A mobile monitoring device and system for limited space

Abstract

The present invention provides a mobile monitoring device and system for limited space, which obtains the first gas data at the movable column collected by the first gas monitor, and the second gas data collected by the second gas monitor as the worker moves, wherein the first gas data and the second gas data include target gas concentration and environmental data; then, the pre-trained gas monitoring model is called to process the first gas data and the second gas data to predict and generate the gas data of the worker at the next position; finally, when the gas data at the next position is monitored as an abnormal value, an alarm signal is sent to the second gas monitor through the repeater. The possible operational risks of workers moving when working in a limited space are eliminated.

Description

Technical Field

[0001] The present invention relates to the field of gas monitoring, and in particular to a mobile monitoring device and system for a limited space. Background Art

[0002] In the fields of urban drainage, sewage treatment, chemical industry, etc., confined space operations such as pipelines, tanks, underground spaces and other operating places have poor ventilation, small spaces, and easy accumulation of toxic gases. When workers enter the confined space to perform operations, there may be operational risks if they move rashly.

[0003] In view of this, this application is filed. Summary of the invention

[0004] The invention discloses a mobile monitoring device and system for a limited space, aiming to eliminate possible operational risks when workers move when working in the limited space.

[0005] A first embodiment of the present invention provides a mobile monitoring device for a limited space, comprising: a movable column, a controller configured on the movable column, a network module electrically connected to a radio frequency end of the controller, a repeater electrically connected to the network module, a first gas monitor electrically connected to an input end of the controller, and a plurality of second gas monitors for being worn on a human body wirelessly connected to the repeater;

[0006] Wherein, the controller is configured to execute a computer program stored therein to implement the following steps;

[0007] Acquire first gas data at the movable column collected by the first gas monitor, and second gas data moving with the worker collected by the second gas monitor, wherein the first gas data and the second gas data include target gas concentration and environmental data;

[0008] Calling a pre-trained gas monitoring model to process the first gas data and the second gas data to predict and generate gas data of the worker at the next position;

[0009] When the gas data at the next position is detected to be an abnormal value, an alarm signal is sent to the second gas monitor through the repeater.

[0010] Preferably, the calling of the pre-trained gas monitoring model to process the first gas data and the second gas data to predict and generate gas data of the worker at the next position is specifically:

[0011] The gas data of the first gas monitor and the second gas monitor are obtained in real time, and the location information of the second gas monitor is obtained, and a characteristic vector of the combination of gas concentration data and location information is constructed in combination with a preset time window; the expression of the characteristic vector is:

[0012]

[0013] in, is a sliding time window, For the first gas monitor at the current time The gas concentration and environmental data collected at all times, For the time window The gas concentration and environmental data collected from the first gas monitor, For the second gas monitor at the current time The gas concentration and environmental data collected at all times, For the time window The gas concentration and environmental data collected from the second gas monitor, Current time for staff Location information at the moment, For the time window Location data within;

[0014] The pre-trained gas monitoring model is called to process the feature vector to recursively calculate the hidden state and memory state of each time step. The specific steps include:

[0015] The input gate calculates the impact of the current input on the memory state and generates the input gate state ,in, is the weight matrix input to the input gate, is the weight matrix from hidden state to input gate, is the bias term of the input gate, is the hidden state of the previous time step, is the Sigmoid function;

[0016] The forget gate is used to control the retention degree of the previous memory state and generate the forget gate state. , , , They are the input weight, hidden state weight and bias term of the forget gate respectively;

[0017] Generate candidate memory states based on the current input , , , They are the input weight, hidden state weight and bias term of the candidate state respectively;

[0018] Update memory status , is the element-wise product, is the memory state of the previous time step;

[0019] The output gate controls the output of the hidden state and generates the output gate state , , , are the input weight, hidden state weight and bias term of the output gate respectively;

[0020] Based on the updated memory state and output gate state, calculate the current hidden state ;

[0021] After recursively calculating the hidden state at all time steps in the time window, the hidden state of the last time step is input into the fully connected layer to generate the predicted value of the gas concentration at the next position.

[0022] Preferably, it also includes: a lighting device movably arranged on the column.

[0023] Preferably, it further comprises: an image acquisition device arranged on the movable column, wherein the image acquisition device is electrically connected to the input end of the controller.

[0024] Preferably, it also includes:

[0025] The video information collected by the image acquisition device is obtained, the behavioral characteristics of the staff are extracted based on the video information, and when it is determined that the staff has abnormal behavior according to the behavioral characteristics, an alarm is sent to the cloud platform through the network module.

[0026] Preferably, a plurality of vital sign monitors for being worn on the human body are wirelessly connected to the repeater.

[0027] Preferably, it also includes: a UPS power supply, and the UPS power supply is electrically connected to the power supply end of the movable column.

[0028] Preferably, the network module is arranged at the top of the movable column, and the repeater is arranged at the bottom of the movable column.

[0029] A second embodiment of the present invention provides a mobile monitoring system for a confined space, comprising a cloud platform and a mobile monitoring device for a confined space as described in any one of the above items, wherein the controller can establish a communication connection with the cloud platform through the network module.

[0030] Based on a mobile monitoring device and system for limited space provided by the present invention, the first gas data at the movable column collected by the first gas monitor and the second gas data moving with the staff collected by the second gas monitor are obtained, wherein the first gas data and the second gas data include target gas concentration and environmental data; then, the pre-trained gas monitoring model is called to process the first gas data and the second gas data to predict and generate the gas data of the staff at the next position; finally, when the gas data at the next position is monitored as an abnormal value, an alarm signal is sent to the second gas monitor through the repeater. The possible operational risks of staff moving when working in a limited space are eliminated. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a schematic diagram of a module of a mobile monitoring device for a limited space provided by a first embodiment of the present invention;

[0032] Figure 2 It is a flow chart of a mobile monitoring method for a limited space provided by the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0035] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0037] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0038] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0039] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0040] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0041] The invention discloses a mobile monitoring device and system for a limited space, aiming to eliminate possible operational risks when workers move when working in the limited space.

[0042] See also Figure 1 , a first embodiment of the present invention provides a mobile monitoring device for a limited space, comprising: a movable column, a controller configured on the movable column, a network module electrically connected to a radio frequency end of the controller, a repeater electrically connected to the network module, a first gas monitor electrically connected to an input end of the controller, and a plurality of second gas monitors wirelessly connected to the repeater for being worn on a human body;

[0043] It should be noted that the usage scenario of the invention is in underground pipelines or underground tanks, where the ventilation conditions are poor and a mobile monitoring device needs to be placed in before personnel enter. The first gas monitor can collect the gas conditions in a limited space and transmit it to the controller. The controller can be but is not limited to an STM32 series of microcontrollers. The controller can transmit the gas conditions to a terminal or a cloud platform through the network module. The first gas monitor will issue an alarm when it determines that some gas concentrations exceed the standard (such as carbon monoxide, hydrogen sulfide gas, or flammable gas, etc.) or the oxygen concentration is lower than a preset value. Of course, the terminal or cloud platform will also issue an alarm to remind staff not to enter or to take protective measures before entering. The second gas monitor is used to be worn on the body of the staff, and can monitor the gas conditions at the current location in real time, and can issue an alarm when it is determined that the gas concentration at the current location is abnormal.

[0044] Further, in this embodiment, the network module may be configured at the top of the movable column, and the repeater may be configured at the bottom of the movable column;

[0045] It should be noted that, taking the confined space operation of sewage pipes as an example, it refers to work activities related to drainage pipes carried out in a closed or partially closed space, such as entering the tank to install grilles and other work. After the staff enters the shaft, the network signal will decay rapidly. In this embodiment, the network module can be but is not limited to a 5G module or other network modules that can be connected to the Internet. The network module configured at the top can maintain networking, and the relay device configured at the bottom can be wirelessly connected to the second gas monitor configured on the staff, so as to avoid the inability to transmit the collected signal due to the lack of signal inside, resulting in the inability to transmit the gas data at the staff's location back.

[0046] Furthermore, in this embodiment, a lighting device that can be movably arranged on the column is also included, which is intended to provide a stable and adjustable light source support for a limited space working environment and ensure the visibility of the work under various light conditions. The lighting device is modularly designed as a whole, so that it can be flexibly installed at different heights and angles of the movable column to provide a bright working environment for the staff.

[0047] Furthermore, in this embodiment, a plurality of vital sign monitors for being worn on a human body and wirelessly connected to the repeater are also included;

[0048] It should be noted that the vital sign monitor can be a bracelet worn on the hand, which can collect vital sign data such as heart rate, blood oxygen saturation, body temperature, etc. in real time, so as to help identify possible physiological abnormalities, such as excessive fatigue, hypoxia, etc., especially under harsh conditions, and can provide the most direct safety protection. For example, when the heart rate or blood oxygen content exceeds the safety threshold, the monitor will immediately send an alarm signal to the repeater, and then notify the safety officer through the network module.

[0049] Furthermore, in this embodiment, an image acquisition device is also included which is arranged on the movable column, wherein the image acquisition device is electrically connected to the input end of the controller.

[0050] It should be noted that the image acquisition device configured on the movable column is used to capture images and video data in a limited space in real time, and transmit the acquired image data directly to the controller for processing. The terminal or cloud platform can access these images or video data in real time through the network module. The image acquisition device can be a high-definition camera that can provide clear pictures under complex and low-light conditions. By adopting wide dynamic range (WDR) and low-light enhancement technology, the camera can still capture detailed images even in a limited space with extremely insufficient light, thereby ensuring the real-time visibility of the monitoring system to the scene.

[0051] Furthermore, in this embodiment, a UPS power supply may be included, and the UPS power supply is electrically connected to the power supply terminal of the movable column, which can continuously supply power to the equipment in the event of a sudden power outage or voltage fluctuation, thereby ensuring the continuous operation of key monitoring and data acquisition devices.

[0052] See also Figure 2 In a possible implementation of the present invention, the controller is configured to execute a computer program stored therein to implement the following steps;

[0053] S101, acquiring first gas data at the movable column collected by the first gas monitor, and second gas data moving with the worker collected by the second gas monitor, wherein the first gas data and the second gas data include target gas concentration and environmental data;

[0054] It should be noted that the controller is responsible for acquiring the data of the gas monitor in real time to ensure safe monitoring of the working environment. Specifically, the first gas monitor is installed on a movable column and is used to collect the first gas data at the current column position. Through a direct electrical connection with the controller, the first gas monitor can continuously monitor the air quality around the column and transmit the collected target gas concentration and environmental data to the controller in real time. Among them, the first gas monitor can collect gas data directly below the working shaft by pumping. The target gas concentration includes monitoring of toxic gases (such as carbon monoxide, hydrogen sulfide, etc.) and combustible gases, while the environmental data covers temperature, humidity, ventilation conditions, etc. It should be understood that environmental data factors will affect the diffusion rate and concentration distribution of the gas.

[0055] At the same time, the second gas monitors are designed as portable devices that can be worn by workers to collect second gas data as they move. These monitors transmit data via wireless connections to repeaters, ensuring that data can be transmitted to the controller in a timely and reliable manner regardless of where the workers are located. The second gas data not only includes the concentration of the target gas, but also records changes in the surrounding environment in real time, such as temperature and humidity fluctuations or air flow conditions. These data are combined with the first gas data to provide support for the establishment of a comprehensive gas concentration distribution map, so that the controller can accurately grasp the dynamic changes in gas concentration and environmental parameters in each area.

[0056] S102, calling a pre-trained gas monitoring model to process the first gas data and the second gas data to predict and generate gas data of the worker at the next position;

[0057] It should be noted that confined space generally refers to operations in pipelines underground. It can be based on the movable column as a reference, with the next position being a preset distance away from the movable column (for example, 0.4m / 0.5m / 0.6m from the radius of the movable column). The distance setting can be based on the actual situation and is not specifically limited here.

[0058] Specifically, in this embodiment, the gas data of the first gas monitor and the second gas monitor are obtained in real time, and the location information of the second gas monitor is obtained. To ensure that the model can capture the dynamic characteristics of the ambient gas concentration, a feature vector of the combination of gas concentration data and location information is constructed in combination with a preset time window; the expression of the feature vector is:

[0059]

[0060] in, is a sliding time window, For the first gas monitor at the current time The gas concentration and environmental data collected at all times, For the time window The gas concentration and environmental data collected from the first gas monitor, For the second gas monitor at the current time The gas concentration and environmental data collected at all times, For the time window The gas concentration and environmental data collected from the second gas monitor, Current time for staff Location information at the moment, For the time window These data are sampled slidingly within the time window to form a complete description of the current environmental gas characteristics, enabling the gas monitoring model to efficiently use real-time data for prediction.

[0061] The pre-trained gas monitoring model is called to process the feature vector to recursively calculate the hidden state and memory state of each time step. After the feature vector is input into the pre-trained gas monitoring model, the model first recursively calculates the data impact of each time step through the long short-term memory network (LSTM) structure. The calculation process at the input gate stage of the LSTM is as follows:

[0062] The input gate calculates the impact of the current input on the memory state and generates the input gate state ,in, is the weight matrix input to the input gate, is the weight matrix from hidden state to input gate, is the bias term of the input gate, is the hidden state of the previous time step, is the Sigmoid function;

[0063] The forget gate state is generated by controlling the retention degree of the previous memory state through the forget gate (ensuring that the model can dynamically adjust the retention or abandonment of historical information and ensure that the model effectively balances the long-term and short-term characteristics of gas concentration and environmental data). , , , They are the input weight, hidden state weight and bias term of the forget gate respectively;

[0064] Generate candidate memory states based on the current input , , , They are the input weight, hidden state weight and bias term of the candidate state respectively;

[0065] Update memory status , is the element-wise product, is the memory state of the previous time step; this recursive process enables the model to accumulate dynamic information of gas data within the time window, ensuring that the continuity of gas changes can be reflected in the final prediction results.

[0066] The output gate controls the output of the hidden state and generates the output gate state , , , are the input weight, hidden state weight and bias term of the output gate respectively;

[0067] Based on the updated memory state and output gate state, calculate the current hidden state ;

[0068] After recursively calculating the hidden states for all time steps in the time window, the last hidden state will be used as a representation summarizing the gas data characteristics in the current time window. The hidden state of the last time step is input into the fully connected layer to generate the predicted gas concentration value at the next position.

[0069] It should be noted that the gas monitoring model can dynamically combine the current gas concentration, environmental data and location information to predict the gas concentration of the staff at the future location in real time. The recursive structure of the model ensures that the impact of each time step on subsequent data persists, thereby enhancing the accuracy of gas concentration prediction.

[0070] S103, when it is detected that the gas data at the next position is an abnormal value, an alarm signal is sent to the second gas monitor through the repeater.

[0071] It should be noted that when the controller uses the gas monitoring model to predict that the gas data at the next position of the staff is an abnormal value, the alarm mechanism will be activated immediately to ensure that the operator can take safety measures in time. At this time, the controller will first compare the predicted gas concentration with the preset safety threshold. If the prediction result shows that the gas concentration at the next position has reached or exceeded this threshold, it is determined that there is a potential gas hazard in the area, such as excessive concentration of toxic gas or insufficient oxygen concentration. After confirming the abnormal situation, the controller quickly transmits the alarm signal to the repeater through a wireless connection, and uses the signal enhancement function of the repeater to cover the information to the entire operation area. Even in a limited space where the signal is relatively limited, the alarm signal can be transmitted to the operator stably and without delay. After receiving the alarm signal, the repeater will immediately forward the information to the second gas monitor worn by the staff so that the operator can receive the warning in real time.

[0072] In a possible implementation of the present invention, the training process of the gas monitoring model includes:

[0073] Arrange multiple gas monitoring points in a confined space environment, collect gas concentration data (such as oxygen, CO, H2S, etc.) at each location, and record the changes in data over time, environmental data (such as ventilation status), and worker positions. Based on the collected gas data, record whether the gas concentration at the corresponding location is safe (marked as normal or abnormal) and use it as the target variable for model training.

[0074] Defining Datasets ,in: To represent the input features, it is a collection of gas monitoring data, environmental data, and location information. To represent the target gas concentration value, that is, the gas concentration at the next position of the worker.

[0075] Similarly, a sliding time window is set, and the gas monitoring data within the time window is used as the input feature to construct a feature vector, which is then trained using the long short-term memory network (LSTM) model. The model parameters are set to , it will be continuously adjusted through optimization algorithms during the training process.

[0076] The predicted output value of the model is expressed as

[0077] in, It represents the model's prediction result of the gas concentration at the next moment. is the model function; the loss function uses the mean square error (MSE) to measure the gap between the predicted value and the true value, and defines the loss function for:

[0078]

[0079] in, Indicates the actual gas concentration at the next position, represents the number of samples, Represents the square of the prediction error.

[0080] Use an optimization algorithm (such as stochastic gradient descent) to optimize the parameters Perform iterative updates to make the loss function Minimize:

[0081]

[0082] in, Represents the learning rate, which is used to control the parameter update step size. Represents the gradient of the loss function to the model parameters. After multiple iterations, the model parameters obtained by training Used to construct the final prediction model.

[0083] In a possible implementation of the present invention, the following may also be included:

[0084] The video information collected by the image acquisition device is obtained, the behavioral characteristics of the staff are extracted based on the video information, and when it is determined that the staff has abnormal behavior according to the behavioral characteristics, an alarm is sent to the cloud platform through the network module.

[0085] It should be noted that the controller obtains the video information captured by the image acquisition device in real time to capture and analyze the behavioral characteristics of the staff. After the video information is initially processed, each frame is converted into feature data containing the character's action and position. Through computer vision technology, the controller can automatically identify the posture, movement trajectory and working status of the operator. The built-in behavior analysis module of the controller will compare the current behavior characteristics with the normal working behavior based on the preset action pattern library, so as to extract the characteristics of any abnormal behavior, such as falling, standing still for a long time, suddenly stopping the operation, or not wearing a helmet, smoking, etc. Further, in this embodiment, a deep learning algorithm (such as a convolutional neural network or LSTM) can be used to analyze the video stream in real time to identify abnormal behavior patterns. The extraction process of behavioral features uses skeleton detection and action classification in the video to generate sequence data about the action. Through dynamic comparison, the controller can detect potential dangerous behaviors in the shortest time. For example, if it is identified that the operator is in a stationary state and exceeds the safety allowable time, the behavior will be immediately judged as a possible accident. Once the system confirms the abnormal behavior, the controller will send an alarm message to the cloud platform through the network module. This alarm information includes the description of the abnormal behavior, time and location information, and possible security risk level. By connecting to the cloud platform, managers can receive the alarm in real time and make quick response decisions.

[0086] In a possible implementation of the present invention, the lighting device can also be configured to automatically follow. Specifically, the lighting device realizes real-time perception and positioning of the position of the staff by linking with the image data collected by the camera and the data of the second gas monitor. The camera will continuously monitor the position of the personnel in the working environment and automatically identify the coordinates of the staff through the computer vision algorithm. At the same time, the second gas monitor will also send the position information of the staff in real time to supplement the visual data and provide more comprehensive positioning accuracy. Through the combination of these two types of data, the system can grasp the position dynamics of the staff in real time.

[0087] The controller adjusts the direction and angle of the lighting device according to this position, so that the light is always concentrated in the work area near the worker. The lighting device can be equipped with a rotatable multi-axis motor system, which allows it to rotate smoothly and flexibly in the horizontal and vertical directions, so as to quickly respond to the displacement of the worker. The controller calculates the optimal illumination angle based on the position data and automatically adjusts the rotation angle and light intensity of the lighting device to ensure that the brightness of the work area is always within the appropriate range.

[0088] A second embodiment of the present invention provides a mobile monitoring system for a confined space, comprising a cloud platform and a mobile monitoring device for a confined space as described in any one of the above items, wherein the controller can establish a communication connection with the cloud platform through the network module.

[0089] Based on a mobile monitoring device and system for limited space provided by the present invention, the first gas data at the movable column collected by the first gas monitor and the second gas data moving with the staff collected by the second gas monitor are obtained, wherein the first gas data and the second gas data include target gas concentration and environmental data; then, the pre-trained gas monitoring model is called to process the first gas data and the second gas data to predict and generate the gas data of the staff at the next position; finally, when the gas data at the next position is monitored as an abnormal value, an alarm signal is sent to the second gas monitor through the repeater. The possible operational risks of staff moving when working in a limited space are eliminated.

Claims

1. A mobile monitoring device for a limited space, characterized in that: include: A movable column, a controller configured on the movable column, a network module electrically connected to a radio frequency end of the controller, a repeater electrically connected to the network module, a first gas monitor electrically connected to an input end of the controller, and a plurality of second gas monitors for being worn on a human body wirelessly connected to the repeater; Wherein, the controller is configured to execute a computer program stored therein to implement the following steps; Acquire first gas data at the movable column collected by the first gas monitor, and second gas data moving with the worker collected by the second gas monitor, wherein the first gas data and the second gas data include target gas concentration and environmental data; The pre-trained gas monitoring model is called to process the first gas data and the second gas data to predict and generate the gas data of the worker at the next position, specifically: the gas data of the first gas monitor and the second gas monitor are obtained in real time, and the location information of the second gas monitor is obtained, and a feature vector of the combination of gas concentration data and location information is constructed in combination with a preset time window; the expression of the feature vector is: in, is a sliding time window, For the first gas monitor at the current time The gas concentration and environmental data collected at all times, For the time window The gas concentration and environmental data collected from the first gas monitor, For the second gas monitor at the current time The gas concentration and environmental data collected at all times, For the time window The gas concentration and environmental data collected from the second gas monitor, Current time for staff Location information at the moment, For the time window Location data within; The pre-trained gas monitoring model is called to process the feature vector to recursively calculate the hidden state and memory state of each time step. The specific steps include: The input gate calculates the impact of the current input on the memory state and generates the input gate state ,in, is the weight matrix input to the input gate, is the weight matrix from hidden state to input gate, is the bias term of the input gate, is the hidden state of the previous time step, is the Sigmoid function; The forget gate is used to control the retention degree of the previous memory state and generate the forget gate state. , , , They are the input weight, hidden state weight and bias term of the forget gate respectively; Generate candidate memory states based on the current input , , , They are the input weight, hidden state weight and bias term of the candidate state respectively; Update memory status , is the element-wise product, is the memory state of the previous time step; The output gate controls the output of the hidden state and generates the output gate state , , , are the input weight, hidden state weight and bias term of the output gate respectively; Based on the updated memory state and output gate state, calculate the current hidden state ; After recursively calculating the hidden state at all time steps in the time window, the hidden state of the last time step is input into the fully connected layer to generate the predicted value of the gas concentration at the next position; When it is predicted that the gas data at the next position is an abnormal value, an alarm signal is sent to the second gas monitor through the repeater.

2. A mobile monitoring device for a limited space according to claim 1, characterized in that: Also includes: A lighting device can be movably arranged on the column.

3. A mobile monitoring device for a limited space according to claim 1, characterized in that: Also includes: An image acquisition device is arranged on the movable column, wherein the image acquisition device is electrically connected to an input terminal of the controller.

4. A mobile monitoring device for a limited space according to claim 3, characterized in that: Also includes: The video information collected by the image acquisition device is obtained, the behavioral characteristics of the staff are extracted based on the video information, and when it is determined that the staff has abnormal behavior according to the behavioral characteristics, an alarm is sent to the cloud platform through the network module.

5. The mobile monitoring device for limited space according to claim 2, characterized in that: Also includes: A plurality of vital sign monitors for being worn on a human body are wirelessly connected to the repeater.

6. A mobile monitoring device for a limited space according to claim 2, characterized in that: Also includes: A UPS power supply is electrically connected to a power supply terminal of the movable column.

7. A mobile monitoring device for a limited space according to claim 2, characterized in that: The network module is arranged on the top of the movable column, and the repeater is arranged on the bottom of the movable column.

8. A mobile monitoring system for a limited space, characterized in that: It comprises a cloud platform and a mobile monitoring device for a limited space as described in any one of claims 1 to 7, wherein the controller can establish a communication connection with the cloud platform through the network module.

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