is detected that the frequency of the door body switching from the compliant semi - open state to the non - compliant fully open state within a short period is too high, the cross - probability matrix will show a high switching probability, and then generate a state transition decision vector to help identify potential safety hazards in the door body.

[0077] Perform time - deviation fitting on the transition window, and dynamically overlap the fitting result with the abnormal candidate segment to obtain the state uncertainty index.

[0078] By performing time - deviation fitting on the transition window, the time delay of the door body state transition can be accurately evaluated. Specifically, by fitting the time deviation of the transition window, the time difference between the door body switching from one state to another can be estimated, and it is dynamically overlapped with the abnormal candidate segment to obtain the state uncertainty index. This index helps to identify the instability and potential risks of the door body state.

[0079] For example, if the time deviation of the door body state change is large and the switching is frequent, this change will be regarded as a potential unstable factor, and the risks existing in the door body will be reflected through the uncertainty index.

[0080] Fuse the state transition decision vector and the state uncertainty index to generate opening - degree state judgment data, perform hierarchical structure combination and time - series pattern matching on the behavior pattern sequence and the state evolution trajectory to obtain a dynamic structure sequence, and use the dynamic structure sequence to generate opening - degree time - series feature data.

[0081] By fusing the state transition decision vector and the state uncertainty index, the state stability of the door body can be comprehensively evaluated. Specifically, through the fusion of these two data, a more accurate judgment data for the state of the door body can be generated. In addition, through the hierarchical structure combination and time - series pattern matching of the behavior pattern sequence and the state evolution trajectory, a dynamic structure sequence is generated. Finally, using this dynamic structure sequence, accurate opening - degree time - series feature data can be generated.

[0082] For example, when the door body frequently switches states within a certain period of time with great uncertainty, the evaluation strategy for the opening state will be automatically adjusted to generate more accurate time-series feature data, ensuring that the state of the door body can be accurately evaluated under various complex conditions.

[0083] In step S300, the state sequence of the opening state judgment data is deconstructed and the frequency characteristics are extracted to obtain the state fluctuation frequency and the state transition category, so as to construct a state distribution matrix. The opening time-series feature data is segmented and fitted and resampled with a time window to extract the angle evolution sequence. The state distribution matrix and the angle evolution sequence are superimposed in a time-varying manner to generate a state time-series coupling vector.

[0084] By deconstructing the state sequence of the opening state judgment data, the transitions and fluctuation characteristics between different states can be identified. Specifically, by performing periodic analysis on the state sequence, the state fluctuation frequency during the opening and closing process of the door body can be extracted, and the state transition category (such as the transition from compliant half-open to illegal fully-open) can be further identified. Then, by constructing a state distribution matrix, the data of these fluctuation frequencies and transition categories are quantitatively represented. At the same time, the opening time-series feature data is segmented and fitted and resampled with a time window to extract the angle evolution sequence. On this basis, the state distribution matrix and the angle evolution sequence are superimposed in a time-varying manner to generate a time-series coupling vector for further analyzing the opening and closing rules of the door body and predicting potential risks.

[0085] For example, if the door body is frequently in the compliant half-open state during a certain period and occasionally switches to the illegal fully-open state, this fluctuation frequency and state transition category will be first identified. Then, through resampling with a time window, the change trajectory of the door body opening angle will be extracted, and this change information will be fused with the state transition data to generate a time-varying superimposed state time-series coupling vector, thereby helping to evaluate whether there are potential safety risks for the door body.

[0086] Principal components of the current environmental data are extracted to obtain a group of interference parameters. The group of interference parameters and the state time-series coupling vector are nested and fused to obtain an environmental adjustment matrix. The environmental adjustment matrix is subjected to fuzzy interval mapping to obtain a feedback smoothing coefficient.

[0087] By extracting the principal components of the current environmental data, the most influential environmental factors (such as temperature, humidity, air pressure, etc.) can be extracted as interference parameters. Specifically, the principal component analysis (PCA) method is used to reduce the dimension of the multi-dimensional environmental data to extract the group of interference parameters that most affect the door magnetic state. These parameters will be nested and fused with the state time-series coupling vector to form a comprehensive environmental adjustment matrix for reflecting the influence of environmental factors on the door magnetic state. Then, fuzzy interval mapping will be performed on this environmental adjustment matrix to obtain a feedback smoothing coefficient, which is used to balance the influence of environmental interference on the communication signal.

[0088] For example, if the temperature rises or the humidity changes significantly, resulting in performance fluctuations of the door magnetic device, these environmental factors will be converted into interference parameters through principal component extraction. Then, the fusion of the interference parameter group and the state time series coupling vector will help accurately calculate the environmental impact factors in the change of the door state, and the data will be adjusted through the feedback smoothing coefficient obtained by fuzzy interval mapping to ensure the accuracy of state evaluation.

[0089] The state distribution matrix is weighted and adjusted using the feedback smoothing coefficient to obtain a set of communication factors, and an asynchronous coding stream is constructed based on the set of communication factors to generate a communication signal.

[0090] By applying the feedback smoothing coefficient to the state distribution matrix, the weights of different states can be dynamically adjusted. Specifically, a set of communication factors is obtained through the weighted adjusted state distribution matrix, and these factors reflect the reliability and risk degree of each state under different environmental conditions. Subsequently, an asynchronous coding stream is constructed using these sets of communication factors to ensure the stability and accuracy of the communication signal.

[0091] For example, if the door is in an illegal always-open state and the surrounding environment temperature is high, a higher weight will be assigned to this state, and the importance of this state will be highlighted in the generated set of communication factors. Based on these sets of factors, an asynchronous coding stream will be constructed, and the signal will be transmitted to the cloud through the optimal communication path to ensure efficient transmission in a complex environment.

[0092] In step S400, the communication signal is filtered for noise and enhanced, and the preprocessed communication signal is obtained, and the optimal channel for the current communication is identified using a channel selection algorithm.

[0093] By adopting noise filtering and signal enhancement technologies, the noise caused by environmental interference or device failures can be removed to ensure the stability and accuracy of the communication signal. Specifically, the transmission signal is monitored in real time through spectrum analysis and filtering algorithms, and the low-frequency noise, interference signals, and redundant data in the signal are removed. The filtered signal will determine the optimal communication channel through the channel selection algorithm, avoiding communication failures caused by frequency band interference or signal loss. This algorithm dynamically selects the best channel according to the real-time communication quality, signal strength, and time delay, thus ensuring the accurate transmission of data.

[0094] For example, if it is detected that the interference of the current channel signal is strong, the channel selection algorithm will automatically switch to an idle channel and adjust the communication strategy according to the real-time measured signal strength and the availability of the channel to ensure stable signal transmission even in a complex environment or when the signal attenuation is large.

[0095] Input the preprocessed communication signal into a preset cloud multi-dimensional decision engine based on the optimal channel for risk level analysis to obtain the risk level.

[0096] By inputting the preprocessed communication signal into the cloud multi-dimensional decision engine, complex data analysis and processing can be carried out to generate the final risk level. Specifically, the cloud decision engine combines multiple input data sources (including door opening data, environmental data, historical data, etc.), and uses deep learning models and rule-based reasoning methods to evaluate the risk level of the current door magnetic. It will calculate the safety of the door opening and closing state according to the comparison between real-time data and historical patterns, and finally generate a risk level (such as safe, warning or high risk), and send an alarm notice or maintenance instruction to the management personnel.

[0097] For example, if the door remains in an illegally open state for a long time and the surrounding environmental temperature is high, combining these data, the cloud decision engine will judge that the fire risk of the door is relatively high, and then rate its risk level as "high risk" and issue an alarm immediately. This decision-making process is based on a comprehensive analysis of the door opening and closing state, environmental changes, and the learned historical data model to ensure the accuracy and timeliness of the judgment results.

[0098] In this embodiment, an intelligent door magnetic risk assessment method is constructed through the accurate identification of the door magnetic state, the comprehensive analysis of environmental data, and the optimization of signal processing. First, by deploying multiple magnetic induction chips at the contact surface between the door frame and the door body, the magnetic field intensity data is collected in real time, and combined with the opening degree time series characteristics and environmental data, the opening degree state of the door body is accurately judged. Then, gradient analysis is performed on these data to generate opening degree change data, and the opening degree angle of the door body is generated through synchronous mapping and displacement data. By combining these accurate door body state data, different opening degree states (such as fully closed, compliant half-open, illegally open) can be identified and intelligent judgments can be made. In the further analysis of the state data, through means such as time series difference modeling and state trend clustering, the behavior pattern of the door body is deeply analyzed, periodic characteristics are extracted, and potential abnormal states are identified. The extraction and fusion of the main components of environmental data further enhance the adaptability to external interference and ensure stable operation under various environmental conditions. In terms of the processing of communication signals, through noise filtering, signal enhancement, and channel selection algorithms, the efficient transmission and accuracy of data are ensured, and finally the risk level of the door body is generated through the cloud decision engine. The entire solution significantly improves the accuracy and reliability of door magnetic state monitoring, reduces the false alarm rate, improves the emergency response efficiency, adapts to complex environments and extreme situations, and ensures the safety and reliability of fire doors through the application of multi-dimensional data fusion and intelligent algorithms.

[0099] Embodiment 3: Such as Figure 2As shown, the present application also provides a door magnetic risk assessment system 10 , including an acquisition module 11 , an analysis module 12 , a fusion module 13 and a generation module 14 .

[0100] The acquisition module 11 is mainly used to obtain the magnetic field strength data of the door magnet, the timing characteristics of the door opening and closing, and the current environmental data, perform threshold comparison on the magnetic field strength data, obtain the magnetic field attenuation, and use the magnetic field attenuation to calculate the door opening angle.

[0101] The analysis module 12 is mainly used to judge the opening state of the door based on the door opening angle, obtain the analysis result, and jointly analyze the analysis result and the door opening and closing timing characteristics to obtain the opening state judgment data and the opening timing characteristic data.

[0102] The fusion module 13 is mainly used to fuse the opening state judgment data, the opening time series characteristic data and the current environment data to generate a communication signal.

[0103] The generation module 14 is mainly used to pre-process the communication signal and transmit the pre-processed communication signal to a preset cloud-based multi-dimensional decision engine to generate a corresponding risk level.

[0104] In this embodiment, by deploying multiple magnetic sensing chips on the contact surface of the door body and the door frame, it is possible to collect magnetic field strength data, door opening and closing timing characteristics and current environmental data in real time. Specifically, these magnetic sensing chips are responsible for monitoring the changes in the magnetic field when the door body is opened and closed, while recording the timestamp of the changes, and transmitting the collected data to the analysis module 12. By comparing and analyzing the magnetic field strength data with the environmental data, it is possible to timely identify the opening state of the door body, conduct risk assessment, and provide reliable data support for the subsequent decision engine. By detecting the changes in the magnetic field strength data in real time, it is possible to accurately judge whether the door body is in a fully closed, compliant half-open or illegal normally open state during the door body opening and closing process, and combine environmental data (such as temperature, humidity, etc.) to evaluate the risk of the current door magnet.

[0105] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned embodiment 1, and will not be repeated here.

[0106] Embodiment 4: like Figure 3 As shown, the present application also provides a door magnetic risk assessment device 20, including a memory 21 and a processor 22, the memory 21 stores a computer program that can be run on the processor 22, and the processor 22 implements the intelligent fire door status monitoring method of Example 1 when executing the computer program.

[0107] In this embodiment, by storing a computer program for executing the intelligent fire door status monitoring method described in Embodiment 1 in the memory 21, when the processor 22 executes this program, it can realize the automated operation of all steps. Specifically, this computer program includes obtaining magnetic field intensity data, opening and closing timing characteristics, and environmental data from the door magnetic device, and using a preset algorithm to process the data, further determining the door opening state, and generating a risk level through data fusion and a decision engine. By executing this program, the processor 22 can automatically and precisely evaluate the door magnetic state, and send the result to the cloud for decision-making through a preset communication protocol to provide an instant response.

[0108] The structures, ratios, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions under which the present invention can be implemented. Therefore, they do not have a substantial technical meaning. Any modification of the structure, change in the ratio relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.

[0109] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A door magnetic risk assessment method, characterized in that: The evaluation methods include: Obtain the magnetic field strength data of the door magnet, the timing characteristics of the door opening and closing, and the current environmental data, perform a threshold comparison on the magnetic field strength data, obtain the magnetic field attenuation, and use the magnetic field attenuation to calculate the door opening angle; Based on the door opening angle, the door opening state is judged to obtain an analysis result, and the analysis result and the door opening and closing timing characteristics are jointly analyzed to obtain opening state judgment data and opening timing characteristic data; The opening state judgment data, the opening time series characteristic data and the current environment data are integrated to generate a communication signal; The communication signal is preprocessed and the preprocessed communication signal is transmitted to a preset cloud-based multi-dimensional decision engine to generate a corresponding risk level.

2. The door magnetic risk assessment method according to claim 1, characterized in that: The step of obtaining magnetic field strength data of the door magnet, the timing characteristics of the door opening and closing, and the current environment data, performing threshold comparison on the magnetic field strength data to obtain the magnetic field attenuation, and calculating the door opening angle using the magnetic field attenuation includes: Several magnetic sensing chips are deployed on the contact surface between the door frame and the door body. The magnetic field strength data is collected by each magnetic sensing chip, and the number of changes and change time of each magnetic sensing chip are recorded to generate the timing characteristics of the door body opening and closing, and the current environmental data is obtained by using sensors; Calculate the magnetic field intensity change gradient of the door body based on the magnetic field intensity data, compare the magnetic field intensity change gradient with a preset magnetic field change threshold, and obtain the magnetic field attenuation; Performing gradient analysis on the magnetic field attenuation to obtain displacement data and attenuation data, synchronously mapping the displacement data and the attenuation data to generate opening change data; The door opening angle is calculated according to the opening change data and the displacement data.

3. The door magnetic risk assessment method according to claim 2, characterized in that: The step of performing gradient analysis on the magnetic field attenuation to obtain displacement data and attenuation data, and synchronously mapping the displacement data and the attenuation data to generate the opening change data comprises: The gradient mutation segment of the magnetic field attenuation is extracted by using the differential fitting method, and a magnetic field change gradient sequence and fluctuation window data are generated based on the gradient mutation segment. The magnetic field change gradient sequence is fused with the time label data to extract the mutation-intensive segment, and a multi-scale time segment data group is constructed based on the mutation-intensive segment; wherein the time label data refers to the synchronous mapping data formed by the timestamp sequence recorded by the magnetic induction chip and the time point of the door body state change; Performing time series difference modeling on the multi-scale time segment data group to obtain door movement trend information and delayed response characteristics, and constructing a dynamic filtering model using the fluctuation window data; The dynamic filtering model is used to filter the door movement trend information to obtain attenuation data and displacement data, and the displacement data and the attenuation data are synchronously mapped to generate opening change data.

4. The door magnetic risk assessment method according to claim 2, characterized in that: The calculation formula for calculating the door opening angle according to the opening change data and the displacement data is as follows: in, Indicates the door opening angle. Indicates time The displacement data, Indicates the opening change data at time The opening increment per unit time.

5. The door magnetic risk assessment method according to claim 1, characterized in that: The step of judging the opening state of the door based on the door opening angle, obtaining an analysis result, and jointly analyzing the analysis result and the door opening and closing timing characteristics to obtain opening state judgment data and opening timing characteristic data comprises: When the door opening angle is 0°, the door opening state is regarded as a fully closed state; When the door opening angle is >0° and ≤15°, the door opening state is regarded as a compliant half-open state; When the door opening angle is ≥30°, the door opening state is regarded as an illegal normally open state; When the opening state judgment is completed, an analysis result is generated, and the analysis result and the door opening and closing timing characteristics are jointly analyzed to obtain opening state judgment data and opening timing characteristic data.

6. The door magnetic risk assessment method according to claim 5, characterized in that: The step of jointly analyzing the analysis result and the door opening and closing timing characteristics to obtain the opening state judgment data and the opening timing characteristic data comprises: The analysis results are subjected to state trend clustering and angle threshold segmentation processing to obtain a state label sequence and angle change interval data of the door body, and the change frequency data in the door body opening and closing timing characteristics are periodically reconstructed to obtain a periodic feature set and a transition window; Performing time domain registration on the angle change interval data and the periodic feature set to obtain a state evolution trajectory, and performing dynamic segment splitting on the state evolution trajectory to obtain a behavior pattern sequence and an abnormal candidate segment; Establishing a crossover probability matrix using the behavior pattern sequence and the state label sequence, and generating a state switching determination vector based on the crossover probability matrix; Performing time deviation fitting on the transition window, dynamically overlapping the fitting result with the abnormal candidate segment, and obtaining a state uncertainty index; The state switching judgment vector is fused with the state uncertainty index to generate opening state judgment data, the behavior pattern sequence and the state evolution trajectory are hierarchically combined and time series pattern matched to obtain a dynamic structure sequence, and the dynamic structure sequence is used to generate opening time series feature data.

7. The door magnetic risk assessment method according to claim 1, characterized in that: The step of fusing the opening state judgment data, the opening time series characteristic data and the current environment data to generate a communication signal comprises: The state sequence deconstruction and frequency feature extraction are performed on the opening state judgment data to obtain the state fluctuation frequency and state switching category to construct a state distribution matrix, the segmented fitting and time window resampling of the opening time series feature data are performed to extract the angle evolution sequence, the state distribution matrix and the angle evolution sequence are superimposed in a time-varying manner to generate a state time series coupling vector; Performing principal component extraction on the current environmental data to obtain an interference parameter group, performing nested fusion on the interference parameter group and the state time series coupling vector to obtain an environmental adjustment matrix, and performing fuzzy interval mapping on the environmental adjustment matrix to obtain a feedback smoothing coefficient; The state distribution matrix is ​​weightedly adjusted by using the feedback smoothing coefficient to obtain a communication factor set, and an asynchronous coding stream is constructed based on the communication factor set to generate a communication signal.

8. The door magnetic risk assessment method according to claim 1, characterized in that: The step of preprocessing the communication signal and transmitting the preprocessed communication signal to a preset cloud-based multi-dimensional decision engine to generate a corresponding risk level includes: Performing noise filtering and signal enhancement on the communication signal to obtain a preprocessed communication signal, and using a channel selection algorithm to identify an optimal channel for current communication; Based on the optimal channel, the preprocessed communication signal is input into a preset cloud-based multi-dimensional decision engine for risk level analysis to obtain a risk level.

9. A door magnetic risk assessment system, characterized in that: include: An acquisition module is used to acquire the magnetic field strength data of the door magnet, the timing characteristics of the door opening and closing, and the current environmental data, perform a threshold comparison on the magnetic field strength data, obtain the magnetic field attenuation, and use the magnetic field attenuation to calculate the door opening angle; An analysis module is used to determine the opening state of the door body based on the door body opening angle, obtain an analysis result, and jointly analyze the analysis result and the door body opening and closing timing characteristics to obtain opening state judgment data and opening timing characteristic data; A fusion module, used for fusing the opening state judgment data, the opening time series characteristic data and the current environment data to generate a communication signal; The generation module is used to pre-process the communication signal and transmit the pre-processed communication signal to a preset cloud-based multi-dimensional decision engine to generate a corresponding risk level.

10. A door magnetic risk assessment device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the intelligent fire door status monitoring method according to any one of claims 1 to 8 is implemented.

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