Electromechanical engineering safety visual management system

Abstract

The invention relates to the technical field of electromechanical engineering, and discloses an electromechanical engineering safety visual management system, which comprises a data processing module used for acquiring multi-source data of electromechanical equipment and environment in real time; the tensor generation module is used for generating a four-dimensional space-time tensor according to the quantum state output by the data processing module; the risk evaluation module is used for calculating safety risk indexes of the equipment based on the tensor information output by the tensor generation module; the visualization module is used for carrying out three-dimensional holographic visualization display on the risk indexes output by the risk assessment module; the feedback control module is used for performing feedback according to the risk indexes generated by the risk assessment module and the equipment state information provided by the visualization module; and the user interaction module is used for receiving a control instruction of a user. According to the technical scheme, the user interaction module and the visualization module are integrated, the effects of displaying the equipment state and user instruction interaction in real time are achieved, and the problem that information obtained by a user lags behind is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical engineering, and in particular to a electromechanical engineering safety visualization management system. Background Art

[0002] In the current field of electromechanical engineering, existing technologies generally rely on independent equipment monitoring systems and manual control. Such systems often result in untimely information feedback and low operational efficiency. For example, when monitoring equipment status, operators often need to rely on instrument panels on each device. This approach results in information delays and hinders quick decision-making.

[0003] Traditional control modules lack user interaction. Current technology doesn't support immediate operational adjustments when equipment experiences anomalies. This prevents the equipment from adapting quickly in complex or changing environments, leading to failures or downtime. Thus, even though the equipment itself may be operating normally, operational shortcomings can significantly hinder overall production efficiency.

[0004] Furthermore, existing technologies often rely on static control parameters and lack adaptability. As environmental and operating conditions change during equipment operation, fixed control strategies often struggle to cope with unexpected situations. This leads to reduced equipment energy efficiency and may even pose safety risks. While experienced operators can intuitively adjust parameters, this process is highly subjective, carries unpredictable risks, and lacks scientific basis.

[0005] Furthermore, traditional monitoring systems often lack real-time feedback mechanisms. Even if a user requests an adjustment, multiple steps, from information collection to command execution, must be completed, resulting in a long response time. Such delays can, in some cases, lead to irreversible equipment damage or production accidents, seriously impacting the company's economic profitability.

[0006] Therefore, from this analysis, it can be seen that existing technologies have obvious shortcomings in automation, real-time performance, and flexibility. These issues emphasize the need to introduce more intelligent solutions to improve the control capabilities of electromechanical equipment and the overall efficiency of the system. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, the present invention provides a mechanical and electrical engineering safety visualization management system, which solves the technical problems of low efficiency, untimely response and lack of flexibility in the traditional control system during equipment status monitoring and adjustment.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a mechanical and electrical engineering safety visualization management system, comprising: The data processing module is used to acquire multi-source data of electromechanical equipment and the environment in real time, convert the collected multi-source data into quantum states through quantum state encoding, and perform adaptive noise compensation on the quantum states based on the Kraus operator; a tensor generation module, configured to generate a four-dimensional space-time tensor based on the quantum state output by the data processing module and extract characteristic information of the four-dimensional space-time tensor, wherein the tensor generation module processes the four-dimensional space-time tensor using a variational quantum tensor decomposition algorithm; a risk assessment module, which calculates the security risk index of the device based on the tensor information output by the tensor generation module and generates corresponding risk alerts; The visualization module is used to display the risk indicators output by the risk assessment module in a three-dimensional holographic visualization; A feedback control module is used to dynamically control the electromechanical equipment based on the risk indicators generated by the risk assessment module and the equipment status information provided by the visualization module; The user interaction module is used to receive user control instructions, display device status information, and feed back user instructions to the feedback control module.

[0009] Preferably, the data processing module includes at least one temperature sensor, pressure sensor and vibration sensor to ensure comprehensive acquisition of multivariate data.

[0010] Preferably, the data processing module encodes the data into quantum states using phase encoding and amplitude encoding methods.

[0011] Preferably, the data processing module dynamically adjusts the noise coupling coefficient based on the ambient noise intensity.

[0012] Preferably, the tensor generation module uses the following mathematical model to generate the tensor structure: ; In the formula, represents a physical quantity, This is an inner product involving two functions or states. represents some time-dependent state or wave function, represents some basis function or mode in space, Specifies the different dimensions of space, Re represents the real part of the inner product result, Indicates time With a central time The deviation, is a time scale that controls the width of the decay.

[0013] Preferably, the tensor generation module realizes feature extraction through a variational quantum tensor decomposition algorithm, and its optimization goal is: ; In the formula, Indicates the parameters Optimize, represents the norm, represents the main part of the objective function, represents a large matrix or tensor, Is with each The relevant weight coefficient, are basis functions or vectors in different dimensions, Represents different modes of tensors, represents the regularization part, is the number of some parameter or dimension.

[0014] Preferably, the risk assessment module uses a variety of algorithms to assess the safety of the equipment based on the equipment's historical operating data and current status, and generates corresponding alarms.

[0015] Preferably, the visualization module can display the equipment status and risk assessment results in the form of three-dimensional holographic images, and supports interactive functions for users to obtain information in real time.

[0016] Preferably, the feedback control module dynamically adjusts the operating parameters of the electromechanical device according to the quantum information feedback and risk assessment results.

[0017] Preferably, the user interaction module includes a touch screen interface for displaying device status and receiving user instructions and providing real-time feedback.

[0018] The present invention provides a visual management system for electromechanical engineering safety. It has the following beneficial effects: 1. The present invention adopts the technical solution of integrating user interaction module and visualization module to achieve the effect of real-time display of device status and user command interaction. Compared with the single display method in the existing technology, this solution solves the problem of delayed user information acquisition, enables users to understand the device operation status in a timely manner, and improves the safety and efficiency of operation.

[0019] 2. The present invention introduces a dynamic control mechanism and combines the technical solutions of the feedback control module and the risk assessment module to achieve the effect of automatic adjustment of the equipment. Compared with the manual adjustment method in the existing technology, this solution reduces human errors, improves the intelligence level and response speed of the system, and ensures the safe operation of the equipment in complex environments.

[0020] 3. The present invention achieves accurate control command feedback through standardized mathematical models and algorithm processes, which improves the overall stability of the system. Compared with the failure risk caused by untimely command response in traditional control systems, this technical solution significantly reduces the probability of unexpected shutdown and improves the reliability of the system.

[0021] 4. The present invention adopts a technical solution that combines fuzzy control and PID control to achieve more flexible adjustment capabilities. Compared with the fixed control parameter method in the existing technology, this solution can dynamically optimize the control strategy according to the actual operating status, effectively deal with abnormal situations that may occur during the operation of the equipment, and enhance the adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. 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 making creative efforts are within the scope of protection of the present invention.

[0024] Please see the attached Figure 1 The embodiment of the present invention provides a mechanical and electrical engineering safety visualization management system, including: The data processing module is used to acquire multi-source data of electromechanical equipment and the environment in real time, convert the collected multi-source data into quantum states through quantum state encoding, and perform adaptive noise compensation on the quantum states based on the Kraus operator; Specifically, the data processing module includes multiple sensors, such as temperature sensors, pressure sensors, vibration sensors, etc. These sensors are connected to the data processing unit through a data acquisition system to form a complete data acquisition and processing chain.

[0025] First, during the data collection phase, the sensor monitors the operating status of the electromechanical equipment in real time at a high frequency (e.g., 100 times per second). The physical quantities of the collected data include temperature, pressure, vibration, etc. Specifically: The temperature sensor is used to detect the operating temperature of the equipment. The parameter is set to a range of (-40℃ to 150℃) with a resolution of 0.1℃.

[0026] The pressure sensor is used to detect the pressure inside the equipment or system. The working range is (10kPa to 200kPa) and the resolution is 0.5kPa.

[0027] Vibration sensors are used to monitor the vibration of equipment and can sense frequencies in the range of 10Hz to 1000Hz.

[0028] Then, the data processing module performs preliminary screening and preprocessing on the collected data. The main steps include removing outliers and signal smoothing. After preliminary processing, the data obtained is expressed by the formula: ; In the formula, is the processed data set, For the original collected data, Outlier Indicates outliers that are detected and removed through statistical methods.

[0029] Next, the data processing module encodes the processed data into quantum states. This process is described by the following formula: ; In the formula, Used to describe the state of the system. represents a linear combination, represents the number of possible states, Indicates that the quantum state in each basis state The weight on is the ground state representation, arrive Indicates a total of A ground state.

[0030] The absolute value square of each probability amplitude should satisfy the normalization condition: ; In the formula, the sum of the probabilities of all ground states is 1, which indicates the possibility that the system is in a certain state.

[0031] After completing the quantum state encoding, the data processing module uses the Kraus operator to perform noise adaptive compensation. According to the loss and distortion of the quantum state, it is described as follows: ; In the formula, represents the quantum density matrix after some operation, For all operators The sum of The effect of a quantum operation can be achieved through a set of operators describe, is the initial density matrix of the system, yes The Hermitian conjugate operator of .

[0032] ; In the formula, is the unit operator.

[0033] The specific form of the Kraus operator can be designed according to the properties of the ambient noise, as shown below: ; In the formula, is the probability of noise occurrence, is the corresponding operator.

[0034] After completing the noise adaptive compensation, the module outputs the final quantum state to the subsequent tensor generation module for feature extraction and risk assessment.

[0035] Through this process, the data processing module can efficiently convert multi-source input data into quantum states and effectively eliminate the influence of noise, ultimately producing optimized quantum features to provide accurate support for subsequent risk assessment and equipment status monitoring.

[0036] a tensor generation module, connected to the data processing module, for generating a four-dimensional space-time tensor based on the quantum state output by the data processing module and extracting characteristic information of the four-dimensional space-time tensor, wherein the tensor generation module processes the four-dimensional space-time tensor using a variational quantum tensor decomposition algorithm; Specifically, the main components of the tensor generation module include a receiving unit, a tensor generation unit, and a feature extraction unit. These components are connected via a data bus to complete real-time information transmission. The receiving unit is responsible for receiving the quantum state representation from the data processing module and transmitting the quantum state data to the tensor generation unit, which is responsible for generating a four-dimensional space-time tensor. The tensor generation process uses the following formula: ; In the formula, represents a physical quantity, This is an inner product involving two functions or states. represents some time-dependent state or wave function, represents some basis function or mode in space, Specifies the different dimensions of space, Re represents the real part of the inner product result, Indicates time With a central time The deviation, is a time scale that controls the width of the decay.

[0037] In the tensor generation unit, the interaction information at a specific point in time and space is first obtained by calculating the overlap of the quantum state with each spatial ground state. Subsequently, a Gaussian function is used for weighting to ensure that the characteristic information is concentrated in the desired time period. For example, in fault prediction tasks, this process can emphasize the period before and after a fault occurs.

[0038] The feature extraction unit uses the variational quantum tensor decomposition algorithm to process the generated tensor. The basic steps of the algorithm are as follows: First, the loss function is defined to minimize the reconstruction error, which is expressed as: ; In the formula, represents the loss function, is the generated four-dimensional tensor, Indicates the importance of the corresponding factor, Representing different dimensions The basis functions on Corresponding to each dimension of the four-dimensional tensor; Represents the Frobenius norm, which is used to calculate the overall size of a matrix or tensor.

[0039] Next, we set the optimization goal to minimize the loss function and introduce a regularization term to control the complexity of the model. The optimization process can be expressed as: ; In the formula, Indicates the parameters Optimize, represents the norm, represents the main part of the objective function, represents a large matrix or tensor, Is with each The relevant weight coefficient, are basis functions or vectors in different dimensions, Represents different modes of tensors, represents the regularization part, is the number of some parameter or dimension.

[0040] During the optimization process, the parameters are iteratively updated by setting initial values ​​and using a stochastic optimization algorithm (such as the Adam optimizer or stochastic gradient descent) to minimize the loss function: ; In the formula, Indicates in The updated value of the parameter in the iteration, Indicates in The value of the parameter in the iteration, is the learning rate, Represents the loss function Parameters gradient.

[0041] After multiple iterations, the feature extraction unit generates a low-dimensional tensor representation with significant features that efficiently expresses the equipment's operating status and potential fault information. This extracted feature information is then passed to the risk assessment module to support condition analysis, maintenance decision-making, and fault prediction for electromechanical equipment.

[0042] a risk assessment module, connected to the tensor generation module, calculating a security risk indicator of the device based on the tensor information output by the tensor generation module and generating a corresponding risk alert; Specifically, the risk assessment module consists of a receiving unit, a risk calculation unit, and an alarm generation unit. The receiving unit is directly connected to the tensor generation module and is responsible for receiving the generated four-dimensional tensor information, which is denoted as: Represents a four-dimensional tensor processed by the tensor generation module.

[0043] The received tensor data is passed from the receiving unit to the risk calculation unit, which is responsible for calculating the security risk index. The risk calculation formula is as follows: ; In the formula, Represents the final output value or score Express From 1 to The sum of It is with The weights associated with each feature, Indicates the Features from tensor The value extracted from .

[0044] The feature extraction process is based on the following function definition: ; In the formula, the feature extraction function Ability to extract security-related features from tensors, Indicates that for input The calculated features, Represents a specific element in a four-dimensional data structure, Represents a function accept takes as input and outputs the eigenvalues.

[0045] The risk calculation unit summarizes the various features to obtain the security risk index The value is used to evaluate the security status of the device. To determine whether to trigger an alarm, set a risk threshold: ; In the formula, Used to determine whether a condition is met, mean express The mean of represents the multiplication factor, The standard deviation of .

[0046] The alarm generation unit generates risk alarms based on the calculation results. The alarm generation rules are as follows: ; In the formula, Variables representing intelligence states, when If there is no abnormality in the system, :if Greater than or equal to threshold , the alarm status is "Alert! Risk is too high", which means that in this case, the system has detected potential risks or anomalies.

[0047] This logic ensures that when risk indicators exceed set thresholds, corresponding risk alerts are generated in a timely manner, ensuring that operators can respond quickly.

[0048] The implementation process of the risk assessment module is as follows: First, the receiving unit receives the four-dimensional tensor information from the tensor generation module. Then, the risk calculation unit extracts features based on the received tensor information and calculates the security risk index. Finally, the alarm generation unit outputs risk alarm information based on the comparison between the risk index and the threshold.

[0049] The visualization module is connected to the risk assessment module and is used to perform a three-dimensional holographic visualization of the risk indicators output by the risk assessment module; Specifically, the visualization module is mainly composed of a receiving unit, a data processing unit and a display unit. The receiving unit is connected to the risk assessment module and is used to receive the generated risk indicator data, which is recorded as: ; In the formula, represents the calculated security risk index, is the total number of features extracted, For the The importance weight associated with each feature, Extracted from the risk assessment module Features, Indicates the final result or score, : means from arrive The sum of is the total number of features.

[0050] The received risk indicator data will be transmitted to the data processing unit through the receiving unit. The main function of the data processing unit is to convert the risk indicator into a coordinate format suitable for 3D holographic display. To do this, the 3D coordinates are first calculated based on the risk indicator using the following formula: ; In the formula, represents the coordinate point in three-dimensional space, where 、 and are the coordinate values ​​on each axis, represents the distance from the origin to the point, Indicates from the positive The angle at which the axis starts to rotate counterclockwise, Indicates point Height on axis, calculate coordinate calculate coordinate.

[0051] The data processing unit transmits the calculated 3D coordinate data to the display unit. The display unit is responsible for generating a 3D holographic image. The specific process is as follows: First, a hologram model is established, and the visualization object is generated by analyzing the obtained three-dimensional coordinates. The hologram intensity distribution can be expressed by the following formula: ; In the formula, Indicates the hologram at coordinate point The intensity distribution of is a constant, Indicates the strength of the risk status. Represents complex phase information, is the phase information.

[0052] Next, the display unit uses a specific holographic projection device to project the generated three-dimensional holographic image onto a visualization device, allowing operators to present risk status information in a three-dimensional form. Holographic projection is generally implemented using optical equipment or virtual reality equipment.

[0053] Throughout the implementation process, the physical and logical connections are clear: the receiving unit obtains risk indicator data from the risk assessment module, the data processing unit converts the risk data into three-dimensional coordinates, and then the display unit generates and projects a three-dimensional holographic image to ensure that operators can obtain equipment operating status information in a timely manner.

[0054] A feedback control module is connected to the risk assessment module and the visualization module, and is used to dynamically control the electromechanical equipment based on the risk indicators generated by the risk assessment module and the equipment status information provided by the visualization module; Specifically, the feedback control module includes a receiving unit, a decision unit and an execution unit. The receiving unit is connected to the risk assessment module and the visualization module to receive the generated risk indicators and equipment status information respectively.

[0055] The receiving unit transmits the risk indicators and equipment status information to the decision-making unit. The decision-making unit forms a control decision based on the received data. The decision-making process is based on the following control algorithm, which can adopt fuzzy control, adaptive control, or PID control. The following uses a simple PID control algorithm as an example: ; In the formula, Indicates that the controller is at time The output signal, Indicates at time The error signal, , , are the proportional, integral and differential gain coefficients of the PID controller, Used to adjust the current error Proportional to the control quantity, Used to accumulate past errors and help eliminate steady-state errors. For the rate of change of forecast error, Indicates from 0 to Error The points, Representation error The rate of change over time.

[0056] The decision-making unit also adjusts the control strategy based on the device status information S. For example, the following response strategy can be defined: ; In the formula, Represents the adjusted device state, which is used to further influence control decisions and adjust functions Ability to adjust control parameters based on the current status of the equipment (such as overtemperature, abnormal vibration, etc.).

[0057] The control signal after the decision The control signal is transmitted to the execution unit, which then executes the command on the electromechanical device. The execution unit adjusts the operating state of the device according to the control signal, which may include: Adjust the operating frequency of the motor; Control valve opening; Adjust the pressure setting of the hydraulic system; Operate the cooling system on and off.

[0058] The feedback control module implements a dynamic control mechanism, using control signals to direct the execution unit to implement specific adjustments, making equipment operation safer and more efficient. To monitor equipment conditions in real time, the execution unit then feeds back the equipment operating parameters to the receiving unit after executing the control, forming a closed-loop control loop and effectively adjusting the strategy.

[0059] In summary, the feedback control module can realize dynamic adjustment of electromechanical equipment according to the risk indicators of the risk assessment module and the equipment status information provided by the visualization module.

[0060] The user interaction module is connected to the visualization module and the feedback control module, and is used to receive user control instructions, display device status information, and feed back user instructions to the feedback control module; Specifically, the user interaction module includes an input unit, a display unit, and a feedback unit. The input unit interacts directly with the user and is responsible for receiving control instructions input by the user. These instructions can affect the operating parameters of the device and are specifically represented by a control instruction vector: ; In the formula, Represents a collection, Representing a collection The specific elements in are the elements in the set, the number of which is , Indicates the total number of elements in the collection.

[0061] Users can enter settings through the interface, such as setting the target temperature or target speed The corresponding formula is: ; In the formula, Indicates the A collection, The target temperature set by the user, The target speed set by the user, The target pressure is set by the user.

[0062] The received user instructions are sent to the feedback control module through the input unit, and the device status information is transmitted to the display unit at the same time. Provided by the visualization module, expressed as a state parameter vector: ; In the formula, Represents a collection or sequence, Representing a collection The specific elements in are the elements in the set, the number of which is , Indicates the total number of elements in the collection, indicating How many elements are included, such as the current temperature , current pressure , current speed wait.

[0063] These state parameters can be further defined as: ; In the formula, is the current temperature of the device, is the current pressure of the device, is the current speed of the device, is the current vibration frequency of the device, It is a timestamp, indicating the specific time of status detection.

[0064] The display unit receives device status information from the visualization module And display it on the user interface through function mapping, update the content so that users can obtain the real-time working status of the device in time. This process is implemented through the following function: ; In the formula, For in time Device status information displayed at all times, To convert state information into a user interface function, its output includes the real-time data of the state parameters, for example: ; In the formula, Indicates at time A data set or state set, Representing a collection The specific elements in It is elements, indicating that at time A particular state or measure of is the total number of elements in the collection, indicating How many elements are contained.

[0065] The system can monitor the status of equipment in real time, ensuring that users can quickly identify the current operating status of the equipment and detect potential anomalies in a timely manner.

[0066] The function of the feedback unit is to convert the user's control instructions Feedback is sent to the feedback control module to implement the instruction execution. The feedback control module combines the user instruction with the risk index generated by the risk assessment module to make control decisions. The generation of the control signal involves the following formula: ; In the formula, Indicates that the controller is at time The output signal, Indicates at time The error signal, , , are the proportional, integral and differential gain coefficients of the PID controller, Used to adjust the current error Proportional to the control quantity, Used to accumulate past errors and help eliminate steady-state errors. For the rate of change of forecast error, Indicates from 0 to Error The points, Representation error The rate of change over time.

[0067] In the decision-making process, when the user inputs a command and feeds it back to the feedback control module, the system will decide whether to execute the command based on the current device status and output the resultant control. Controls applied to the device.

[0068] The physical connection relationship is as follows: the input unit interacts with the user, receives control instructions and passes them to the feedback control module; the display unit obtains device status information in real time and integrates it with user feedback information to keep the user interface updated; the feedback unit ensures that the user's operations are implemented accurately.

[0069] Through this implementation, the risk assessment module can effectively calculate security risk indicators from the information output by the tensor generation module and generate corresponding risk alerts based on set thresholds. This process clearly demonstrates the logical relationship between various technical elements and ensures the integrity and feasibility of the technical chain.

[0070] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The mechanical and electrical engineering safety visualization management system is characterized by: include: The data processing module is used to acquire multi-source data of electromechanical equipment and the environment in real time, convert the collected multi-source data into quantum states through quantum state encoding, and perform adaptive noise compensation on the quantum states based on the Kraus operator; a tensor generation module, configured to generate a four-dimensional space-time tensor based on the quantum state output by the data processing module and extract characteristic information of the four-dimensional space-time tensor, wherein the tensor generation module processes the four-dimensional space-time tensor using a variational quantum tensor decomposition algorithm; a risk assessment module, which calculates the security risk index of the device based on the tensor information output by the tensor generation module and generates corresponding risk alerts; The visualization module is used to display the risk indicators output by the risk assessment module in a three-dimensional holographic visualization; A feedback control module is used to dynamically control the electromechanical equipment based on the risk indicators generated by the risk assessment module and the equipment status information provided by the visualization module; The user interaction module is used to receive user control instructions, display device status information, and feed back user instructions to the feedback control module.

2. The electromechanical engineering safety visualization management system according to claim 1 is characterized in that: The data processing module includes at least one temperature sensor, pressure sensor and vibration sensor to ensure comprehensive acquisition of multivariate data.

3. The electromechanical engineering safety visualization management system according to claim 1 is characterized in that: The data processing module encodes data into quantum states using phase encoding and amplitude encoding methods.

4. The electromechanical engineering safety visualization management system according to claim 1 is characterized in that: The data processing module dynamically adjusts the noise coupling coefficient based on the ambient noise intensity.

5. The electromechanical engineering safety visualization management system according to claim 1 is characterized in that: The tensor generation module uses the following mathematical model to generate the tensor structure: ； In the formula, represents a physical quantity, This is an inner product involving two functions or states. represents some time-dependent state or wave function, represents some basis function or mode in space, Specifies the different dimensions of space, Re represents the real part of the inner product result, Indicates time With a central time The deviation, is a time scale that controls the width of the decay.

6. The electromechanical engineering safety visualization management system according to claim 1 is characterized in that: The tensor generation module implements feature extraction through the variational quantum tensor decomposition algorithm, and its optimization goal is: ； In the formula, Indicates the parameters Optimize, represents the norm, represents the main part of the objective function, represents a large matrix or tensor, is with The relevant weight coefficient, are basis functions or vectors in different dimensions, Represents different modes of tensors, represents the regularization part, is the number of some parameter or dimension.

7. The electromechanical engineering safety visualization management system according to claim 1 is characterized in that: The risk assessment module uses multiple algorithms to assess the safety of the equipment based on the equipment's historical operating data and current status, and generates corresponding alarms.

8. The electromechanical engineering safety visualization management system according to claim 1 is characterized in that: The visualization module can display the equipment status and risk assessment results in the form of three-dimensional holographic images, and supports interactive functions for users to obtain information in real time.

9. The electromechanical engineering safety visualization management system according to claim 1, characterized in that: The feedback control module dynamically adjusts the operating parameters of the electromechanical equipment according to the quantum information feedback and risk assessment results.

10. The electromechanical engineering safety visualization management system according to claim 1, characterized in that: The user interaction module includes a touch screen interface for displaying device status, receiving user instructions, and providing real-time feedback.

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