Industrial equipment operation and maintenance remote inspection simulation system and method based on virtual reality

Abstract

The present invention discloses a virtual reality-based industrial equipment operation and maintenance remote inspection simulation system and method, and relates to the field of inspection simulation. The present invention iteratively adjusts the parameters of a virtual reality development platform for building a virtual scene of industrial equipment to be inspected for multiple times, so that the industrial equipment to be inspected in the virtual scene can accurately reflect the actual operating status of the industrial equipment to be inspected, so that the operation and maintenance personnel can know the actual operating status of the industrial equipment to be inspected; the operating parameters of the industrial equipment to be inspected in the virtual scene are iteratively adjusted for multiple times, so that the operating status of the industrial equipment to be inspected in the virtual scene is restored to normal, and then the operation and maintenance personnel adjust the operating parameters of the actual industrial equipment to be inspected, so that the actual industrial equipment to be inspected can operate normally, and by setting the operating parameter error threshold, a judgment basis is provided for judging whether the industrial equipment in the current virtual environment meets the requirements.

Description

Technical Field

[0001] The present invention belongs to the field of inspection simulation, and in particular, relates to a virtual reality-based industrial equipment operation and maintenance remote inspection simulation system and method. Background Art

[0002] When conducting remote inspection simulations on the operation and maintenance of industrial equipment, if there is a large error between the simulated scenario and the actual operation scenario, the simulated scenario will not be able to truly reflect the actual operating status and potential safety hazards of the equipment, causing the operation and maintenance personnel to face safety risks in actual operations; and it may provide erroneous information, which may affect the operation and maintenance personnel's judgment of the equipment status and lead to wrong decisions. Summary of the invention

[0003] In response to the problems in the related technology, the present invention proposes a virtual reality-based industrial equipment operation and maintenance remote inspection simulation system and method to overcome the above-mentioned technical problems existing in the existing related technology.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention is a virtual reality-based industrial equipment operation and maintenance remote inspection simulation method, comprising the following steps:

[0006] S1. Collect modeling data of industrial equipment to be inspected, calculate and screen, and obtain an average numerical data set of modeling of industrial equipment to be inspected and a screened image data set of modeling of industrial equipment to be inspected;

[0007] S2. Perform three-dimensional modeling based on the average numerical data set of the modeling of the industrial equipment to be inspected and the image data set of the modeling of the industrial equipment to be inspected after screening to obtain a three-dimensional model of the industrial equipment to be inspected; construct a virtual scene for the operation of the three-dimensional model of the industrial equipment to be inspected to obtain an initial virtual scene of the industrial equipment to be inspected;

[0008] S3, optimizing the parameters of the initial virtual scene of industrial equipment to be inspected and the actual operating parameter data of the industrial equipment to be inspected in order to obtain a final virtual scene of industrial equipment to be inspected;

[0009] S4, the construction module uses the historical operating parameter data of the industrial equipment to be inspected and the corresponding operating status data collected multiple times to construct an operating status mapping model; cooperates with the operating status mapping model to adjust the operating parameters of the industrial equipment to be inspected with abnormal operating status in the final virtual scene of the industrial equipment to be inspected, and obtains the operating parameter inspection reference data set of the industrial equipment to be inspected; remotely adjusts the operating parameters of the industrial equipment to be inspected according to the operating parameter inspection reference data set of the industrial equipment to be inspected;

[0010] In this scheme, the parameters of the virtual reality development platform used to build the virtual scene of the industrial equipment to be inspected are iteratively adjusted multiple times, so that the error between the operating parameter data of the industrial equipment to be inspected in the virtual scene and the actual operating parameter data of the industrial equipment to be inspected is less than a set threshold, so that the industrial equipment to be inspected in the virtual scene can accurately reflect the operating status of the actual industrial equipment to be inspected, so that the operation and maintenance personnel can know the actual operating status of the industrial equipment to be inspected based on the operating status of the industrial equipment to be inspected in the virtual scene; when the industrial equipment to be inspected in the virtual scene has an operating abnormality, this scheme iteratively adjusts the operating parameters of the industrial equipment to be inspected in the virtual scene multiple times, so that the operating status of the industrial equipment to be inspected in the virtual scene returns to normal, and then the operation and maintenance personnel can adjust the operating parameters of the actual industrial equipment to be inspected based on the adjusted operating parameter data of the industrial equipment to be inspected in the virtual scene, so that the actual industrial equipment to be inspected can operate normally.

[0011] Preferably, the S1 comprises the following steps:

[0012] S11, setting the industrial equipment to be inspected; setting a plurality of numerical data types and image data types required for three-dimensional modeling of the industrial equipment to be inspected, and obtaining a set of numerical data types for industrial equipment modeling. and a set of industrial equipment modeling image data types , a 1i , a 4i Respectively indicate the setting i industrial equipment modeling data types and industrial equipment modeling image data types, , respectively represent the total number of the set industrial equipment modeling data types and the industrial equipment modeling image data types; set a number of acquisition devices for acquiring the data required for modeling of the industrial equipment to be inspected, and obtain the industrial equipment acquisition device set , a 2i Indicates setting i Industrial equipment collection equipment, Indicates the total number of set industrial equipment collection devices;

[0013] S12, according to the industrial equipment modeling numerical data type set and the industrial equipment modeling image data type set, and using the industrial equipment acquisition equipment in the industrial equipment acquisition equipment set to repeatedly collect the modeling numerical data and modeling image data of the industrial equipment to be inspected, to obtain the modeling numerical data matrix of the industrial equipment to be inspected a 3 and the image data matrix of the industrial equipment to be inspected a5; as follows,

[0014] ; ;

[0015] in, a 3ij , a 5ij Respectively represent i The first data of the industrial equipment to be inspected collected j types of modeled numerical data and modeled image data, Indicates the total number of times the modeling numerical data and modeling image data of the industrial equipment to be inspected are collected;

[0016] S13, calculating the average value of each type of modeling numerical data of the industrial equipment to be inspected collected multiple times according to the modeling numerical data matrix of the industrial equipment to be inspected, to obtain the average numerical data set of the modeling numerical data of the industrial equipment to be inspected , a 6j Indicates the first j The average value of the modeling numerical data of the types; the calculation formula is as follows,

[0017] ;

[0018] The modeling image data of each type of industrial equipment to be inspected collected each time in the modeling image data matrix of the industrial equipment to be inspected is screened; after the screening is completed, the modeling image data set of the industrial equipment to be inspected after the screening is obtained , a 7j Indicates the first j Types of modeling image data;

[0019] The industrial equipment modeling numerical data type set includes the equipment's dimension data type, such as length, width, height, radius, and the connection relationship data type of each component of the equipment; the industrial equipment modeling image data type set includes the image data type of each component of the industrial equipment, the image data type of the connection between each component, and the precise texture image data type of the surface of each component; the industrial equipment acquisition equipment set includes vernier calipers, height gauges, wall thickness gauges, intelligent diameter gauges, laser thickness gauges, image measuring instruments, three-coordinate measuring machines, laser trackers, flash testers, and cameras;

[0020] By calculating the average value of the modeling numerical data of each type of industrial equipment to be inspected that is collected multiple times, the randomness of the modeling data of the industrial equipment to be inspected is reduced; by screening the modeling image data of each type of industrial equipment to be inspected that is collected each time in the modeling image data matrix of the industrial equipment to be inspected, the image data with lower clarity is eliminated and the image data with the highest clarity is retained, so that the subsequent model constructed for the industrial equipment to be inspected is more accurate.

[0021] Preferably, in S13, each type of modeling image data of industrial equipment to be inspected collected each time in the modeling image data matrix of industrial equipment to be inspected is screened; after the screening is completed, obtaining the screened modeling image data set of industrial equipment to be inspected includes the following steps:

[0022] S131, collecting multiple modeling image data and corresponding definition data of historical industrial equipment in conjunction with the industrial equipment modeling image data type set to obtain a historical industrial equipment modeling image data set and historical industrial equipment modeling image clarity dataset , , Respectively represent the collected i Historical industrial equipment modeling image data and corresponding image clarity data, Indicates the total amount of image definition data collected;

[0023] S132, using the historical industrial equipment modeling image clarity data set and the historical industrial equipment modeling image clarity data set to construct an image clarity mapping model; inputting the modeling image data in the industrial equipment modeling image data matrix to be inspected into the image clarity mapping model for mapping, and obtaining the industrial equipment modeling image clarity data matrix to be inspected a 8; as follows,

[0024] ;

[0025] in, a 8ij Indicates i The first data of the industrial equipment to be inspected collected j Types of clarity data for modeling image data; a 7j The calculation formula is as follows,

[0026] ;

[0027] By constructing an image clarity mapping model, a mapping model is provided for obtaining the clarity data of the modeling image data in the modeling image data matrix of the industrial equipment to be inspected, thereby facilitating subsequent screening.

[0028] Preferably, S2 comprises the following steps:

[0029] S21, performing three-dimensional modeling of the industrial equipment to be inspected according to the average numerical data set of the modeling of the industrial equipment to be inspected and the image data set of the modeling of the industrial equipment to be inspected after screening, to obtain a three-dimensional model of the industrial equipment to be inspected;

[0030] S22, setting several parameter types of virtual reality development platforms to obtain a virtual reality parameter type set; then setting several operating parameter types of the industrial equipment to be inspected to obtain an industrial equipment operating parameter type set; setting initial parameter data of the virtual reality development platform according to the virtual reality parameter type set to obtain a virtual reality initial parameter data set , b 1i The virtual reality development platform i Types of initial parameter data, Represents the total number of parameter types of the virtual reality development platform;

[0031] S23, importing the three-dimensional model of the industrial equipment to be inspected into the virtual reality development platform, and then collecting the current actual operating parameter data of the industrial equipment to be inspected according to the industrial equipment operating parameter type set to obtain a current actual operating parameter data set; setting the operating parameters of the industrial equipment to be inspected imported into the virtual scene according to the current actual operating parameter data set, and obtaining an initial virtual scene of the industrial equipment to be inspected after the setting is completed;

[0032] By constructing the initial virtual scene of the industrial equipment to be inspected, an optimization object is provided for the subsequent model optimization, thereby reflecting the actual operation status of the industrial equipment to be inspected in real time; the industrial equipment operation parameter types include the motion trajectory, speed, acceleration, etc. of each component of the equipment; the virtual reality parameter types include resolution, that is, the pixel density of the display, delay: the time difference from user action to screen feedback, etc.

[0033] Preferably, S3 comprises the following steps:

[0034] S31, set multiple operation data collection time points to obtain an operation data collection time point set; collect the operation parameter data of the initial virtual scene of the industrial equipment to be inspected and the actual industrial equipment to be inspected according to the operation data collection time point set and the industrial equipment operation parameter type set, and obtain an initial virtual operation parameter data matrix And the actual operation parameter data matrix ; They are as follows,

[0035] ; ;

[0036] in, , Respectively indicate that in the setting i The initial virtual scene of the industrial equipment to be inspected collected at the first collection time point and the actual industrial equipment to be inspected j types of operating parameter data, Indicates the total number of set collection time points. Indicates the total number of the types of operating parameters of the industrial equipment to be inspected;

[0037] S32, calculating the Euclidean distance data between the initial virtual operation parameter data matrix and the actual operation parameter data matrix, and obtaining the operation parameter error data c ; The calculation formula is as follows,

[0038] ;

[0039] S33, setting an operation parameter error threshold; when the operation parameter error data is greater than or equal to the operation parameter error threshold, adjusting the virtual reality initial parameter data set until the operation parameter error data is less than the operation parameter error threshold, and obtaining the virtual reality final parameter data set , b 2i Indicates that the adjustment is made to obtain the first i otherwise, there is no need to adjust the virtual reality initial parameter data set, and the virtual reality initial parameter data set is used as the virtual reality final parameter data set;

[0040] S34, setting the parameters of the initial virtual scene of the industrial equipment to be inspected according to the virtual reality final parameter data set, and after the setting is completed, obtaining the final virtual scene of the industrial equipment to be inspected;

[0041] By calculating the Euclidean distance data between the initial virtual operating parameter data matrix and the actual operating parameter data matrix, the difference between the operating conditions of industrial equipment in the current virtual environment and the actual industrial equipment can be obtained; by setting the operating parameter error threshold, a quantitative judgment basis is provided for determining whether the error between the operating conditions of industrial equipment in the current virtual environment and the actual industrial equipment meets the requirements of remote operation and maintenance.

[0042] Preferably, S4 comprises the following steps:

[0043] S41, according to the industrial equipment operation parameter type set, repeatedly collect historical operation parameter data of the industrial equipment to be inspected and the corresponding operation status data of the industrial equipment to be inspected, to obtain a historical operation parameter data matrix And historical operation status data set , Indicates i The historical operating status data of the industrial equipment to be inspected obtained by the collection; The value is 0 or 1. When the value is 0, it indicates that the operation status is abnormal, and when the value is 1, it indicates that the operation status is normal; e Indicates the total number of times the historical operating parameter data of the industrial equipment to be inspected and the corresponding operating status data of the industrial equipment to be inspected are collected; as follows,

[0044] ;

[0045] in, Indicates i The first collected data of the industrial equipment to be inspected j types of historical operating parameter data;

[0046] Constructing an operation status mapping model according to the historical operation parameter data matrix and the historical operation status data set;

[0047] S42, collecting the operating parameter data of the industrial equipment in the final virtual scene of the industrial equipment to be inspected in real time according to the industrial equipment operating parameter type set, to obtain a real-time operating parameter data set of the industrial equipment to be inspected; inputting the real-time operating parameter data set of the industrial equipment to be inspected into the operating status mapping model for mapping, to obtain the real-time operating status data of the industrial equipment to be inspected ;

[0048] When the real-time operating status data of the industrial equipment to be inspected is 0, the real-time operating parameter data set of the industrial equipment to be inspected is optimized in conjunction with the operating status mapping model until the real-time operating status data of the industrial equipment to be inspected is 1, and the inspection reference data set of the operating parameters of the industrial equipment to be inspected is obtained and displayed; otherwise, there is no need to optimize the real-time operating parameter data set of the industrial equipment to be inspected;

[0049] S43, the industrial equipment operation and maintenance personnel remotely adjust the operating parameters of the industrial equipment to be inspected according to the inspection reference data set of the operating parameters of the industrial equipment to be inspected;

[0050] By constructing an operation status mapping model, the corresponding operation status data can be obtained through the set operation parameter data, and the effect of each iterative adjustment of the operation parameter data of the corresponding industrial equipment to be inspected can be obtained, so as to iterate in the right direction.

[0051] Preferably, in S42, the real-time operating parameter data set of the industrial equipment to be inspected is optimized in conjunction with the operating status mapping model until the real-time operating status data of the industrial equipment to be inspected is 1, and obtaining the inspection reference data set of the operating parameters of the industrial equipment to be inspected includes the following steps:

[0052] S421, build operating parameters to adjust Harris Hawk population , Indicates that the operating parameters adjust the Harris Hawk population i Harris Hawk, Indicates that the operating parameters adjust the size of the Harris Hawk population; Set the maximum number of iterations for adjusting the Harris Hawk population by the operating parameters to And the current number of iterations is , respectively recorded as the maximum number of iterations of the operating parameter adjustment and the current number of iterations of the operating parameter adjustment; the search space dimension of the operating parameter adjustment Harris Hawk population is set to ;

[0053] S422: Set the operating parameter data value interval of each type of the industrial equipment to be inspected according to the industrial equipment operating parameter type set to obtain an operating parameter value interval set. d 2; as follows,

[0054] ;

[0055] in, , Respectively represent the first i The lower and upper limits of the values ​​of the various types of operating parameter data;

[0056] The operating parameters are set according to the operating parameter value interval set to adjust the initial position of each Harris hawk in the Harris hawk population to obtain a second initial position matrix ;as follows,

[0057] ;

[0058] in, Indicates that the operating parameters adjust the Harris Hawk population j The initial position of the Harris Hawk is at the first i The position component of the operating parameter data dimension of the type; the calculation formula is as follows,

[0059] ;

[0060] In the formula, rand 2ji Indicates for Generate a random number between 0 and 1;

[0061] S423, based on the real-time operating status data of the industrial equipment to be inspected Constructing a fitness function for operating parameters to adjust the Harris Hawk population ;as follows,

[0062] ;

[0063] S424, start iteration, before iteration, adjust the current iteration number of the operating parameters to 1; in the first round of iteration, use the operating parameters to adjust the fitness function of the Harris hawk population And cooperate with the operation state mapping model to calculate the fitness value of the initial position of each Harris hawk in the second initial position matrix to obtain a third fitness value set; the maximum fitness value in the third fitness value set and the corresponding initial position of the Harris hawk are respectively used as the third global optimal fitness and the third global optimal position; according to the third global optimal fitness and the third global optimal position, the initial position of each Harris hawk in the second initial position matrix is ​​updated; after the update is completed, the operation parameter is adjusted to the current iteration number plus 1 and enter the next round of iteration;

[0064] In each other iteration, the operating parameters are used to adjust the fitness function of the Harris Hawk population. The operation state mapping model is used to calculate the fitness value of the position of each Harris hawk in the Harris hawk population adjusted by the operation parameters updated in the previous iteration process to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding position of the Harris hawk are used as the fourth global optimal fitness and the fourth global optimal position respectively; the operation parameters updated in the previous iteration process are used to adjust the position of each Harris hawk in the Harris hawk population to update according to the fourth global optimal fitness and the fourth global optimal position; after the update is completed, the operation parameters are adjusted to the current iteration number plus 1 and enter the next iteration;

[0065] S425, when When , stop the iteration and get the second final global optimal position; otherwise, continue to iterate until until the time; taking the second final global optimal position as the optimized real-time operating parameter data set of the industrial equipment to be inspected; inputting the optimized real-time operating parameter data set of the industrial equipment to be inspected into the operating state mapping model for mapping, and obtaining the optimized real-time operating state data of the industrial equipment to be inspected;

[0066] When the optimized real-time operating status data of the industrial equipment to be inspected is 1, the optimized real-time operating status data of the industrial equipment to be inspected is used as a reference data set for inspection of operating parameters of the industrial equipment to be inspected; otherwise, return to S424 and continue to iterate until the optimized real-time operating status data of the industrial equipment to be inspected is 1;

[0067] The Harris Eagle optimization algorithm is used to perform multiple iterative optimizations on the operating parameter data of the industrial equipment to be inspected with abnormal operating status, and the operating status data of the industrial equipment to be inspected is used as the fitness function. Therefore, as the iteration proceeds, the operating status data of the industrial equipment to be inspected will return to normal, thereby providing reference data for subsequent operation and maintenance personnel to operate and maintain the actual industrial equipment to be inspected.

[0068] The remote inspection simulation system for industrial equipment operation and maintenance based on virtual reality includes an industrial equipment modeling data type setting module, a modeling data acquisition and calculation module, a three-dimensional modeling module, a virtual scene construction module, a virtual scene parameter optimization module, an operation status mapping model, an operation parameter adjustment module for industrial equipment to be inspected, and a remote operation and maintenance module.

[0069] The present invention has the following beneficial effects:

[0070] 1. In the present invention, the parameters of the virtual reality development platform used to build the virtual scene of the industrial equipment to be inspected are adjusted iteratively for multiple times, so that the industrial equipment to be inspected in the virtual scene can accurately reflect the operating status of the actual industrial equipment to be inspected, so that the operation and maintenance personnel can know the actual operating status of the industrial equipment to be inspected; the operating parameters of the industrial equipment to be inspected in the virtual scene are adjusted iteratively for multiple times, so that the operating status of the industrial equipment to be inspected in the virtual scene is restored to normal, and then the operation and maintenance personnel adjust the operating parameters of the actual industrial equipment to be inspected, so that the actual industrial equipment to be inspected can operate normally.

[0071] 2. The present invention constructs an image clarity mapping model to provide a mapping model for obtaining clarity data of modeling image data in the modeling image data matrix of the industrial equipment to be inspected, thereby facilitating subsequent screening.

[0072] 3. In the present invention, by setting the operating parameter error threshold, a quantitative basis is provided for determining whether the error between the operating conditions of industrial equipment in the current virtual environment and the actual industrial equipment meets the requirements of remote operation and maintenance.

[0073] 4. In the present invention, the Harris Eagle optimization algorithm is used to perform multiple iterative optimizations on the operating parameter data of the industrial equipment to be inspected with abnormal operating status, and the operating status data of the industrial equipment to be inspected is used as the fitness function. Therefore, as the iteration proceeds, the operating status data of the industrial equipment to be inspected will return to normal, thereby providing reference data for subsequent operation and maintenance personnel to operate and maintain the actual industrial equipment to be inspected.

[0074] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0076] Figure 1 It is a flow chart of the virtual reality-based industrial equipment operation and maintenance remote inspection simulation method of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0078] Embodiment 1

[0079] See also Figure 1 , this embodiment is a remote inspection simulation method for industrial equipment operation and maintenance based on virtual reality, comprising the following steps:

[0080] S1. Collect modeling data of industrial equipment to be inspected, calculate and screen, and obtain an average numerical data set of modeling of industrial equipment to be inspected and a screened image data set of modeling of industrial equipment to be inspected;

[0081] The S1 comprises the following steps:

[0082] S11, setting the industrial equipment to be inspected; setting a plurality of numerical data types and image data types required for three-dimensional modeling of the industrial equipment to be inspected, and obtaining a set of numerical data types for industrial equipment modeling. and a set of industrial equipment modeling image data types , a 1i , a4i Respectively indicate the setting i industrial equipment modeling data types and industrial equipment modeling image data types, , respectively represent the total number of the set industrial equipment modeling data types and the industrial equipment modeling image data types; set a number of acquisition devices for acquiring the data required for modeling of the industrial equipment to be inspected, and obtain the industrial equipment acquisition device set , a 2i Indicates setting i Industrial equipment collection equipment, Indicates the total number of set industrial equipment collection devices;

[0083] S12, according to the industrial equipment modeling numerical data type set and the industrial equipment modeling image data type set, and using the industrial equipment acquisition equipment in the industrial equipment acquisition equipment set to repeatedly collect the modeling numerical data and modeling image data of the industrial equipment to be inspected, to obtain the modeling numerical data matrix of the industrial equipment to be inspected a 3 and the image data matrix of the industrial equipment to be inspected a 5; as follows,

[0084] ; ;

[0085] in, a 3ij , a 5ij Respectively represent i The first data of the industrial equipment to be inspected collected j types of modeled numerical data and modeled image data, Indicates the total number of times the modeling numerical data and modeling image data of the industrial equipment to be inspected are collected;

[0086] S13, calculating the average value of each type of modeling numerical data of the industrial equipment to be inspected collected multiple times according to the modeling numerical data matrix of the industrial equipment to be inspected, to obtain the average numerical data set of the modeling numerical data of the industrial equipment to be inspected , a 6j Indicates the first j The average value of the modeling numerical data of the types; the calculation formula is as follows,

[0087] ;

[0088] The modeling image data of each type of industrial equipment to be inspected collected each time in the modeling image data matrix of the industrial equipment to be inspected is screened; after the screening is completed, the modeling image data set of the industrial equipment to be inspected after the screening is obtained , a 7j Indicates the first j Types of modeling image data;

[0089] In S13, each type of modeling image data of industrial equipment to be inspected collected each time in the modeling image data matrix of industrial equipment to be inspected is screened; after the screening is completed, obtaining the screened modeling image data set of industrial equipment to be inspected includes the following steps:

[0090] S131, collecting multiple modeling image data and corresponding definition data of historical industrial equipment in conjunction with the industrial equipment modeling image data type set to obtain a historical industrial equipment modeling image data set and historical industrial equipment modeling image clarity dataset , , Respectively represent the collected i Historical industrial equipment modeling image data and corresponding image clarity data, Indicates the total amount of image definition data collected;

[0091] S132, using the historical industrial equipment modeling image clarity data set and the historical industrial equipment modeling image clarity data set to construct an image clarity mapping model; inputting the modeling image data in the industrial equipment modeling image data matrix to be inspected into the image clarity mapping model for mapping, and obtaining the industrial equipment modeling image clarity data matrix to be inspected a 8; as follows,

[0092] ;

[0093] in, a 8ij Indicates i The first data of the industrial equipment to be inspected collected j Types of clarity data for modeling image data; a 7j The calculation formula is as follows,

[0094] ;

[0095] In S132, using the historical industrial equipment modeling image clarity dataset and the historical industrial equipment modeling image clarity dataset to construct an image clarity mapping model includes the following steps:

[0096] S1321, constructing an initial CNN classification model and a first training data ratio; dividing the historical industrial equipment modeling image data set and the historical industrial equipment modeling image clarity data set according to the first training data ratio to obtain a historical industrial equipment modeling image training data set, a historical industrial equipment modeling image clarity training data set, a historical industrial equipment modeling image test data set, and a historical industrial equipment modeling image clarity test data set;

[0097] S1322, setting a first training error threshold; inputting the historical industrial equipment modeling image training data set and the historical industrial equipment modeling image clarity training data set as training data and training labels respectively into the initial CNN classification model for training; during the training process, when the training error is less than the first training error threshold, stopping the training to obtain a trained CNN classification model; otherwise, continuing the training until the training error is less than the first training error threshold;

[0098] S1323, set a first test accuracy threshold; input the historical industrial equipment modeling image test data set and the historical industrial equipment modeling image clarity test data set as test data and test labels respectively into the trained CNN classification model for testing; after the test is completed, obtain the first test accuracy data; when the first test accuracy data is greater than or equal to the first test accuracy threshold, use the trained CNN classification model as the image clarity mapping model; otherwise, return to S1322 to continue training the trained CNN classification model until the first test accuracy data is greater than or equal to the first test accuracy threshold;

[0099] S2. Perform three-dimensional modeling based on the average numerical data set of the modeling of the industrial equipment to be inspected and the image data set of the modeling of the industrial equipment to be inspected after screening to obtain a three-dimensional model of the industrial equipment to be inspected; construct a virtual scene for the operation of the three-dimensional model of the industrial equipment to be inspected to obtain an initial virtual scene of the industrial equipment to be inspected;

[0100] The S2 comprises the following steps:

[0101] S21, performing three-dimensional modeling of the industrial equipment to be inspected according to the average numerical data set of the modeling of the industrial equipment to be inspected and the screened image data set of the modeling of the industrial equipment to be inspected, to obtain a three-dimensional model of the industrial equipment to be inspected;

[0102] S22, setting several parameter types of virtual reality development platforms to obtain a virtual reality parameter type set; then setting several operating parameter types of the industrial equipment to be inspected to obtain an industrial equipment operating parameter type set; setting initial parameter data of the virtual reality development platform according to the virtual reality parameter type set to obtain a virtual reality initial parameter data set , b 1i The virtual reality development platform i Types of initial parameter data, Represents the total number of parameter types of the virtual reality development platform;

[0103] S23, importing the three-dimensional model of the industrial equipment to be inspected into the virtual reality development platform, and then collecting the current actual operating parameter data of the industrial equipment to be inspected according to the industrial equipment operating parameter type set to obtain a current actual operating parameter data set; setting the operating parameters of the industrial equipment to be inspected imported into the virtual scene according to the current actual operating parameter data set, and obtaining an initial virtual scene of the industrial equipment to be inspected after the setting is completed;

[0104] S3, optimizing the parameters of the initial virtual scene of industrial equipment to be inspected and the actual operating parameter data of the industrial equipment to be inspected in order to obtain a final virtual scene of industrial equipment to be inspected;

[0105] The S3 comprises the following steps:

[0106] S31, set multiple operation data collection time points to obtain an operation data collection time point set; collect the operation parameter data of the initial virtual scene of the industrial equipment to be inspected and the actual industrial equipment to be inspected according to the operation data collection time point set and the industrial equipment operation parameter type set, and obtain an initial virtual operation parameter data matrix And the actual operation parameter data matrix ; They are as follows,

[0107] ; ;

[0108] in, , Respectively indicate that in the setting i The initial virtual scene of the industrial equipment to be inspected collected at the first collection time point and the actual industrial equipment to be inspected j types of operating parameter data, Indicates the total number of set collection time points. Indicates the total number of the types of operating parameters of the industrial equipment to be inspected;

[0109] S32, calculating the Euclidean distance data between the initial virtual operation parameter data matrix and the actual operation parameter data matrix, and obtaining the operation parameter error data c ; The calculation formula is as follows,

[0110] ;

[0111] S33, setting an operation parameter error threshold; when the operation parameter error data is greater than or equal to the operation parameter error threshold, adjusting the virtual reality initial parameter data set until the operation parameter error data is less than the operation parameter error threshold, and obtaining the virtual reality final parameter data set , b 2i Indicates that the adjustment is made to obtain the first i otherwise, there is no need to adjust the virtual reality initial parameter data set, and the virtual reality initial parameter data set is used as the virtual reality final parameter data set;

[0112] In S33, the virtual reality initial parameter data set is adjusted until the operating parameter error data is less than the operating parameter error threshold, and obtaining the virtual reality final parameter data set includes the following steps:

[0113] S331, build virtual platform parameters to adjust Harris Hawk population , Indicates that the virtual platform parameters adjust the Harris Hawk population i Harris Hawk, Indicates that the virtual platform parameters adjust the size of the Harris Hawk population; Sets the maximum number of iterations of the virtual platform parameters to adjust the Harris Hawk population And the current number of iterations is , respectively recorded as the maximum number of iterations of virtual platform adjustment and the current number of iterations of virtual platform adjustment; the virtual platform parameters are set to adjust the search space dimension of Harris Hawk population to ;

[0114] S332: setting the value interval of each type of parameter data of the virtual reality development platform according to the virtual reality parameter type set, and obtaining a platform parameter value interval set d 1; as follows,

[0115] ;

[0116] in, , They represent the virtual reality development platform iThe lower and upper limits of the values ​​of the various types of parameter data;

[0117] The virtual platform parameters are set according to the platform parameter value interval set to adjust the initial position of each Harris hawk in the Harris hawk population to obtain a first initial position matrix ;as follows,

[0118] ;

[0119] in, Indicates that the virtual platform parameters are adjusted in the Harris Hawk population j The initial position of the Harris Hawk in the virtual reality development platform i The position component of the parameter data dimension of the type; the calculation formula is as follows,

[0120] ;

[0121] In the formula, rand 1ji Indicates for Generate a random number between 0 and 1;

[0122] S333, according to the operating parameter error data c Constructing virtual platform parameters to adjust the fitness function of Harris hawk population ;as follows,

[0123] ;

[0124] S334, start iteration, before iteration, set the virtual platform to adjust the current iteration number to 1; in the first round of iteration, use the virtual platform parameters to adjust the fitness function of the Harris hawk population Calculate the fitness value of the initial position of each Harris hawk in the first initial position matrix to obtain a first fitness value set; use the maximum fitness value in the first fitness value set and the corresponding initial position of the Harris hawk as the first global optimal fitness and the first global optimal position respectively; update the initial position of each Harris hawk in the first initial position matrix according to the first global optimal fitness and the first global optimal position; after the update is completed, adjust the current iteration number of the virtual platform by 1 and enter the next round of iteration;

[0125] In each other iteration, the virtual platform parameters are used to adjust the fitness function of the Harris hawk population. Calculate the fitness value of the virtual platform parameters updated in the previous iteration process to adjust the position of each Harris hawk in the Harris hawk population, and obtain a second fitness value set; use the maximum fitness value in the second fitness value set and the corresponding position of the Harris hawk as the second global optimal fitness and the second global optimal position respectively; update the position of each Harris hawk in the Harris hawk population adjusted by the virtual platform parameters updated in the previous iteration process according to the second global optimal fitness and the second global optimal position; after the update is completed, add 1 to the current iteration number of the virtual platform adjustment and enter the next iteration;

[0126] S335, when When , stop the iteration and get the first final global optimal position; otherwise, continue to iterate until until the time; using the first final global optimal position as a virtual reality optimized parameter data set; setting the initial virtual scene of the industrial equipment to be inspected according to the virtual reality optimized parameter data set to obtain an optimized virtual scene of the industrial equipment;

[0127] S336, respectively collecting the operating parameter data of the industrial equipment to be inspected in the optimized virtual scene of industrial equipment according to the operating data collection time point set and the industrial equipment operating parameter type set, to obtain an optimized virtual operating parameter data matrix; calculating the Euclidean distance data between the optimized virtual operating parameter data matrix and the actual operating parameter data matrix, to obtain optimized operating parameter error data;

[0128] When the optimized operating parameter error data is less than the operating parameter error threshold, the virtual reality optimized parameter data set is used as the virtual reality optimized parameter data set; otherwise, return to S334 and continue to iterate until the optimized operating parameter error data is less than the operating parameter error threshold;

[0129] The Harris Hawk optimization algorithm mainly includes two stages: the exploration stage and the development stage. The exploration stage simulates the Harris Hawk searching for prey in a wide area, and uses random search and global information to explore the solution space. The development stage simulates the Harris Hawk surrounding and attacking the prey after discovering it, and uses local information and group cooperation to optimize the solution. Therefore, the Harris Hawk optimization algorithm can effectively explore the entire solution space and avoid falling into the local optimal solution. In the development stage, it can effectively find the local optimal solution and improve the convergence speed and optimization accuracy of the algorithm. The algorithm has fewer parameters and is easy to debug and apply. Based on the above advantages, the Harris Hawk optimization algorithm is used in this scheme to iterate multiple parameters of the virtual reality development platform at the same time, and the error value between the operating parameter data of the initial virtual scene of the industrial equipment to be inspected and the actual industrial equipment to be inspected is used as the fitness function. Therefore, as the iteration proceeds, the error value between the operating parameter data of the initial virtual scene of the industrial equipment to be inspected and the actual industrial equipment to be inspected becomes smaller and smaller, and finally meets the requirement of real-time reflection of the operating parameter data of the actual industrial equipment to be inspected.

[0130] S34, setting the parameters of the initial virtual scene of the industrial equipment to be inspected according to the virtual reality final parameter data set, and after the setting is completed, obtaining the final virtual scene of the industrial equipment to be inspected;

[0131] S4, the construction module uses the historical operating parameter data of the industrial equipment to be inspected and the corresponding operating status data collected multiple times to construct an operating status mapping model; cooperates with the operating status mapping model to adjust the operating parameters of the industrial equipment to be inspected with abnormal operating status in the final virtual scene of the industrial equipment to be inspected, and obtains the operating parameter inspection reference data set of the industrial equipment to be inspected; remotely adjusts the operating parameters of the industrial equipment to be inspected according to the operating parameter inspection reference data set of the industrial equipment to be inspected;

[0132] The S4 comprises the following steps:

[0133] S41, according to the industrial equipment operation parameter type set, repeatedly collect historical operation parameter data of the industrial equipment to be inspected and the corresponding operation status data of the industrial equipment to be inspected, to obtain a historical operation parameter data matrix And historical operation status data set , Indicates i The historical operating status data of the industrial equipment to be inspected obtained by the collection; The value is 0 or 1. When the value is 0, it indicates that the operation status is abnormal, and when the value is 1, it indicates that the operation status is normal; e Indicates the total number of times the historical operating parameter data of the industrial equipment to be inspected and the corresponding operating status data of the industrial equipment to be inspected are collected; as follows,

[0134] ;

[0135] in, Indicates i The first collected data of the industrial equipment to be inspected j types of historical operating parameter data;

[0136] Constructing an operation status mapping model according to the historical operation parameter data matrix and the historical operation status data set;

[0137] In S41, constructing the operation status mapping model according to the historical operation parameter data matrix and the historical operation status data set includes the following steps:

[0138] S411, constructing an initial SVM model and a second training data ratio; dividing the historical operation parameter data matrix and the historical operation status data set according to the second training data ratio to obtain a historical operation parameter training data matrix, a historical operation status training data set, a historical operation parameter test data matrix, and a historical operation status test data set;

[0139] S412, setting a second training error threshold; inputting the historical operation parameter training data matrix and the historical operation status training data set as training data and training labels respectively into the initial SVM model for training; during the training process, when the training error is less than the second training error threshold, stopping the training to obtain a trained SVM model; otherwise, continuing the training until the training error is less than the second training error threshold;

[0140] S413, set a second test accuracy threshold; input the historical operating parameter test data matrix and the historical operating status test data set as test data and test labels respectively into the trained SVM model for testing; after the test is completed, obtain the second test accuracy data; when the second test accuracy data is greater than or equal to the second test accuracy threshold, use the trained SVM model as the operating status mapping model; otherwise, return to S412 to continue training the trained SVM model until the second test accuracy data is greater than or equal to the second test accuracy threshold;

[0141] S42, collecting the operating parameter data of the industrial equipment in the final virtual scene of the industrial equipment to be inspected in real time according to the industrial equipment operating parameter type set, to obtain a real-time operating parameter data set of the industrial equipment to be inspected; inputting the real-time operating parameter data set of the industrial equipment to be inspected into the operating status mapping model for mapping, to obtain the real-time operating status data of the industrial equipment to be inspected ;

[0142] When the real-time operating status data of the industrial equipment to be inspected is 0, the real-time operating parameter data set of the industrial equipment to be inspected is optimized in conjunction with the operating status mapping model until the real-time operating status data of the industrial equipment to be inspected is 1, and the inspection reference data set of the operating parameters of the industrial equipment to be inspected is obtained and displayed; otherwise, there is no need to optimize the real-time operating parameter data set of the industrial equipment to be inspected;

[0143] In S42, the real-time operating parameter data set of the industrial equipment to be inspected is optimized in conjunction with the operating status mapping model until the real-time operating status data of the industrial equipment to be inspected is 1, and obtaining the inspection reference data set of the operating parameters of the industrial equipment to be inspected includes the following steps:

[0144] S421, build operating parameters to adjust Harris Hawk population , Indicates that the operating parameters adjust the Harris Hawk population i Harris Hawk, Indicates that the operating parameters adjust the size of the Harris Hawk population; Set the maximum number of iterations for adjusting the Harris Hawk population by the operating parameters to And the current number of iterations is , respectively recorded as the maximum number of iterations of the operating parameter adjustment and the current number of iterations of the operating parameter adjustment; the search space dimension of the operating parameter adjustment Harris Hawk population is set to ;

[0145] S422: Set the operating parameter data value interval of each type of the industrial equipment to be inspected according to the industrial equipment operating parameter type set to obtain an operating parameter value interval set. d 2; as follows,

[0146] ;

[0147] in, , Respectively represent the first i The lower and upper limits of the values ​​of the various types of operating parameter data;

[0148] The operating parameters are set according to the operating parameter value interval set to adjust the initial position of each Harris hawk in the Harris hawk population to obtain a second initial position matrix ;as follows,

[0149] ;

[0150] in, Indicates that the operating parameters adjust the Harris Hawk population jThe initial position of the Harris Hawk is at the first i The position component of the operating parameter data dimension of the type; the calculation formula is as follows,

[0151] ;

[0152] In the formula, rand 2ji Indicates for Generate a random number between 0 and 1;

[0153] S423, based on the real-time operating status data of the industrial equipment to be inspected Constructing a fitness function for operating parameters to adjust the Harris Hawk population ;as follows,

[0154] ;

[0155] S424, start iteration, before iteration, adjust the current iteration number of the operating parameters to 1; in the first round of iteration, use the operating parameters to adjust the fitness function of the Harris hawk population And cooperate with the operation state mapping model to calculate the fitness value of the initial position of each Harris hawk in the second initial position matrix to obtain a third fitness value set; the maximum fitness value in the third fitness value set and the corresponding initial position of the Harris hawk are respectively used as the third global optimal fitness and the third global optimal position; according to the third global optimal fitness and the third global optimal position, the initial position of each Harris hawk in the second initial position matrix is ​​updated; after the update is completed, the operation parameter is adjusted to increase the current iteration number by 1 and enter the next round of iteration;

[0156] In each other iteration, the operating parameters are used to adjust the fitness function of the Harris Hawk population. And cooperate with the operation state mapping model to calculate the fitness value of the position of each Harris hawk in the Harris hawk population adjusted by the operation parameters updated in the previous round of iterations, and obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding position of the Harris hawk are respectively used as the fourth global optimal fitness and the fourth global optimal position; according to the fourth global optimal fitness and the fourth global optimal position, the position of each Harris hawk in the Harris hawk population adjusted by the operation parameters updated in the previous round of iterations is updated; after the update is completed, the current iteration number of the operation parameter adjustment is increased by 1 and the next iteration is entered;

[0157] S425, when When , stop the iteration and get the second final global optimal position; otherwise, continue to iterate until until the time; taking the second final global optimal position as the optimized real-time operating parameter data set of the industrial equipment to be inspected; inputting the optimized real-time operating parameter data set of the industrial equipment to be inspected into the operating state mapping model for mapping, and obtaining the optimized real-time operating state data of the industrial equipment to be inspected;

[0158] When the optimized real-time operating status data of the industrial equipment to be inspected is 1, the optimized real-time operating status data of the industrial equipment to be inspected is used as a reference data set for inspection of operating parameters of the industrial equipment to be inspected; otherwise, return to S424 and continue to iterate until the optimized real-time operating status data of the industrial equipment to be inspected is 1;

[0159] S43. The industrial equipment operation and maintenance personnel remotely adjust the operating parameters of the industrial equipment to be inspected according to the inspection reference data set of the operating parameters of the industrial equipment to be inspected;

[0160] Embodiment 2

[0161] This embodiment discloses a remote inspection simulation system for industrial equipment operation and maintenance based on virtual reality. The system can implement the method of the above embodiment, including an industrial equipment modeling data type setting module, a modeling data acquisition and calculation module, a three-dimensional modeling module, a virtual scene construction module, a virtual scene parameter optimization module, an operation status mapping model, an operation parameter adjustment module for industrial equipment to be inspected, and a remote operation and maintenance module;

[0162] The industrial equipment modeling data type setting module is used to set the numerical data type and image data type required for three-dimensional modeling of the industrial equipment to be inspected, and obtain an industrial equipment modeling numerical data type set and an industrial equipment modeling image data type set;

[0163] The modeling data acquisition and calculation module is used to collect the modeling data of the industrial equipment to be inspected according to the industrial equipment modeling numerical data type set and the industrial equipment modeling image data type set, and perform calculation and screening to obtain the average numerical data set of the industrial equipment modeling to be inspected and the screened industrial equipment modeling image data set;

[0164] The three-dimensional modeling module is used to perform three-dimensional modeling based on the average numerical data set of the industrial equipment to be inspected and the image data set of the industrial equipment to be inspected after screening, so as to obtain a three-dimensional model of the industrial equipment to be inspected;

[0165] The virtual scene construction module is used to construct a virtual scene for the three-dimensional model of the industrial equipment to be inspected to run, and obtain an initial virtual scene of the industrial equipment to be inspected;

[0166] The virtual scene parameter optimization module is used to optimize the parameters of the initial virtual scene of the industrial equipment to be inspected according to the operating parameter data of the industrial equipment to be inspected in the initial virtual scene of the industrial equipment to be inspected and in reality, so as to obtain the final virtual scene of the industrial equipment to be inspected;

[0167] The operation status mapping model building module is used to construct an operation status mapping model using the historical operation parameter data of the industrial equipment to be inspected and the corresponding operation status data collected multiple times;

[0168] The operating parameter adjustment module of the industrial equipment to be inspected is used to cooperate with the operating status mapping model to adjust the operating parameters of the industrial equipment to be inspected with abnormal operating status in the final virtual scene of the industrial equipment to be inspected, so as to obtain an inspection reference data set of the operating parameters of the industrial equipment to be inspected;

[0169] The remote operation and maintenance module is used to remotely adjust and operate the operating parameters of the industrial equipment to be inspected according to the inspection reference data set of the operating parameters of the industrial equipment to be inspected.

[0170] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0171] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A virtual reality-based remote inspection simulation method for industrial equipment operation and maintenance, characterized in that: The following steps are involved: S1. Collect modeling data of industrial equipment to be inspected, calculate and screen, and obtain an average numerical data set of modeling of industrial equipment to be inspected and a screened image data set of modeling of industrial equipment to be inspected; S2. Perform three-dimensional modeling based on the average numerical data set of the modeling of the industrial equipment to be inspected and the image data set of the modeling of the industrial equipment to be inspected after screening to obtain a three-dimensional model of the industrial equipment to be inspected; construct a virtual scene for the operation of the three-dimensional model of the industrial equipment to be inspected to obtain an initial virtual scene of the industrial equipment to be inspected; S3, optimizing the parameters of the initial virtual scene of industrial equipment to be inspected and the actual operating parameter data of the industrial equipment to be inspected in order to obtain a final virtual scene of industrial equipment to be inspected; S4, the construction module uses the historical operating parameter data of the industrial equipment to be inspected and the corresponding operating status data collected multiple times to construct an operating status mapping model; Cooperating with the operation status mapping model, the operation parameters of the industrial equipment to be inspected with abnormal operation status in the final virtual scene of the industrial equipment to be inspected are adjusted to obtain an inspection reference data set of the operation parameters of the industrial equipment to be inspected; and the operation parameters of the industrial equipment to be inspected are remotely adjusted and maintained according to the inspection reference data set of the operation parameters of the industrial equipment to be inspected; The S3 comprises the following steps: S31, collecting the operating parameter data of the initial virtual scene of the industrial equipment to be inspected and the actual industrial equipment to be inspected according to the industrial equipment operating parameter type set, to obtain an initial virtual operating parameter data matrix and an actual operating parameter data matrix; S32, calculating the Euclidean distance data between the initial virtual operation parameter data matrix and the actual operation parameter data matrix to obtain operation parameter error data; S33, setting an operation parameter error threshold; when the operation parameter error data is greater than or equal to the operation parameter error threshold, adjusting the virtual reality initial parameter data set until the operation parameter error data is less than the operation parameter error threshold, thereby obtaining a virtual reality final parameter data set; otherwise, there is no need to adjust the virtual reality initial parameter data set, and the virtual reality initial parameter data set is used as the virtual reality final parameter data set; S34. Setting parameters of the initial virtual scene of the industrial equipment to be inspected according to the virtual reality final parameter data set, and after the setting is completed, obtaining the final virtual scene of the industrial equipment to be inspected.

2. The virtual reality-based industrial equipment operation and maintenance remote inspection simulation method according to claim 1 is characterized in that: The S1 comprises the following steps: S11, collecting modeling numerical data and modeling image data of the industrial equipment to be inspected for multiple times to obtain a modeling numerical data matrix and a modeling image data matrix of the industrial equipment to be inspected; S12, calculating the average value of each type of modeling numerical data of the industrial equipment to be inspected collected multiple times according to the modeling numerical data matrix of the industrial equipment to be inspected, to obtain an average numerical data set of the modeling numerical data of the industrial equipment to be inspected; Each type of modeling image data of industrial equipment to be inspected collected each time in the modeling image data matrix of industrial equipment to be inspected is screened; after the screening is completed, a screened modeling image data set of industrial equipment to be inspected is obtained.

3. The virtual reality-based industrial equipment operation and maintenance remote inspection simulation method according to claim 2 is characterized in that: In S12, each type of modeling image data of industrial equipment to be inspected collected each time in the modeling image data matrix of industrial equipment to be inspected is screened; after the screening is completed, obtaining the screened modeling image data set of industrial equipment to be inspected includes the following steps: S121, collecting multiple modeling image data and corresponding clarity data of historical industrial equipment in conjunction with the industrial equipment modeling image data type set to obtain a historical industrial equipment modeling image data set and a historical industrial equipment modeling image clarity data set; S122. Use the historical industrial equipment modeling image clarity data set and the historical industrial equipment modeling image clarity data set to construct an image clarity mapping model; input the modeling image data in the industrial equipment modeling image data matrix to be inspected into the image clarity mapping model for mapping, and obtain the industrial equipment modeling image clarity data matrix to be inspected.

4. The virtual reality-based industrial equipment operation and maintenance remote inspection simulation method according to claim 3 is characterized in that: The S2 comprises the following steps: S21, performing three-dimensional modeling of the industrial equipment to be inspected according to the average numerical data set of the modeling of the industrial equipment to be inspected and the image data set of the modeling of the industrial equipment to be inspected after screening, to obtain a three-dimensional model of the industrial equipment to be inspected; S22, setting several parameter types of virtual reality development platforms to obtain a virtual reality parameter type set; then setting several operating parameter types of the industrial equipment to be inspected to obtain an industrial equipment operating parameter type set; setting initial parameter data of the virtual reality development platform according to the virtual reality parameter type set to obtain a virtual reality initial parameter data set; S23. Import the three-dimensional model of the industrial equipment to be inspected into the virtual reality development platform, and then collect the current actual operating parameter data of the industrial equipment to be inspected according to the industrial equipment operating parameter type set to obtain a current actual operating parameter data set; set the operating parameters of the industrial equipment to be inspected imported into the virtual scene according to the current actual operating parameter data set, and obtain the initial virtual scene of the industrial equipment to be inspected after the setting is completed.

5. The virtual reality-based industrial equipment operation and maintenance remote inspection simulation method according to claim 4 is characterized in that: In S33, the virtual reality initial parameter data set is adjusted using the Harris Eagle optimization algorithm.

6. The virtual reality-based industrial equipment operation and maintenance remote inspection simulation method according to claim 5 is characterized in that: The S4 comprises the following steps: S41, collecting historical operating parameter data of the industrial equipment to be inspected and corresponding operating status data of the industrial equipment to be inspected for multiple times according to the industrial equipment operating parameter type set and constructing an operating status mapping model; S42, collecting the operating parameter data of the industrial equipment in the final virtual scene of the industrial equipment to be inspected in real time according to the industrial equipment operating parameter type set and inputting it into the operating status mapping model for mapping, so as to obtain the real-time operating status data of the industrial equipment to be inspected; optimizing the real-time operating parameter data set of the industrial equipment to be inspected according to the real-time operating status data of the industrial equipment to be inspected, obtaining the inspection reference data set of the operating parameters of the industrial equipment to be inspected and displaying it; S43. The industrial equipment operation and maintenance personnel remotely adjust the operating parameters of the industrial equipment to be inspected according to the inspection reference data set of the operating parameters of the industrial equipment to be inspected.

7. The virtual reality-based industrial equipment operation and maintenance remote inspection simulation method according to claim 6 is characterized by: The operation status mapping model adopts the SVM model.

8. The virtual reality-based industrial equipment operation and maintenance remote inspection simulation method according to claim 7 is characterized in that: In S42, optimizing the real-time operating parameter data set of the industrial equipment to be inspected according to the real-time operating status data of the industrial equipment to be inspected to obtain the inspection reference data set of the operating parameters of the industrial equipment to be inspected includes the following steps: S421, constructing an operating parameter to adjust the Harris Hawk population; setting the maximum number of iterations of the operating parameter to adjust the Harris Hawk population is And the current number of iterations is , respectively recorded as the maximum number of iterations of operating parameter adjustment and the current number of iterations of operating parameter adjustment; S422, setting operating parameters to adjust the initial position of each Harris hawk in the Harris hawk population to obtain a second initial position matrix; S423, constructing a fitness function for adjusting the Harris Hawk population by operating parameters according to the real-time operating status data of the industrial equipment to be inspected; S424, start iteration, and before iteration, set the current iteration number of the operating parameter adjustment to 1; in each round of iteration, use the operating parameter to adjust the fitness function of the Harris hawk population and cooperate with the operating state mapping model to calculate the fitness value of the position of each Harris hawk in the Harris hawk population updated in the previous round of iteration, and update the position of each Harris hawk in the Harris hawk population updated in the previous round of iteration; S425, when When , stop the iteration and get the second final global optimal position; otherwise, continue to iterate until until the time; taking the second final global optimal position as the optimized real-time operating parameter data set of the industrial equipment to be inspected; inputting the optimized real-time operating parameter data set of the industrial equipment to be inspected into the operating state mapping model for mapping, and obtaining the optimized real-time operating state data of the industrial equipment to be inspected; When the real-time operating status data of the industrial equipment to be inspected after optimization is 1, the real-time operating status data of the industrial equipment to be inspected after optimization is used as a reference data set for inspection of operating parameters of the industrial equipment to be inspected; otherwise, return to S424 and continue to iterate until the real-time operating status data of the industrial equipment to be inspected after optimization is 1.

9. A system for implementing the virtual reality-based industrial equipment operation and maintenance remote inspection simulation method as described in any one of claims 1 to 8, characterized in that: It includes industrial equipment modeling data type setting module, modeling data acquisition and calculation module, 3D modeling module, virtual scene construction module, virtual scene parameter optimization module, operation status mapping model, operation parameter adjustment module of industrial equipment to be inspected and remote operation and maintenance module; The industrial equipment modeling data type setting module is used to set the numerical data type and image data type required for three-dimensional modeling of the industrial equipment to be inspected, and obtain an industrial equipment modeling numerical data type set and an industrial equipment modeling image data type set; The modeling data acquisition and calculation module is used to collect the modeling data of the industrial equipment to be inspected according to the industrial equipment modeling numerical data type set and the industrial equipment modeling image data type set, and perform calculation and screening to obtain the average numerical data set of the industrial equipment modeling to be inspected and the screened industrial equipment modeling image data set; The three-dimensional modeling module is used to perform three-dimensional modeling based on the average numerical data set of the industrial equipment to be inspected and the image data set of the industrial equipment to be inspected after screening, so as to obtain a three-dimensional model of the industrial equipment to be inspected; The virtual scene construction module is used to construct a virtual scene for the three-dimensional model of the industrial equipment to be inspected to run, and obtain an initial virtual scene of the industrial equipment to be inspected; The virtual scene parameter optimization module is used to optimize the parameters of the initial virtual scene of the industrial equipment to be inspected according to the operating parameter data of the industrial equipment to be inspected in the initial virtual scene of the industrial equipment to be inspected and in reality, so as to obtain the final virtual scene of the industrial equipment to be inspected; The operation status mapping model building module is used to construct an operation status mapping model using the historical operation parameter data of the industrial equipment to be inspected and the corresponding operation status data collected multiple times; The operating parameter adjustment module of the industrial equipment to be inspected is used to cooperate with the operating status mapping model to adjust the operating parameters of the industrial equipment to be inspected with abnormal operating status in the final virtual scene of the industrial equipment to be inspected, so as to obtain an inspection reference data set of the operating parameters of the industrial equipment to be inspected; The remote operation and maintenance module is used to remotely adjust and operate the operating parameters of the industrial equipment to be inspected according to the inspection reference data set of the operating parameters of the industrial equipment to be inspected.

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