Electric permanent magnetic equipment intelligent regulation and control management system based on Internet of Things

Abstract

The invention discloses an intelligent regulation and control management system for electric permanent magnetic equipment based on Internet of Things, and relates to the technical field of equipment regulation and control. The monitoring information and the aging coefficient of the electric permanent magnet equipment are obtained, the corresponding electric-gravity control relation is obtained according to the electromagnetic density relation and the gravity-magnetic conversion relation of the electric permanent magnet equipment in combination with the aging coefficient, and the corresponding real-time control current is output according to the real-time adsorption weight through the electric-gravity control relation. According to different real-time adsorption weights and corresponding real-time control currents, an intelligent regulation and control model of the electric permanent magnet equipment is constructed, a corresponding digital twin model is constructed according to basic equipment information of the electric permanent magnet equipment, and analogue simulation is carried out on the electric permanent magnet equipment in the digital twin model to obtain simulation information of the electric permanent magnet equipment. Judging whether equipment abnormity exists or not according to the monitoring information; the pertinence and efficiency of equipment management and control can be improved, and the total power consumption of equipment operation can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment regulation and control, and specifically to an intelligent regulation and management system for electro-permanent magnet equipment based on the Internet of Things. Background Art

[0002] The purpose of intelligent regulation and control of electro-permanent magnet equipment is to utilize Internet of Things technology to achieve intelligent regulation and efficient management of electro-permanent magnet equipment, and use data analysis and algorithms to analyze equipment operation data to achieve intelligent control and management of the equipment, which can improve the operation efficiency, reliability and safety of the equipment;

[0003] In the prior art, when regulating and controlling electro-permanent magnet equipment, the aging differences between different equipment are often ignored, resulting in an unsatisfactory final regulation result. Moreover, in the prior art, the management and control of electro-permanent magnet equipment are not intelligent enough to provide a targeted regulation and control scheme in a timely and effective manner, resulting in high equipment power consumption. In view of the deficiencies of the prior art, the present invention provides an intelligent regulation and management system for electro-permanent magnet equipment based on the Internet of Things. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent regulation and management system for electro-permanent magnet equipment based on the Internet of Things.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An intelligent regulation and management system for electro-permanent magnet equipment based on the Internet of Things includes the following modules:

[0006] An equipment monitoring module, which is used to set a number of monitoring units on the electro-permanent magnet equipment and use the monitoring units to obtain different monitoring information respectively;

[0007] An equipment evaluation module, which is used to obtain the maximum adsorption force of the electro-permanent magnet equipment under standard working conditions and obtain the aging coefficient of the electro-permanent magnet equipment according to the maximum adsorption force;

[0008] A first construction module, which is used to obtain the electromagnetic density relationship and the heavy-magnetic conversion relationship of the electro-permanent magnet equipment respectively, and obtain the corresponding electro-heavy control relationship in combination with the aging coefficient of the electro-permanent magnet equipment;

[0009] A second construction module, which is used to obtain the real-time adsorption weight of the electro-permanent magnet equipment, output the corresponding real-time control current by using the electro-heavy control relationship, and construct an intelligent regulation model of the electro-permanent magnet equipment according to different real-time adsorption weights and their corresponding real-time control currents;

[0010] An equipment twin module, which is used to obtain the basic equipment information of the electro-permanent magnet equipment, construct a corresponding digital twin model, perform simulation on the electro-permanent magnet equipment in the digital twin model, obtain the simulation information of the electro-permanent magnet equipment, and judge whether there is equipment abnormality and give feedback in combination with the monitoring information.

[0011] Further, the process of setting several monitoring units on the electro-permanent magnet device and using the monitoring units to respectively obtain different monitoring information includes:

[0012] Set a current monitoring unit to obtain the current monitoring information on the coil branch circuit of the electro-permanent magnet device during operation in real time through the current monitoring unit. Set a vibration monitoring unit to obtain the vibration monitoring information on the pole working surface of the electro-permanent magnet device during operation in real time through the vibration monitoring unit;

[0013] Set a temperature monitoring unit to obtain the temperature monitoring information inside the electro-permanent magnet device during operation in real time through the temperature monitoring unit. The monitoring units include a current monitoring unit, a vibration monitoring unit, and a temperature monitoring unit. The monitoring information includes current monitoring information, vibration monitoring information, and temperature monitoring information.

[0014] Further, the process of obtaining the maximum adsorption force of the electro-permanent magnet device under standard working conditions and obtaining the aging coefficient of the electro-permanent magnet device based on the maximum adsorption force includes:

[0015] Under standard working conditions, apply a lateral force to the adsorbed object until it slides, and based on the lateral force F at this time h Obtain the maximum adsorption force F of the electro-permanent magnet device z ;

[0016] F z = μF h ;

[0017] where μ is the friction coefficient between the pole working surface and the adsorbed object, and the standard working conditions refer to the preset current value, temperature value, contact area, and weight of the adsorbed object;

[0018] Obtain the theoretical maximum adsorption force F of the electro-permanent magnet device under standard working conditions max , and obtain the aging coefficient S of the electro-permanent magnet device based on the maximum adsorption force and the theoretical maximum adsorption force p ;

[0019]

[0020] Further, the process of respectively obtaining the electromagnetic density relationship and the gravity-magnetism conversion relationship of the electro-permanent magnet device and obtaining the corresponding electro-gravity control relationship in combination with the aging coefficient of the electro-permanent magnet device includes:

[0021] According to the vacuum permeability μ0, effective pole area A, and magnetic circuit efficiency coefficient η of the electro-permanent magnet device, obtain the corresponding relationship between the adsorption force F and the magnetic flux density B of the electro-permanent magnet device;

[0022]

[0023] According to the number of turns N of the coil, the coil current I, and the equivalent magnetic circuit length l of the electro-permanent magnet device e , the relative magnetic permeability μ of the magnetic core r , obtain the residual magnetic flux density B of the permanent magnet included in the magnetic flux density B of the electro-permanent magnet device r and the exciting magnetic flux density B of the coil c ;

[0024] B = B r + B c ;

[0025]

[0026] Obtain the electromagnetic density relationship between the coil current I and the exciting magnetic flux density B c , and record the corresponding relationship parameter between the coil current I and the exciting magnetic flux density B c as k I ;

[0027]

[0028] Obtain the weight-magnetic conversion relationship between the adsorbed object weight m and the adsorption force F during the operation of the electro-permanent magnet device, where g is the acceleration due to gravity and ξ is a preset safety factor;

[0029] F = mgξ;

[0030] Combined with the aging coefficient S of the electro-permanent magnet device p obtain the electro-weight control relationship between the coil current I and the adsorbed object weight m;

[0031]

[0032] Further, the process of obtaining the real-time adsorption weight of the electro-permanent magnet device and outputting the corresponding real-time control current using the electro-weight control relationship includes:

[0033] Set up a weight monitoring unit to obtain the bearing weight of the electro-permanent magnet device during operation in real time through the weight monitoring unit, and subtract its no-load weight when it is no-load to obtain the corresponding real-time adsorption weight;

[0034] Input the real-time adsorption weight as the adsorbed object weight into the electro-weight control relationship and output the corresponding real-time control current.

[0035] Further, the process of constructing an intelligent regulation model of the electro-permanent magnet device according to different real-time adsorption weights and their corresponding real-time control currents includes:

[0036] Use the electro-weight control relationship to obtain the corresponding real-time control currents according to the real-time adsorption weights of different electro-permanent magnet devices respectively, and obtain the set of parameters corresponding to each real-time adsorption weight and its electro-permanent magnet device;

[0037] The parameter set includes an aging coefficient, an effective magnetic pole area, a magnetic circuit efficiency coefficient, the number of turns of the coil, a safety factor, a residual magnetic flux density, an equivalent magnetic circuit length, and a relative magnetic permeability of the magnetic core;

[0038] Generate an intelligent regulation set according to different real-time adsorption weights, the parameter set, and their corresponding real-time control currents, and divide the obtained intelligent regulation set into a training set and a test set;

[0039] Construct a convolutional neural network. Use different real-time adsorption weights and the parameter set in the training set as the input data of the convolutional neural network, and use the corresponding real-time control current in the training set as the output data of the convolutional neural network, and train the convolutional neural network to obtain an initial convolutional neural network;

[0040] Use the test set to verify the model of the initial convolutional neural network, and output the initial convolutional neural network whose test error threshold is less than or equal to the preset value as the corresponding intelligent regulation model.

[0041] Furthermore, obtain the basic device information of the electro-permanent magnet device, and construct a corresponding digital twin model. The process of simulating the electro-permanent magnet device in the digital twin model includes:

[0042] The basic device information includes the structural parameters, material parameters, electrical parameters, mechanical parameters, and environmental parameters of the electro-permanent magnet device;

[0043] Use digital twin technology to construct a digital twin model of the electro-permanent magnet device according to the obtained basic device information, and use simulation software to simulate the constructed digital twin model.

[0044] Furthermore, the process of obtaining the simulation information of the electro-permanent magnet device, combining the monitoring information to judge whether there is device abnormality and giving feedback includes:

[0045] In the actual application scenario, input the real-time adsorption weight and its parameter set of the electro-permanent magnet device into the intelligent regulation model to output the corresponding real-time control current, and operate the electro-permanent magnet device according to the obtained real-time control current;

[0046] Synchronize the real-time adsorption weight and real-time control current of the electro-permanent magnet device to the corresponding digital twin model, and use the digital twin model to obtain the simulation information at each monitoring unit of the electro-permanent magnet device, including current simulation information, vibration simulation information, and temperature simulation information;

[0047] According to the simulation information Wa at the same monitoring unit and the monitoring information Wb at the corresponding moment, obtain the deviation coefficient P of the monitoring information;

[0048]

[0049] Set a deviation threshold P0, compare the obtained deviation coefficient with the deviation threshold. If P > P0, it is determined that there is an abnormal situation in the electro-permanent magnet device, generate a corresponding device abnormal signal and feedback it to the relevant personnel;

[0050] The abnormal situation of the device includes abnormal temperature situation, abnormal vibration situation, and abnormal current situation. The device abnormal signal includes abnormal temperature signal, abnormal vibration signal, and abnormal current signal.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] By obtaining the aging coefficients of different electro-permanent magnet devices, the aging conditions of different devices can be included in the evaluation scope. By respectively obtaining the electromagnetic density relationship and the heavy-magnetic conversion relationship of the electro-permanent magnet device, and then combining the aging coefficient of the electro-permanent magnet device to obtain the corresponding electro-weight control relationship, the corresponding real-time control current can be output according to the real-time adsorption weight of the electro-permanent magnet device, which is beneficial to improving the pertinence of device management and control and can reduce the total power consumption of device operation;

[0053] By obtaining the real-time adsorption weight of different electro-permanent magnet devices and their corresponding real-time control currents, and constructing an intelligent regulation model in combination with the corresponding parameter set, the corresponding real-time control current can be directly output for different electro-permanent magnet devices, which can improve the efficiency of device management and control. By using the digital twin model to obtain various simulation information of the electro-permanent magnet device in real time and comparing it with the monitoring information at the corresponding moment, it can be judged whether there is an abnormal device, which helps the relevant personnel to repair the device in time. Description of the Drawings

[0054] Figure 1 It is the schematic diagram of the present invention. Detailed Embodiments

[0055] As Figure 1 shown, an intelligent regulation and management system for electro-permanent magnet devices based on the Internet of Things includes the following modules:

[0056] The device monitoring module is used to set a number of monitoring units on the electro-permanent magnet device and use the monitoring units to obtain different monitoring information respectively;

[0057] The device evaluation module is used to obtain the maximum adsorption force of the electro-permanent magnet device under standard working conditions and obtain the aging coefficient of the electro-permanent magnet device according to the maximum adsorption force;

[0058] The first construction module is used to respectively obtain the electromagnetic density relationship and the heavy-magnetic conversion relationship of the electro-permanent magnet device, and obtain the corresponding electro-weight control relationship in combination with the aging coefficient of the electro-permanent magnet device;

[0059] The second construction module is used to obtain the real-time adsorption weight of the electro-permanent magnet device, output the corresponding real-time control current by using the electro-weight control relationship, and construct an intelligent regulation model of the electro-permanent magnet device according to different real-time adsorption weights and their corresponding real-time control currents;

[0060] The device twin module is used to obtain the basic device information of the electro-permanent magnet device, construct a corresponding digital twin model, perform simulation on the electro-permanent magnet device in the digital twin model, obtain the simulation information of the electro-permanent magnet device, and judge whether there is device abnormality and give feedback in combination with the monitoring information.

[0061] It should be further noted that in the specific implementation process, setting a number of monitoring units on the electro-permanent magnet device, and the process of using the monitoring units to obtain different monitoring information respectively includes:

[0062] Set a corresponding current monitoring unit on the coil branch circuit of the electro-permanent magnet device, and obtain the coil current on the coil branch circuit of the electro-permanent magnet device during operation in real time through the current monitoring unit, and mark it as current monitoring information;

[0063] Set a corresponding vibration monitoring unit on the pole working surface of the electro-permanent magnet device, and obtain the vibration frequency on the pole working surface of the electro-permanent magnet device during operation in real time through the vibration monitoring unit, and mark it as vibration monitoring information;

[0064] Set a corresponding temperature monitoring unit inside the electro-permanent magnet device, and obtain the internal temperature value of the electro-permanent magnet device during operation in real time through the temperature monitoring unit, and mark it as temperature monitoring information. The monitoring units include current monitoring units, vibration monitoring units, and temperature monitoring units, and the monitoring information includes current monitoring information, vibration monitoring information, and temperature monitoring information.

[0065] It should be further noted that in the specific implementation process, the process of obtaining the maximum adsorption force of the electro-permanent magnet device under standard working conditions and obtaining the aging coefficient of the electro-permanent magnet device according to the maximum adsorption force includes:

[0066] Taking any electro-permanent magnet device as an example, the standard working condition refers to preset relevant parameters such as current value, temperature value, contact area, and adsorbed object weight. The contact area refers to the contact area between the pole working surface of the electro-permanent magnet device and the adsorbed object;

[0067] Under standard working conditions, apply a lateral force to the adsorbed object until it slides, and obtain the magnitude of the lateral force at this time, denoted as F h , and obtain the maximum adsorption force of the electro-permanent magnet device according to the lateral force, denoted as F z ;

[0068] F z = μFh ;

[0069] Among them, μ is the friction coefficient between the magnetic pole working surface and the adsorbed object, and its value range is 0.15 - 0.2;

[0070] Obtain the theoretical maximum adsorption force of the electro-permanent magnet device under standard working conditions, denoted as F max , and obtain the aging coefficient of the electro-permanent magnet device according to the maximum adsorption force and the theoretical maximum adsorption force, denoted as S p ;

[0071]

[0072] Adopt the same method to obtain the maximum adsorption force of different electro-permanent magnet devices respectively, and obtain their corresponding aging coefficients.

[0073] It should be further noted that in the specific implementation process, the process of respectively obtaining the electromagnetic density relationship and the heavy-magnetic conversion relationship of the electro-permanent magnet device, and obtaining the corresponding electro-heavy control relationship in combination with the aging coefficient of the electro-permanent magnet device includes:

[0074] According to the Maxwell stress tensor method, the corresponding relationship between the adsorption force F and the magnetic flux density B of the electro-permanent magnet device is:

[0075]

[0076] Among them, μ0 is the vacuum permeability, A is the effective magnetic pole area, η is the magnetic circuit efficiency coefficient, and its value range is 0.6 - 0.9;

[0077] The magnetic flux density B of the electro-permanent magnet device includes the residual magnetic flux density B of the permanent magnet r and the excitation magnetic flux density B of the coil c ;

[0078] B = B r + B c ;

[0079]

[0080] Among them, N is the number of turns of the coil, I is the coil current, l e is the equivalent magnetic circuit length, μ r is the relative magnetic permeability of the magnetic core;

[0081] Based on this, obtain the corresponding relationship between the coil current I and the excitation magnetic flux density B c , denoted as the electromagnetic density relationship;

[0082]

[0083] Among them, k I is the coil current I and the excitation magnetic flux density Bc The corresponding relationship parameter between;

[0084] Obtain the corresponding relationship between the adsorbed object weight m and the adsorption force F during the operation of the electro-permanent magnet device, denoted as the heavy-magnetic conversion relationship;

[0085] F = mgξ;

[0086] Wherein, g is the acceleration due to gravity, and ξ is a preset safety factor, and the value range is 1.5 to 2;

[0087] Combined with the aging coefficient S of the electro-permanent magnet device p Obtain the corresponding relationship between the coil current I and the adsorbed object weight m, denoted as the electro-weight control relationship;

[0088]

[0089] It should be further noted that in the specific implementation process, the process of obtaining the real-time adsorption weight of the electro-permanent magnet device and outputting the corresponding real-time control current using the electro-weight control relationship includes:

[0090] Set a corresponding weight monitoring unit in the bearing structure of the electro-permanent magnet device, and obtain the bearing weight of the electro-permanent magnet device during operation in real time through the weight monitoring unit, and subtract its no-load weight when it is no-load to obtain the corresponding real-time adsorption weight;

[0091] Take the monitored real-time adsorption weight as the adsorbed object weight and input it into the electro-weight control relationship, and output the corresponding coil current, denoted as the real-time control current. All the parameters in the electro-weight control relationship are common parameters in the field and can be directly obtained.

[0092] It should be further noted that in the specific implementation process, the process of constructing the intelligent regulation model of the electro-permanent magnet device according to different real-time adsorption weights and their corresponding real-time control currents includes:

[0093] Use the electro-weight control relationship to respectively obtain the corresponding real-time control currents according to the real-time adsorption weights of different electro-permanent magnet devices, and obtain each real-time adsorption weight and the parameter set corresponding to the electro-permanent magnet device. The parameter set includes the aging coefficient, effective pole area, magnetic circuit efficiency coefficient, number of coil turns, safety factor, residual magnetic flux density, equivalent magnetic circuit length, and relative magnetic permeability of the magnetic core;

[0094] Generate an intelligent regulation set according to different real-time adsorption weights, parameter sets, and their corresponding real-time control currents, and divide the obtained intelligent regulation set into a training set and a test set;

[0095] Construct a convolutional neural network. Use different real-time adsorption weights and parameter sets in the training set as the input data of the convolutional neural network, and use the corresponding real-time control current in the training set as the output data of the convolutional neural network. Train the convolutional neural network to obtain an initial convolutional neural network;

[0096] Use the test set to verify the model of the initial convolutional neural network, and output the initial convolutional neural network with a test error threshold less than or equal to the preset value as the corresponding intelligent regulation model.

[0097] It should be further noted that in the specific implementation process, obtaining the basic device information of the electro-permanent magnet device and constructing a corresponding digital twin model. The process of simulating the electro-permanent magnet device in the digital twin model includes:

[0098] Taking any electro-permanent magnet device as an example, the basic device information refers to the various data necessary for constructing the digital twin model of the electro-permanent magnet device, including structural parameters, material parameters, electrical parameters, mechanical parameters, environmental parameters, etc.;

[0099] Use digital twin technology to construct the digital twin model of the electro-permanent magnet device according to the obtained basic device information, and use simulation software to simulate the constructed digital twin model. The digital twin model can simulate the operation process of the electro-permanent magnet device according to the synchronized data.

[0100] It should be further noted that in the specific implementation process, the process of obtaining the simulation information of the electro-permanent magnet device, combining the monitoring information to judge whether there is equipment abnormality and giving feedback includes:

[0101] In the actual application scenario, input the real-time adsorption weight and its parameter set of the electro-permanent magnet device into the intelligent regulation model, and output the corresponding real-time control current through the intelligent regulation model. Operate the electro-permanent magnet device according to the obtained real-time control current;

[0102] Synchronize the real-time adsorption weight and real-time control current of the electro-permanent magnet device to the corresponding digital twin model, and use the digital twin model to obtain the simulation information at each monitoring unit of the electro-permanent magnet device, including current simulation information, vibration simulation information, and temperature simulation information;

[0103] Mark the temperature simulation information as W a and mark the temperature monitoring information at the corresponding moment as W b and obtain the deviation coefficient of the temperature monitoring information, denoted as P;

[0104]

[0105] Set a deviation threshold P0, compare the obtained deviation coefficient with the deviation threshold. If P > P0, it is determined that there is a temperature anomaly in the electro-permanent magnet device, and a corresponding temperature anomaly signal is generated, and the generated temperature anomaly signal is fed back to the relevant personnel;

[0106] Adopt the same method to obtain the deviation coefficients of the vibration monitoring information and the current monitoring information respectively, and then determine whether there are vibration anomalies and current anomalies in the electro-permanent magnet device respectively, and generate corresponding vibration anomaly signals and current anomaly signals for feedback.

[0107] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent control and management system for electric permanent magnet equipment based on the Internet of Things, characterized in that: Includes the following modules: The equipment monitoring module is used to set a number of monitoring units on the electro-permanent magnetic equipment and use the monitoring units to obtain different monitoring information respectively; The equipment evaluation module is used to obtain the maximum adsorption force of the electric permanent magnet equipment under standard working conditions, and obtain the aging coefficient of the electric permanent magnet equipment according to the maximum adsorption force; The first building block is used to obtain the electromagnetic density relationship and the gravity-magnetic conversion relationship of the electric permanent magnet device respectively, and obtain the corresponding electric-gravity control relationship in combination with the aging coefficient of the electric permanent magnet device; The second construction module is used to obtain the real-time adsorption weight of the electric permanent magnet device, output the corresponding real-time control current using the electric-weight control relationship, and construct an intelligent control model of the electric permanent magnet device according to different real-time adsorption weights and their corresponding real-time control currents; The equipment twin module is used to obtain the basic equipment information of the electric permanent magnet equipment and build the corresponding digital twin model. It simulates the electric permanent magnet equipment in the digital twin model, obtains the simulation information of the electric permanent magnet equipment, and combines the monitoring information to determine whether there is any equipment abnormality and provide feedback.

2. According to the Internet of Things-based intelligent control and management system for electric permanent magnet equipment according to claim 1, it is characterized in that: The process of setting up monitoring units and obtaining different monitoring information includes: A current monitoring unit is provided to obtain the current monitoring information on the coil branch circuit of the electric permanent magnet device in real time during operation through the current monitoring unit; a vibration monitoring unit is provided to obtain the vibration monitoring information on the magnetic pole working surface of the electric permanent magnet device in real time during operation through the vibration monitoring unit; A temperature monitoring unit is set up to obtain real-time temperature monitoring information inside the electro-permanent magnet equipment during operation through the temperature monitoring unit. The monitoring unit includes a current monitoring unit, a vibration monitoring unit, and a temperature monitoring unit. The monitoring information includes current monitoring information, vibration monitoring information, and temperature monitoring information.

3. According to the Internet of Things-based intelligent control and management system for electric permanent magnet equipment according to claim 2, it is characterized in that: The process of obtaining the aging coefficient of the electric permanent magnet equipment according to the maximum adsorption force includes: Under standard working conditions, a lateral force is applied to the adsorbed object until it slides. h Get the maximum adsorption force F of the permanent magnet device z ; F z =μF h ; Wherein, μ is the friction coefficient between the working surface of the magnetic pole and the adsorbed object, and the standard working condition refers to the preset current value, temperature value, contact area, and weight of the adsorbed object; Obtain the theoretical maximum adsorption force F of the electro-permanent magnetic device under standard working conditions max , according to the maximum adsorption force and the theoretical maximum adsorption force, the aging coefficient S of the permanent magnet device is obtained. p ; 4. According to the Internet of Things-based intelligent control and management system for electric permanent magnet equipment according to claim 3, it is characterized in that: The process of obtaining the electromagnetic density relationship and the gravity-magnetic conversion relationship and obtaining the electric-gravity control relationship in combination with the aging coefficient includes: According to the vacuum magnetic permeability μ0, effective magnetic pole area A, and magnetic circuit efficiency coefficient η of the electric permanent magnetic device, the corresponding relationship between the adsorption force F and the magnetic flux density B of the electric permanent magnetic device is obtained; According to the number of coil turns N, coil current I and equivalent magnetic circuit length l of the permanent magnet equipment e , core relative permeability μ r , obtain the residual magnetic flux density B of the permanent magnet contained in the magnetic flux density B of the electro-permanent magnetic device r and the coil excitation flux density B c ; B=B r +B c ; Get the coil current I and excitation flux density B c The electromagnetic density relationship between the coil current I and the excitation flux density B c The corresponding relationship parameter is denoted as k I ; Obtain the gravity-magnetism conversion relationship between the weight m of the adsorbed object and its adsorption force F during the operation of the electric permanent magnet device, where g is the gravitational acceleration and ξ is the preset safety factor; F = mgξ; Combined with the aging factor S of the permanent magnet equipment p Obtain the electric-weight control relationship between the coil current I and the weight m of the adsorbed object; 5. According to the Internet of Things-based intelligent control and management system for electric permanent magnet equipment according to claim 4, it is characterized in that: The process of obtaining the real-time adsorption weight and real-time control current of the electro-permanent magnetic device includes: A weight monitoring unit is provided to obtain the load-bearing weight of the electro-permanent magnetic device in real time during operation through the weight monitoring unit, and to subtract the no-load weight when no-loaded to obtain the corresponding real-time adsorption weight; The real-time adsorption weight is input into the electric-weight control relationship as the weight of the adsorbed object, and the corresponding real-time control current is output.

6. The intelligent control and management system for electric permanent magnet equipment based on the Internet of Things according to claim 5 is characterized in that: The process of building an intelligent control model for electric permanent magnet equipment includes: Using the electric-weight control relationship, the corresponding real-time control current is obtained according to the real-time adsorption weight of different permanent magnetic devices, and the parameter set corresponding to each real-time adsorption weight and its permanent magnetic device is obtained; The parameter set includes aging coefficient, effective magnetic pole area, magnetic circuit efficiency coefficient, number of coil turns, safety factor, residual magnetic flux density, equivalent magnetic circuit length, and magnetic core relative permeability; Generate an intelligent control set according to different real-time adsorption weights and parameter sets and their corresponding real-time control currents, and divide the obtained intelligent control set into a training set and a test set; Constructing a convolutional neural network, taking different real-time adsorption weights and parameter sets in the training set as input data of the convolutional neural network, taking the corresponding real-time control current in the training set as output data of the convolutional neural network, and training the convolutional neural network to obtain an initial convolutional neural network; The test set is used to verify the model of the initial convolutional neural network, and the initial convolutional neural network with a preset test error threshold is output as the corresponding intelligent control model.

7. The intelligent control and management system for electric permanent magnet equipment based on the Internet of Things according to claim 6 is characterized in that: The process of simulating an electro-permanent magnetic device in a digital twin model includes: The basic equipment information includes structural parameters, material parameters, electrical parameters, mechanical parameters, and environmental parameters of the electric permanent magnet equipment; The digital twin technology is used to construct a digital twin model of the electro permanent magnet equipment according to the acquired basic equipment information, and the constructed digital twin model is simulated using simulation software.

8. The intelligent control and management system for electric permanent magnet equipment based on the Internet of Things according to claim 7 is characterized in that: The process of obtaining simulation information, combining it with monitoring information to determine whether there is equipment abnormality and providing feedback includes: In actual application scenarios, the real-time adsorption weight and parameter set of the electric permanent magnet device are input into the intelligent control model to output the corresponding real-time control current, and the electric permanent magnet device is operated according to the obtained real-time control current; The real-time adsorption weight and real-time control current of the electric permanent magnet device are synchronized to the corresponding digital twin model, and the analog information of each monitoring unit of the electric permanent magnet device is obtained by using the digital twin model, including current analog information, vibration analog information, and temperature analog information; According to the simulation information W at the same monitoring unit a and the monitoring information W at the corresponding time b , obtain the deviation coefficient P of the monitoring information; A deviation threshold value P0 is set, and the obtained deviation coefficient is compared with the deviation threshold value. If P>P0, it is determined that the electric permanent magnet equipment has an abnormality, and a corresponding abnormality signal is generated and fed back to relevant personnel; Abnormal conditions of equipment include abnormal temperature conditions, abnormal vibration conditions, and abnormal current conditions. Abnormal signals of equipment include abnormal temperature signals, abnormal vibration signals, and abnormal current signals.

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