Method and device for monitoring external force of push-out door driven by motor

Abstract

The invention discloses a method and device for monitoring external force of a push door driven by a motor, and relates to the technical field of push door monitoring. The method comprises the steps that when a target unit door is static and the motor is in a power-on state, whether the motor is in a braking state or not is determined; when the motor is in a braking state, continuously monitoring the motor by using a sensor array to obtain a monitoring sequence; recognizing a push-out door external force risk mode to obtain the push-out door external force risk mode; data acquisition is carried out on an absolute encoder arranged on a motor shaft, motor position data are obtained, and door pushing early warning is carried out. The technical problems that in the prior art, it is difficult for a cell door to accurately monitor the external force of the push-out door, timely and effective early warning cannot be achieved, and potential safety hazards exist are solved, and the technical effects that accurate monitoring and timely early warning of the external force of the push-out door of the cell door are achieved, and the safety of the cell door is guaranteed are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of door prying monitoring, and particularly to a method and device for monitoring external forces on a door driven by a motor. Background Art

[0002] In the field of modern intelligent buildings, unit doors, as important facilities for ensuring the safety of residents and places, are of crucial importance in terms of security. Traditional unit doors have many deficiencies in preventing illegal opening, such as the lack of effective means for monitoring external forces. When the unit door is in a stationary state, if criminals attempt to pry open the door and invade, the existing door structure and simple lock system often fail to detect the application of abnormal external forces. At the same time, even with some monitoring methods, most can only perform simple detection of the open / closed state and cannot accurately judge the magnitude of the external force and the behavior pattern, resulting in the inability to issue early warnings in a timely manner and leaving residents and places at potential security risks.

[0003] The prior art has the technical problem that it is difficult to accurately monitor the external force of prying a unit door, and it is impossible to give an early warning in a timely and effective manner, resulting in potential safety hazards. Summary of the Invention

[0004] This application provides a method and device for monitoring external forces on a door driven by a motor, aiming to solve the technical problems in the prior art that it is difficult to accurately monitor the external force of prying a unit door and it is impossible to give an early warning in a timely and effective manner, resulting in potential safety hazards.

[0005] In view of the above problems, this application provides a method and device for monitoring external forces on a door driven by a motor.

[0006] In the first aspect of this application, a method for monitoring external forces on a door driven by a motor is provided. The method includes: When the target unit door is stationary and the motor is powered on, use a sensor array to monitor the current signal, torque, and vibration of the motor to determine whether the motor is in a braking state; when the motor is in a braking state, use the sensor array to continuously monitor the motor to obtain a current signal monitoring sequence, a torque monitoring sequence, and a vibration amplitude monitoring sequence; perform risk pattern recognition of the external force of prying a door based on the current signal monitoring sequence, the torque monitoring sequence, and the vibration amplitude monitoring sequence to obtain a risk pattern of the external force of prying a door, where the risk pattern of the external force of prying a door includes a high-risk pattern, a first-level low-risk pattern, and a second-level low-risk pattern; when the risk pattern of the external force of prying a door is a high-risk pattern or a first-level low-risk pattern, generate a first warning instruction to give a prying warning; when the risk pattern of the external force of prying a door is a second-level low-risk pattern, collect data from the absolute encoder arranged on the motor shaft at a preset data acquisition frequency based on the risk pattern of the external force of prying a door to obtain motor position data, and give a prying warning based on the motor position data.

[0007] In a second aspect of the present application, a motor-driven external force monitoring device for a door is provided, and the device includes: A motor state determination module, configured to, when the target unit door is stationary and the motor is in an energized state, use a sensor array to monitor the current signal, torque, and vibration of the motor to determine whether the motor is in a braking state; a monitoring sequence acquisition module, configured to, when the motor is in a braking state, use the sensor array to continuously monitor the motor to obtain a current signal monitoring sequence, a torque monitoring sequence, and a vibration amplitude monitoring sequence; a risk mode acquisition module, configured to perform external force risk mode recognition for door prying based on the current signal monitoring sequence, the torque monitoring sequence, and the vibration amplitude monitoring sequence to obtain an external force risk mode for door prying, where the external force risk mode for door prying includes a high-risk mode, a first-level low-risk mode, and a second-level low-risk mode; a first warning instruction generation module, configured to, when the external force risk mode for door prying is a high-risk mode or a first-level low-risk mode, generate a first warning instruction for door prying warning; a door prying warning module, configured to, when the external force risk mode for door prying is a second-level low-risk mode, collect data from an absolute encoder disposed on the motor shaft according to a preset data acquisition frequency based on the external force risk mode for door prying to obtain motor position data, and perform door prying warning based on the motor position data.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: When the target unit door is stationary and the motor is in an energized state, determine whether the motor is in a braking state; when the motor is in a braking state, use a sensor array to continuously monitor the motor to obtain a current signal monitoring sequence, a torque monitoring sequence, and a vibration amplitude monitoring sequence; perform external force risk mode recognition for door prying based on the current signal monitoring sequence, the torque monitoring sequence, and the vibration amplitude monitoring sequence to obtain an external force risk mode for door prying; when the external force risk mode for door prying is a high-risk mode or a first-level low-risk mode, generate a first warning instruction for door prying warning; when the external force risk mode for door prying is a second-level low-risk mode, collect data from an absolute encoder disposed on the motor shaft to obtain motor position data, and perform door prying warning based on the motor position data. The technical effect of accurately monitoring and timely warning of the external force for prying the unit door and ensuring the safety of the unit door is achieved. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0010] Figure 1Schematic flowchart of a method for monitoring external force of a motor-driven door Figure 2 Schematic structural diagram of a device for monitoring external force of a motor-driven door provided by an embodiment of the present application

[0011] Explanation of reference numerals: Motor state determination module 10, monitoring sequence acquisition module 20, risk mode acquisition module 30, first warning instruction generation module 40, door pulling warning module 50. Detailed implementation manners

[0012] The present application provides a method and a device for monitoring external force of a motor-driven door, which are used to solve the technical problems in the prior art that it is difficult to accurately monitor the external force of a unit door and it is impossible to give a timely and effective warning, resulting in potential safety hazards.

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0014] Embodiment 1, as Figure 1 shown, the present application provides a method for monitoring external force of a motor-driven door, and the method includes: Step S100: When the target unit door is stationary and the motor is in the powered-on state, use a sensor array to monitor the current signal, torque, and vibration of the motor to determine whether the motor is in the braking state.

[0015] Specifically, in the operation system of the intelligent unit door, the motor is the core power component, and its working state plays a key role in the safety of the unit door. When the target unit door is stationary and the motor is in the powered-on state, in order to determine whether the motor is in the braking state, start the sensor array for comprehensive monitoring. The sensor array consists of a current sensor, a torque sensor, and a vibration sensor, which respectively perform real-time detection on the current signal, torque, and vibration of the motor to obtain the first current signal, the first torque, and the first vibration amplitude. Compare these real-time obtained data with the pre-set braking current signal, braking torque, and braking vibration amplitude in detail. If the real-time data is consistent with the pre-set braking state data, it indicates that the motor is in the normal braking state; if not, it means that the motor state is abnormal. In this way, it is possible to accurately judge the braking state of the motor when the unit door is stationary and powered on, providing a basic guarantee for the subsequent possible external force monitoring of the door. Ensure that during the stationary period of the unit door, once an external force acts on the door body and is transmitted to the motor, it can be detected in time and the subsequent processing process can be started, thereby ensuring the safety of the unit door.

[0016] Step S200: When the motor is in the braking state, continuously monitor the motor using a sensor array to obtain a current signal monitoring sequence, a torque monitoring sequence, and a vibration amplitude monitoring sequence.

[0017] Specifically, when it is determined that the motor is in the braking state, it means that although the target unit door is stationary and the motor is powered on, there may be a potential risk of being subjected to external forces. At this time, use the sensor array to continuously monitor the motor. The sensor array consists of high-precision current sensors, torque sensors, and vibration sensors, which respectively collect the current signal, torque, and vibration amplitude of the motor in real time. During the continuous monitoring process, at a certain time interval, continuously record the values of the current signal, torque, and vibration amplitude of the motor. As time goes by, these sequentially recorded current signal values form a current signal monitoring sequence, the torque values form a torque monitoring sequence, and the vibration amplitude values form a vibration amplitude monitoring sequence. These monitoring sequences can completely and dynamically reflect the working conditions of the motor in the braking state, providing a rich and accurate data basis for identifying the risk mode of external forces for prying the door in the subsequent steps, and can more accurately judge whether there is an external force for prying the door and the risk level of the external force.

[0018] Step S300: Identify the risk mode of external forces for prying the door based on the current signal monitoring sequence, the torque monitoring sequence, and the vibration amplitude monitoring sequence to obtain the risk mode of external forces for prying the door, where the risk mode of external forces for prying the door includes a high-risk mode, a first-level low-risk mode, and a second-level low-risk mode.

[0019] Specifically, after completing the continuous monitoring of the motor and obtaining the current signal monitoring sequence, the torque monitoring sequence, and the vibration amplitude monitoring sequence, enter the link of identifying the risk mode of external forces for prying the door. First, traverse these three monitoring sequences and check one by one whether there is data greater than the pre-set warning current signal threshold, warning torque threshold, and warning vibration amplitude threshold. Once it is found that there is data greater than the corresponding threshold, quickly determine that the risk mode of external forces for prying the door at this time is the high-risk mode, indicating that there may be external forces acting on the unit door, posing a certain threat to the safety of the door.

[0020] If in the initial traversal, none of the three monitoring sequences have data greater than the corresponding threshold, further deeply analyze these sequences. By performing feature recognition on the sequences according to the first convolution scale and the second convolution scale, respectively obtain the current signal monitoring features, torque monitoring features, and vibration amplitude monitoring features at different scales, and then iteratively fuse these features at different scales to determine the current signal iterative monitoring features, torque iterative monitoring features, and vibration amplitude iterative monitoring features.

[0021] Pre-build a prototype library of risk patterns. Each risk pattern prototype has a corresponding risk pattern identifier, including a first-level low-risk pattern identifier and a second-level low-risk pattern identifier. Match the obtained iterative monitoring features with the risk pattern prototype library, and finally determine the current risk pattern of the external force of prying the door according to the identifiers of the risk pattern prototypes in the matching results. This multi-level and multi-dimensional analysis method can more accurately and comprehensively identify the risk pattern of the external force of prying the door, providing a reliable basis for subsequent targeted early warning measures.

[0022] Step S400: When the risk pattern of the external force of prying the door is a high-risk pattern or a first-level low-risk pattern, generate a first warning instruction for prying the door warning.

[0023] Specifically, when it is determined through risk pattern recognition that the risk pattern of the external force of prying the door is a high-risk pattern or a first-level low-risk pattern, it indicates that the unit door may be under abnormal external force and there is a certain safety risk. At this time, the early warning mechanism is quickly activated, and the first warning instruction generation module starts to work and generates a first warning instruction. This instruction will quickly transmit the prying the door warning information to the cloud server through the controller and the 4G communication module, and the cloud server will then push the warning information to the terminal devices of relevant personnel, such as the mobile phones of property management personnel and the monitoring computers in the security room. After receiving the warning, relevant personnel can view the monitoring screen of the unit door in time and take corresponding measures, such as going to the scene to check the situation, notifying the owner to pay attention to safety, etc., so as to effectively intervene at the initial stage of the risk and ensure the safety of the unit door and the area inside the door.

[0024] Step S500: When the risk pattern of the external force of prying the door is a second-level low-risk pattern, based on the risk pattern of the external force of prying the door, collect data from the absolute encoder arranged on the motor shaft at a preset data collection frequency to obtain motor position data, and perform prying the door warning based on the motor position data.

[0025] Specifically, when it is determined that the risk mode of the forced door opening is the secondary low-risk mode, it is necessary to more precisely grasp the position information of the motor to judge the actual state of the door body. Since the absolute encoder is installed on the motor shaft, it can directly provide the absolute position information of the motor, and each position has a unique code. According to the preset data acquisition frequency, data acquisition work is carried out on the absolute encoder. This acquisition method at a fixed frequency can continuously and stably obtain the motor position data, so as to ensure real-time monitoring of the change of the motor position. By analyzing the acquired motor position data and comparing it with the pre-set normal position range. Once it is found that the motor position data exceeds the normal range, it indicates that the door body may have abnormal movement under the action of external force. At this time, a corresponding forced door opening warning message is quickly generated. This warning message will be transmitted to relevant personnel in a timely manner through the communication module, such as property management personnel or security personnel, etc., so that they can quickly take measures, such as going to the scene to check, preventing abnormal behaviors, etc., so as to effectively respond to possible safety risks and ensure the safety of the unit door and the surrounding area. The use of the absolute encoder can provide accurate and reliable motor position data in the secondary low-risk mode, making the forced door opening warning work more accurate and timely, and improving the response ability and processing efficiency to external abnormal situations.

[0026] In a possible implementation manner, step S100 further includes: Step S110: The sensor array includes a current sensor, a torque sensor, and a vibration sensor.

[0027] Specifically, the sensor array is composed of a current sensor, a torque sensor, and a vibration sensor. The current sensor is responsible for monitoring the current signal of the motor. The current signal can intuitively reflect the power consumption of the motor during operation and the working state of the internal circuit. The torque sensor focuses on measuring the torque of the motor. Torque is a key parameter to measure the output power of the motor. By monitoring the torque, the load condition of the motor under different working conditions and whether the power output is normal can be understood. The vibration sensor is mainly used to detect the vibration amplitude of the motor. The motor will generate a certain vibration during operation. When the motor fails or is interfered by external force, the vibration amplitude will change, and the vibration sensor can capture these changes in time. When the target unit door is stationary and the motor is in the powered-on state, these three sensors work together to monitor the motor in real time from different dimensions. The current sensor obtains the current signal of the motor, the torque sensor measures the torque of the motor, and the vibration sensor detects the vibration amplitude of the motor. These monitoring data complement and verify each other, providing a comprehensive and accurate basis for subsequent judgment of whether the motor is in the braking state, so as to ensure that the system can timely and accurately identify the working state of the motor and ensure the safe and stable operation of the unit door.

[0028] In a possible implementation manner, step S100 further includes: Step S120: The sensor array monitors the current signal, torque, and vibration of the motor to obtain a first current signal, a first torque, and a first vibration amplitude.

[0029] Step S130: Compare the first current signal, the first torque, and the first vibration amplitude with a braking current signal, a braking torque, and a braking vibration amplitude respectively to obtain a comparison result.

[0030] Step S140: If the comparison result is consistent, the motor is in a braking state.

[0031] Specifically, when the target unit door is in a stationary state and the motor remains powered on, start the precise monitoring process of the motor state. First, the current sensor, torque sensor, and vibration sensor in the sensor array monitor the motor respectively. The current sensor obtains the real-time current signal of the motor, denoted as the first current signal, which reflects the current power consumption of the motor; the torque sensor measures the real-time torque of the motor to obtain the first torque, which reflects the magnitude of the output power of the motor; the vibration sensor detects the real-time vibration amplitude of the motor, that is, the first vibration amplitude, which can reflect the running stability of the motor.

[0032] Then, carefully compare the obtained first current signal, first torque, and first vibration amplitude with the pre-set braking current signal, braking torque, and braking vibration amplitude respectively. These pre-set parameters are determined based on the indicators of the motor in the normal braking state and are important bases for judging whether the motor is in a braking state.

[0033] Finally, judge the state of the motor according to the comparison result. If the first current signal is completely consistent with the braking current signal, the first torque is completely consistent with the braking torque, and the first vibration amplitude is completely consistent with the braking vibration amplitude, this indicates that the running state of the motor at this time matches the indicators in the normal braking state, and thus it can be determined that the motor is in a braking state. Through this rigorous monitoring and comparison process, the working state of the motor when the unit door is stationary and powered on can be accurately judged, ensuring the safe and stable operation of the door control system.

[0034] In a possible implementation manner, step S300 further includes: Step S310: Traverse the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence to determine whether there is data greater than the warning current signal threshold, warning torque threshold, and warning vibration amplitude threshold. If so, obtain a high-risk mode.

[0035] Specifically, when the motor is in the braking state and the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence have been obtained, these sequences are traversed. Each data in the current signal monitoring sequence is checked one by one and compared with the preset warning current signal threshold; at the same time, the data in the torque monitoring sequence is also compared with the warning torque threshold, and the data in the vibration amplitude monitoring sequence is compared with the warning vibration amplitude threshold. If, during the traversal process, it is found that there are data in the current signal monitoring sequence, torque monitoring sequence, or vibration amplitude monitoring sequence that are greater than their respective corresponding warning thresholds, this means that there are abnormal fluctuations in the operating state of the motor. Because these thresholds are set according to the parameter range during the normal operation of the motor, once exceeded, it indicates that there may be abnormal external forces acting on the unit door, resulting in changes in the operating parameters of the motor. When this situation occurs, it is immediately determined that the current risk mode of the external force for opening the door is a high-risk mode.

[0036] In a possible implementation manner, step S300 further includes: Step S320: If there are no data in the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence that are greater than the warning current signal threshold, warning torque threshold, and warning vibration amplitude threshold, respectively, perform iterative monitoring feature analysis on the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence to determine the current signal iterative monitoring feature, torque iterative monitoring feature, and vibration amplitude iterative monitoring feature.

[0037] Step S330: Pre-build a risk mode prototype library, where each risk mode prototype has a risk mode identifier, and the risk mode identifier includes a primary low-risk mode identifier and a secondary low-risk mode identifier.

[0038] Step S340: Match the current signal iterative monitoring feature, torque iterative monitoring feature, and vibration amplitude iterative monitoring feature with the risk mode prototype library to obtain a matching risk mode prototype.

[0039] Step S350: Determine the risk mode of the external force for opening the door according to the risk mode identifier of the matching risk mode prototype.

[0040] Specifically, when the motor is identified for the risk pattern of the external force of the door opening, the current signal monitoring sequence, torque monitoring sequence and vibration amplitude monitoring sequence are preliminarily checked. When it is found that there is no data greater than the respective warning threshold, it means that the current risk status cannot be determined by relying on simple threshold judgment alone. At this time, it is in low-risk mode, and it is necessary to start a more sophisticated iterative monitoring feature analysis process to process the current signal monitoring sequence, torque monitoring sequence and vibration amplitude monitoring sequence respectively, and further subdivide whether it is in the first-level low-risk mode or the second-level low-risk mode. According to the first convolution scale and the second convolution scale, different levels of features are identified in these sequences, and two groups of current signal monitoring features, two groups of torque monitoring features and two groups of vibration amplitude monitoring features are obtained respectively. Afterwards, the two groups of current signal monitoring features are iteratively fused through inner product mapping, normalization and other operations, so as to determine the current signal iterative monitoring features. Similarly, the two groups of torque monitoring features and the two groups of vibration amplitude monitoring features are iteratively fused to obtain torque iterative monitoring features and vibration amplitude iterative monitoring features. These iterative monitoring features can more comprehensively and deeply reflect the operating characteristics of the motor under braking, and provide more precise data support for the subsequent accurate judgment of the risk mode of external force when opening the door.

[0041] Collect a large amount of current signal, torque and vibration amplitude data of the motor under different door-picking behaviors, covering various situations such as short-term door-picking and continuous door-picking. Preprocess these data, remove outliers, and normalize the data to a unified interval through standardization methods to enhance data comparability. Take the mean value of the current signal, the rate of change of the torque, the peak value of the vibration amplitude, etc. as the characteristic attributes of the decision tree. Starting from the root node, according to indicators such as information gain or Gini coefficient, select the features that can best distinguish the data of different door-picking behaviors for splitting. For example, if the mean value of the current signal is most effective in distinguishing different door-picking behaviors, then divide the data set based on this feature. During the splitting process, recursively construct subtrees until the preset stop conditions are met, such as the purity of the node data reaches an extremely high standard, the number of node samples is too small, etc. After the construction is completed, each leaf node represents a risk pattern. Risk pattern identification is assigned to leaf nodes according to the potential harm degree of door-picking behavior. Leaf nodes corresponding to continuous door-picking behavior that may cause the unit door to be opened quickly and is more dangerous are marked as first-level low-risk pattern identification; leaf nodes corresponding to short-term door-picking behavior that poses less threat to the safety of the unit door are marked as second-level low-risk pattern identification. Finally, all leaf nodes with risk pattern identification and their corresponding feature combinations are organized into a risk pattern prototype library for subsequent risk pattern matching and identification.

[0042] After the construction of the risk mode prototype library is completed, the current iterative monitoring features of the current signal, the torque iterative monitoring features, and the vibration amplitude iterative monitoring features obtained in real time are matched with the prototype library. First, these three iterative monitoring features are combined into a multi-dimensional feature vector, which contains the comprehensive information of the current, torque, and vibration amplitude under the current operating state of the motor. For each risk mode prototype in the risk mode prototype library, the corresponding current, torque, and vibration amplitude features are also combined into a multi-dimensional feature vector. Then, the Euclidean distance algorithm is used to calculate the distance between the real-time feature vector and each prototype feature vector. The smaller the distance, the more similar the two feature vectors are. After calculating the Euclidean distances between the real-time feature vector and all prototype feature vectors, the prototype with the smallest distance is found. This prototype with the smallest distance is identified as the risk mode prototype that best matches the current operating state of the motor. In this way, the prototype that matches the real-time monitoring data can be quickly and accurately found from the risk mode prototype library, providing a basis for subsequent determination of the risk mode of the external force of prying the door.

[0043] The risk mode of the external force of prying the door will be finally determined based on the risk mode identifier carried by the matching risk mode prototype. Since each risk mode prototype has been assigned a clear risk mode identifier in the pre-constructed risk mode prototype library, that is, a first-level low-risk mode identifier or a second-level low-risk mode identifier. If the identifier of the matching risk mode prototype is a first-level low-risk mode identifier, it indicates that the unit door is facing a prying behavior with a relatively high risk, such as continuous and high-intensity external force; if the identifier of the matching risk mode prototype is a second-level low-risk mode identifier, it indicates that the external force of prying the door on the current unit door is in a low-risk state, which may correspond to short-term and slight external force interference. Through this determination method based on the preset identifier, the current risk mode of the external force of prying the door can be quickly and accurately output, providing a clear decision-making basis for subsequent triggering of different levels of early warning and disposal measures.

[0044] Preferably, the risk level of the first-level low-risk mode is higher than that of the second-level low-risk mode. The risk mode identifier corresponding to each risk mode prototype is distinguished according to whether the unit door corresponding to the risk mode prototype can be broken into by outsiders. Although the unit door is not broken under the first-level low-risk mode, it is already sufficient to be broken into. Under the second-level low-risk mode, the unit door is not broken and cannot be broken into by people. Those skilled in the art will identify it as a first-level low-risk mode or a second-level low-risk mode according to the specific situation of the risk mode prototype.

[0045] In a possible implementation manner, step S320 further includes: Step S321: Traverse the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence to perform feature recognition according to the first convolution scale and the second convolution scale, and obtain the first current signal monitoring feature and the second current signal monitoring feature, the first torque monitoring feature and the second torque monitoring feature, and the first vibration amplitude monitoring feature and the second vibration amplitude monitoring feature, where the first convolution scale is greater than the second convolution scale.

[0046] Step S322: Iteratively fuse the first current signal monitoring feature and the second current signal monitoring feature to determine the iterative current signal monitoring feature.

[0047] Step S323: Respectively perform iterative fusion on the first torque monitoring feature and the second torque monitoring feature, and the first vibration amplitude monitoring feature and the second vibration amplitude monitoring feature to determine the iterative torque monitoring feature and the iterative vibration amplitude monitoring feature.

[0048] Specifically, a comprehensive scan is performed on the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence in turn. For each sequence, feature recognition work is carried out using two convolution windows of different sizes. Among them, the convolution window corresponding to the first convolution scale is larger, which can analyze the sequence from a more macroscopic perspective and capture the change trend and overall characteristics of the sequence in a larger range. For example, in the current signal monitoring sequence, it can identify the rising or falling trend of the current over a long period of time. The convolution window corresponding to the second convolution scale is smaller, which can analyze the sequence more carefully and discover the subtle changes and local characteristics in the sequence. For example, in the torque monitoring sequence, it can detect the small fluctuations of the torque in a short period of time. Through these two convolution operations of different scales, the first current signal monitoring feature and the second current signal monitoring feature are respectively obtained from the current signal monitoring sequence; the first torque monitoring feature and the second torque monitoring feature are obtained from the torque monitoring sequence; the first vibration amplitude monitoring feature and the second vibration amplitude monitoring feature are extracted from the vibration amplitude monitoring sequence, providing rich and multi-dimensional feature information for subsequent in-depth analysis.

[0049] When monitoring the external force of the door on the motor, after determining that the motor is in the braking state and obtaining the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence, if no data greater than the warning threshold appears in these sequences, further in-depth analysis is required. First, feature recognition is performed by traversing the current signal monitoring sequence according to the first convolution scale and the second convolution scale to obtain the first current signal monitoring feature and the second current signal monitoring feature. Next, these two features are iteratively fused to determine the current signal iterative monitoring feature: first, an inner product mapping is performed on the first current signal monitoring feature and the second current signal monitoring feature to determine the first mapping similarity set; then, normalization processing is performed on this set to construct an iterative fusion matrix; finally, the iterative fusion matrix is iteratively fused with the first current signal monitoring feature, and the resulting result is the current signal iterative monitoring feature. This feature can more accurately reflect the characteristics of the current signal, so as to match with the risk mode prototype library later, determine the external force risk mode of the door, and then provide a key basis for judging whether the unit door faces the risk of door prying and triggering corresponding warnings.

[0050] For the torque monitoring feature, similar to the processing of the current signal monitoring feature, first perform an inner product mapping on the first torque monitoring feature and the second torque monitoring feature to obtain the corresponding mapping similarity set, then perform normalization processing on this set to construct a torque iterative fusion matrix, and iteratively fuse this matrix with the first torque monitoring feature to finally determine the torque iterative monitoring feature. This feature synthesizes the torque change characteristics at different scales and more comprehensively reflects the torque situation. For the vibration amplitude monitoring feature, the same processing method is adopted. Perform an inner product mapping on the first vibration amplitude monitoring feature and the second vibration amplitude monitoring feature, perform normalization processing after obtaining the mapping similarity set, construct a vibration amplitude iterative fusion matrix, and then iteratively fuse it with the first vibration amplitude monitoring feature to determine the vibration amplitude iterative monitoring feature. This feature can more accurately present the characteristics of the vibration amplitude. Through such iterative fusion operations, the obtained torque iterative monitoring feature and vibration amplitude iterative monitoring feature, together with the current signal iterative monitoring feature, provide important data support for subsequent matching with the risk mode prototype library and then determining the external force risk mode of the door, which helps to more accurately judge the door prying risk situation faced by the unit door.

[0051] In a possible implementation manner, step S322 further includes: Step S3221: Perform an inner product mapping on the first current signal monitoring feature and the second current signal monitoring feature to determine the first mapping similarity set.

[0052] Step S3222: Normalize the first mapping similarity set to construct an iterative fusion matrix.

[0053] Step S3223: Iteratively fuse the iterative fusion matrix with the first current signal monitoring feature to obtain an iterative monitoring feature of the current signal.

[0054] Specifically, after obtaining the first current signal monitoring feature and the second current signal monitoring feature, an inner product mapping operation is performed on them. The first current signal monitoring feature and the second current signal monitoring feature can be regarded as two vectors in a multi-dimensional vector space. Inner product mapping is to multiply the elements on the corresponding dimensions of these two vectors and then sum them. For each group of corresponding dimensions of these two feature vectors, such inner product calculations are performed, and then a series of numerical values are obtained. These numerical values reflect the similarity degree of the first current signal monitoring feature and the second current signal monitoring feature in different dimensions, and they together constitute the first mapping similarity set.

[0055] After obtaining the first mapping similarity set, it is normalized by means of the Softmax formula to construct an iterative fusion matrix. First, create an initially empty matrix, which will be used as a container to store the normalization result. The Softmax formula is: Softmax( )= , where e is the natural constant. The numerator of this formula is to perform exponential operations on each similarity value in the set. The denominator is the sum of the exponential operations on all similarity values in the set. is the -th element in the first mapping similarity set, is the total number of elements in the set. Applying this formula to each element in the set, the corresponding normalized value is obtained. After calculating the normalized values, these values are added to the empty matrix in order. When all elements in the first mapping similarity set are normalized and added to the matrix, the iterative fusion matrix is constructed. The elements in this matrix are in the interval, and the sum of all elements is 1. It can effectively reflect the similarity distribution between the first current signal monitoring feature and the second current signal monitoring feature, providing strong support for subsequent feature fusion.

[0056] Iteratively fuse this iterative fusion matrix with the first current signal monitoring feature to obtain the iterative monitoring feature of the current signal. First, set an upper limit on the number of iterations and a convergence threshold as the termination conditions for the iteration. In each iteration, perform matrix multiplication on the iterative fusion matrix and the first current signal monitoring feature to obtain an intermediate feature vector. Optimize and adjust this intermediate feature vector by adding a correction term scaled by a certain ratio of the difference between the previous iteration result and the current intermediate feature vector, so that the feature vector can better fuse feature information at different scales. Then, check whether the iteration termination conditions are met. If the preset upper limit on the number of iterations is reached, or the difference between the feature vectors obtained in two adjacent iterations is less than the convergence threshold, stop the iteration. At this time, the obtained feature vector is the iterative monitoring feature of the current signal. This feature synthesizes the characteristics of the current signal at different convolution scales and can more accurately reflect the actual condition of the current signal, providing a key basis for subsequent judgment of the risk mode of the forced door-opening external force.

[0057] In a possible implementation manner, step S500 further includes: Step S510: When the deviation between the motor position data and the preset position is greater than or equal to the preset deviation amount, generate a second warning instruction for forced door-opening warning.

[0058] Specifically, during the monitoring of the forced door-opening external force driven by the motor, when it is determined that the risk mode of the forced door-opening external force is the secondary low-risk mode, data is collected from the absolute encoder arranged on the motor shaft at a preset data acquisition frequency to obtain the motor position data. After obtaining the motor position data, compare it with the preset position set in advance and calculate the deviation between the two. This preset position is determined according to the ideal position where the motor should be when the unit door is normally closed or stationary. The preset deviation amount is an allowable deviation range set according to the safety standard and actual operation condition of the unit door. When the deviation between the calculated motor position data and the preset position is greater than or equal to the preset deviation amount, it indicates that the actual position of the motor has changed significantly, and it is very likely affected by abnormal forced door-opening external force. At this time, quickly react and generate a second warning instruction. This instruction will transmit information to the cloud server through the controller and the 4G communication module, and then generate a forced door-opening alarm reminder to notify relevant personnel to handle possible safety hazards in a timely manner and ensure the safety of the unit door.

[0059] Embodiment 2, based on the same inventive concept as the method for monitoring the forced door-opening external force driven by a motor in the foregoing embodiment, as Figure 2 shown, the present application provides a device for monitoring the forced door-opening external force driven by a motor. The device in the embodiment of the present application and the method embodiment are based on the same inventive concept. Among them, the device includes: The motor status determination module 10 is configured to, when the target unit door is stationary and the motor is in the powered-on state, use the sensor array to monitor the current signal, torque, and vibration of the motor to determine whether the motor is in the braking state.

[0060] The monitoring sequence acquisition module 20 is configured to, when the motor is in the braking state, use the sensor array to continuously monitor the motor to obtain a current signal monitoring sequence, a torque monitoring sequence, and a vibration amplitude monitoring sequence.

[0061] The risk mode acquisition module 30 is configured to identify the risk mode of the external force for prying the door based on the current signal monitoring sequence, the torque monitoring sequence, and the vibration amplitude monitoring sequence, and obtain the risk mode of the external force for prying the door, where the risk mode of the external force for prying the door includes a high-risk mode, a first-level low-risk mode, and a second-level low-risk mode.

[0062] The first warning instruction generation module 40 is configured to, when the risk mode of the external force for prying the door is the high-risk mode or the first-level low-risk mode, generate a first warning instruction for prying the door warning.

[0063] The prying the door warning module 50 is configured to, when the risk mode of the external force for prying the door is the second-level low-risk mode, collect data from the absolute encoder disposed on the motor shaft at a preset data collection frequency based on the risk mode of the external force for prying the door to obtain motor position data, and perform prying the door warning based on the motor position data.

[0064] Further, the device is further configured to implement the following functions: The sensor array includes a current sensor, a torque sensor, and a vibration sensor.

[0065] Further, the device is further configured to implement the following functions: The sensor array monitors the current signal, torque, and vibration of the motor to obtain a first current signal, a first torque, and a first vibration amplitude; compares the first current signal, the first torque, and the first vibration amplitude with a braking current signal, a braking torque, and a braking vibration amplitude respectively to obtain a comparison result; if the comparison result is consistent, the motor is in the braking state.

[0066] Further, the device is further configured to implement the following functions: Traverse the current signal monitoring sequence, the torque monitoring sequence, and the vibration amplitude monitoring sequence to determine whether there is data greater than the warning current signal threshold, the warning torque threshold, and the warning vibration amplitude threshold. If so, obtain the high-risk mode.

[0067] Further, the device is further configured to implement the following functions: If there is no data in the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence that is greater than the warning current signal threshold, warning torque threshold, and warning vibration amplitude threshold, respectively, perform iterative monitoring feature analysis on the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence to determine the iterative monitoring features of the current signal, torque iterative monitoring features, and vibration amplitude iterative monitoring features; pre-construct a risk pattern prototype library, where each risk pattern prototype has a risk pattern identifier, and the risk pattern identifier includes a primary low-risk pattern identifier and a secondary low-risk pattern identifier; match the iterative monitoring features of the current signal, torque iterative monitoring features, and vibration amplitude iterative monitoring features with the risk pattern prototype library to obtain a matching risk pattern prototype; determine the forced door-opening risk pattern according to the risk pattern identifier of the matching risk pattern prototype.

[0068] Further, the device is also used to implement the following functions: Traverse the current signal monitoring sequence, torque monitoring sequence, and vibration amplitude monitoring sequence to perform feature recognition according to the first convolution scale and the second convolution scale, and obtain the first current signal monitoring feature and the second current signal monitoring feature, the first torque monitoring feature and the second torque monitoring feature, and the first vibration amplitude monitoring feature and the second vibration amplitude monitoring feature, where the first convolution scale is greater than the second convolution scale; perform iterative fusion on the first current signal monitoring feature and the second current signal monitoring feature to determine the iterative monitoring feature of the current signal; perform iterative fusion on the first torque monitoring feature and the second torque monitoring feature, and the first vibration amplitude monitoring feature and the second vibration amplitude monitoring feature, respectively, to determine the torque iterative monitoring feature and the vibration amplitude iterative monitoring feature.

[0069] Further, the device is also used to implement the following functions: Perform inner product mapping on the first current signal monitoring feature and the second current signal monitoring feature to determine the first mapping similarity set; normalize the first mapping similarity set to construct an iterative fusion matrix; perform iterative fusion on the iterative fusion matrix and the first current signal monitoring feature to obtain the iterative monitoring feature of the current signal.

[0070] Further, the device is also used to implement the following functions: When the deviation between the motor position data and the preset position is greater than or equal to the preset deviation amount, generate a second warning instruction for forced door-opening warning.

[0071] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the above specific embodiments of this specification have been described. Further, the processes depicted in the accompanying drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0073] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A motor-driven door-opening force monitoring method, characterized in that: The method comprises: When the target unit door is stationary and the motor is powered on, the sensor array is used to monitor the motor's current signal, torque and vibration to determine whether the motor is in a braking state; When the motor is in a braking state, the motor is continuously monitored using a sensor array to obtain a current signal monitoring sequence, a torque monitoring sequence and a vibration amplitude monitoring sequence; Based on the current signal monitoring sequence, the torque monitoring sequence and the vibration amplitude monitoring sequence, the risk pattern of the door-opening external force is identified to obtain the risk pattern of the door-opening external force, wherein the risk pattern of the door-opening external force includes a high-risk pattern, a first-level low-risk pattern and a second-level low-risk pattern; When the door-opening external force risk mode is a high-risk mode or a level 1 low-risk mode, a first warning instruction is generated to perform a door-opening warning; When the door-opening external force risk mode is a secondary low-risk mode, data is collected from the absolute encoder arranged on the motor shaft according to a preset data collection frequency based on the door-opening external force risk mode to obtain motor position data, and a door-opening warning is performed based on the motor position data.

2. A motor-driven door opening force monitoring method as claimed in claim 1, characterized in that: The sensor array includes a current sensor, a torque sensor, and a vibration sensor.

3. The motor-driven door opening force monitoring method according to claim 1, characterized in that: The sensor array is used to monitor the motor's current signal, torque, and vibration to determine whether the motor is in a braking state, including: The sensor array monitors the current signal, torque and vibration of the motor to obtain a first current signal, a first torque and a first vibration amplitude; Comparing the first current signal, the first torque and the first vibration amplitude with the braking current signal, the braking torque and the braking vibration amplitude respectively to obtain a comparison result; If the comparison result is consistent, the motor is in braking state.

4. The motor-driven door-opening force monitoring method according to claim 1, characterized in that: Based on the current signal monitoring sequence, torque monitoring sequence and vibration amplitude monitoring sequence, the risk pattern of the door-opening external force is identified to obtain the risk pattern of the door-opening external force, including: The current signal monitoring sequence, torque monitoring sequence and vibration amplitude monitoring sequence are traversed to determine whether there is data greater than the warning current signal threshold, the warning torque threshold and the warning vibration amplitude threshold. If so, a high-risk mode is obtained.

5. A motor-driven door-opening force monitoring method as claimed in claim 4, characterized in that: Also includes: If the current signal monitoring sequence, the torque monitoring sequence and the vibration amplitude monitoring sequence do not contain data greater than the warning current signal threshold, the warning torque threshold and the warning vibration amplitude threshold, then iterative monitoring feature analysis is performed on the current signal monitoring sequence, the torque monitoring sequence and the vibration amplitude monitoring sequence to determine the current signal iterative monitoring feature, the torque iterative monitoring feature and the vibration amplitude iterative monitoring feature; Pre-constructing a risk model prototype library, wherein each risk model prototype has a risk model identifier, and the risk model identifier includes a first-level low-risk model identifier and a second-level low-risk model identifier; Matching the current signal iterative monitoring feature, the torque iterative monitoring feature and the vibration amplitude iterative monitoring feature with the risk pattern prototype library to obtain a matching risk pattern prototype; The door-opening external force risk mode is determined according to the risk mode identifier of the matching risk mode prototype.

6. A motor-driven door-opening force monitoring method as claimed in claim 5, characterized in that: Then, iterative monitoring feature analysis is performed on the current signal monitoring sequence, the torque monitoring sequence and the vibration amplitude monitoring sequence to determine the current signal iterative monitoring feature, the torque iterative monitoring feature and the vibration amplitude iterative monitoring feature, including: Traversing the current signal monitoring sequence, the torque monitoring sequence, and the vibration amplitude monitoring sequence, and performing feature recognition according to the first convolution scale and the second convolution scale, to obtain the first current signal monitoring feature and the second current signal monitoring feature, the first torque monitoring feature and the second torque monitoring feature, and the first vibration amplitude monitoring feature and the second vibration amplitude monitoring feature, wherein the first convolution scale is larger than the second convolution scale; Iteratively fuse the first current signal monitoring feature and the second current signal monitoring feature to determine the current signal iterative monitoring feature; The first torque monitoring feature and the second torque monitoring feature, and the first vibration amplitude monitoring feature and the second vibration amplitude monitoring feature are iteratively fused respectively to determine a torque iterative monitoring feature and a vibration amplitude iterative monitoring feature.

7. A motor-driven door-opening force monitoring method as claimed in claim 6, characterized in that: Iteratively fusing the first current signal monitoring feature and the second current signal monitoring feature to determine the current signal iterative monitoring feature, including: Performing inner product mapping on the first current signal monitoring feature and the second current signal monitoring feature to determine a first mapping similarity set; Normalizing the first mapping similarity set to construct an iterative fusion matrix; The iterative fusion matrix is ​​iteratively fused with the first current signal monitoring feature to obtain a current signal iterative monitoring feature.

8. The motor-driven door opening force monitoring method according to claim 1, characterized in that: When the deviation between the motor position data and the preset position is greater than or equal to the preset deviation amount, a second warning instruction is generated to issue a door-opening warning.

9. A motor-driven door-opening force monitoring device, characterized in that: The device is used to implement a motor-driven door-opening external force monitoring method according to any one of claims 1 to 8, and the device comprises: The motor state determination module is used to monitor the current signal, torque and vibration of the motor using the sensor array when the target unit door is stationary and the motor is in the power-on state to determine whether the motor is in the braking state; A monitoring sequence acquisition module, used for continuously monitoring the motor using a sensor array when the motor is in a braking state, to obtain a current signal monitoring sequence, a torque monitoring sequence and a vibration amplitude monitoring sequence; A risk mode acquisition module is used to identify the risk mode of the door-opening external force based on the current signal monitoring sequence, the torque monitoring sequence and the vibration amplitude monitoring sequence, and obtain the risk mode of the door-opening external force, wherein the risk mode of the door-opening external force includes a high-risk mode, a first-level low-risk mode and a second-level low-risk mode; A first warning instruction generating module, configured to generate a first warning instruction for door-opening warning when the door-opening external force risk mode is a high-risk mode or a first-level low-risk mode; The door-picking warning module is used to collect data from the absolute encoder arranged on the motor shaft according to a preset data collection frequency based on the door-picking external force risk mode when the door-picking external force risk mode is the second-level low-risk mode, obtain motor position data, and issue a door-picking warning based on the motor position data.

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