Power equipment fixing method and system based on parametric design of anchor bolts

Abstract

The present invention discloses a method and system for fixing electric equipment based on parametric design of anchor bolts, which specifically relates to the field of power transmission technology, including collecting parameter training data of anchor bolts in historical installation scenarios of electric equipment, wherein the parameter training data includes parameter feature data and corresponding key design data; based on the parameter training data, training a bolt parameter design model for predicting the key design data of initial anchor bolts; importing the key design data of the initial anchor bolts predicted by the bolt parameter design model into three-dimensional software to construct n bolt three-dimensional models; the present invention is conducive to improving design efficiency, accuracy and reliability, while significantly saving costs, and meeting the different needs of different electric equipment, reducing the probability of damage to electric equipment due to quality problems of anchor bolts, and helping to improve the safety and reliability of equipment.

Description

Technical Field

[0001] The present invention relates to the field of power transmission technology, and more specifically, to a method and system for fixing power equipment based on parametric design of anchor bolts. Background Art

[0002] Power equipment includes transformers, transmission towers, and distribution boxes. The safety and stability of power equipment largely depends on the design of anchor bolts. Traditional anchor bolt design methods often rely on empirical formulas and standard specifications, which are difficult to adapt to complex and changeable actual working conditions. Parametric design methods can improve design efficiency and accuracy, but how to effectively determine the relationship between parameters and optimize design results remains a challenge. The existing parametric design method introduces the concept of adjustment coefficients based on the distribution position of bolt forces, takes into account the influence of uneven force on anchor bolts, and considers the influence of stress concentration near the bolt holes. For example, a Chinese patent application with publication number CN107977525A discloses a method for calculating the design thickness of a tower base plate with twelve anchor bolts. Although the above method can solve the problem of uneven force on anchor bolts, the above method and the prior art have at least the following defects:

[0003] For a transformer and a transmission tower, the specific designs of their anchor bolts in terms of size, material and quantity will be very different. The above scheme only designs the anchor bolts for the transmission tower, and does not design them according to the detailed power equipment parameters and specific environmental conditions. There is a lack of precision in the design of the anchor bolts. In the process of using the anchor bolts to fix the power equipment, the anchor bolts may break due to failure to bear the load well, thereby causing damage to the power equipment.

[0004] To this end, the present invention provides a method and system for fixing electric equipment based on parametric design of anchor bolts. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for fixing electric equipment based on parametric design of anchor bolts to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, in a first aspect, the present invention provides the following technical solutions: a method for fixing electric equipment based on parametric design of anchor bolts, comprising:

[0007] Step 1: Collect parameter training data of anchor bolts in historical installation scenarios of power equipment, where the parameter training data includes parameter feature data and corresponding key design data;

[0008] Step 2: Based on the parameter training data, a bolt parameter design model for predicting key design data of the initial anchor bolt is trained; the key design data of the initial anchor bolt predicted by the bolt parameter design model is imported into the 3D software to construct n bolt 3D models;

[0009] Step 3: Obtain load data of n three-dimensional models of bolts, and calculate the load evaluation coefficient of the nth three-dimensional model of bolts according to the load data;

[0010] Step 4: According to the load evaluation coefficient, the three-dimensional models of n bolts are judged, the judgment results are obtained, and the bolt optimization strategy is generated according to the judgment results; the judgment results include a normal bolt model and an abnormal bolt model;

[0011] Step 5: Based on the bolt optimization strategy, parameter design optimization is performed on the corresponding key design data in the abnormal bolt model to obtain an optimized bolt model.

[0012] Step 6: Use the anchor bolts produced by the optimized bolt model to fix the power equipment.

[0013] Furthermore, the parameter characteristic data includes equipment basic data of the power equipment and installation environment data of the anchor bolts collected in historical installation scenarios; wherein the equipment basic data includes the type, weight and size of the power equipment; the installation environment data includes the mean salinity, mean humidity and mean temperature of the power equipment installation environment;

[0014] The key design data in the parameter training data include bolt diameter, strength and quantity; the key design data is recorded and obtained by continuously adjusting the bolt diameter, strength and quantity when the power equipment is installed and tested until the power equipment is installed stably.

[0015] Furthermore, the three-dimensional software is AutoCAD, SolidWorks or Fusion 360;

[0016] The method of training a bolt parameter design model for predicting key design data of an initial anchor bolt includes:

[0017] Each set of parameter feature data is converted into a feature vector, and all elements of the feature vector are used as inputs of a bolt parameter design model. The bolt parameter design model uses the bolt diameter, strength and quantity predicted for each set of parameter feature data as outputs, and uses the actual bolt diameter, strength and quantity corresponding to each set of parameter feature data as prediction targets, and uses minimizing the sum of prediction accuracies of all predicted bolt diameters, strengths and quantities as a training target; the bolt parameter design model is trained until the sum of prediction accuracies converges, and the training is stopped; the bolt parameter design model is specifically a deep neural network model or a deep belief network model.

[0018] Furthermore, the load data includes bearing capacity and total load value; the bearing capacity is the maximum force borne by the three-dimensional model of the bolt, and the bearing capacity is calculated according to the yield strength and cross-sectional area of ​​the three-dimensional model of the bolt. The calculation formula of the bearing capacity is:

[0019] , ;

[0020] In the formula, is the bearing capacity of the initial anchor bolts, The yield strength of, H is the cross-sectional area, is the circumference of a circle, is the diameter of the initial anchor bolt;

[0021] The total load value is the sum of all forces that the bolt three-dimensional model needs to withstand. The total load value includes static load and dynamic load. The static load is the weight of the power equipment itself and the weight of its accessories, and the dynamic load includes wind load and seismic load.

[0022] Furthermore, the method for calculating the load evaluation coefficient of the bolt three-dimensional model according to the load data includes:

[0023] The load capacity and total load values ​​in the load data are formulated to obtain the load assessment factor , and its calculation formula is:

[0024] ;

[0025] In the formula, represents the total load value, As represents the safety factor, and the safety factor is 1.5-2.0.

[0026] Further, the judgment result includes a normal bolt model and an abnormal bolt model;

[0027] According to the load evaluation coefficient, the method for judging the three-dimensional model of n bolts includes:

[0028] Preset coefficient threshold and , < , comparing the load assessment coefficient with a preset coefficient threshold;

[0029] like or < , then the nth bolt three-dimensional model is judged to be an abnormal bolt model;

[0030] like ≤ ≤ , then the nth bolt three-dimensional model is judged to be a normal bolt model;

[0031] The number of abnormal bolt models is counted to obtain S abnormal bolt models.

[0032] Furthermore, the method of obtaining the judgment result and generating the bolt optimization strategy according to the judgment result includes:

[0033] Acquire current optimization parameters of the abnormal bolt model according to the judgment result, wherein the optimization parameters include bolt diameter and strength;

[0034] The current optimization parameters of the abnormal bolt model are input into the pre-built simulation experiment model for simulation to generate the bolt optimization strategy.

[0035] Furthermore, the current optimization parameters of the abnormal bolt model are input into the pre-built simulation experiment model for simulation, and the method for generating the bolt optimization strategy includes:

[0036] Step a1. Obtain the current number S of abnormal bolt models, take the bolt diameter of the bolt diameter and the strength as a fixed quantity, take the strength as a variable, and take the current optimization parameter of the strength as Q;

[0037] Step a2. Let S=S+1, Q=Q+E, and record the bolt deformation coefficient under the optimized value Q, where E is an integer greater than zero;

[0038] Step a3. Repeat step a2. When Q is equal to the preset first optimization threshold, or S is equal to the preset number of abnormal bolt models, obtain K bolt deformation coefficients under the first optimization parameter, and jump to step a4, where K is an integer greater than zero;

[0039] Step a4. Take the strength of the bolt diameter and strength as a fixed quantity, the bolt diameter as a variable, and take the current optimization parameter of the bolt diameter as G;

[0040] Step a5. Reset Q, set G=G+F, S=S+1, and record the bolt deformation coefficient under the optimized value G, where F is an integer greater than zero;

[0041] Step a6. Repeat step a5, when G is equal to the preset second optimization threshold, or S is equal to the preset number of abnormal bolt models, obtain Z bolt deformation coefficients under the second optimization parameter, where Z is an integer greater than zero;

[0042] Step a7. fusion statistics of K bolt deformation coefficients under the first optimization parameter and Z bolt deformation coefficients under the second optimization parameter to obtain L bolt deformation coefficients, and sort the L bolt deformation coefficients from small to large values;

[0043] Step a8. Taking the optimized value of the bolt diameter and the optimized value of the strength of the anchor bolts corresponding to the first sort as the bolt optimization strategy.

[0044] Furthermore, the bolt deformation coefficient generation method is: obtaining deformation data of S abnormal bolt models under the optimization value Q, and performing formula calculation on the deformation data; the deformation data includes the load assessment coefficient, the bolt elongation and the original length of the bolt;

[0045] The calculation formula of the bolt deformation coefficient is:

[0046] ;

[0047] Where: is the bolt deformation coefficient, is the weight factor of the load assessment coefficient, is the bolt elongation, is the original length of the bolt.

[0048] In a second aspect, the present invention provides a power equipment fixing system based on parametric design of anchor bolts, which is used to implement the above-mentioned power equipment fixing method based on parametric design of anchor bolts, comprising:

[0049] A parameter collection module, used to collect parameter training data of anchor bolts in historical installation scenarios of power equipment, wherein the parameter training data includes parameter feature data and corresponding key design data;

[0050] The model training module trains a bolt parameter design model for predicting key design data of the initial anchor bolts based on the parameter training data; the key design data of the initial anchor bolts predicted by the bolt parameter design model are imported into the 3D software to construct n bolt 3D models;

[0051] A load evaluation module, used to obtain load data of n three-dimensional models of bolts, and calculate a load evaluation coefficient of the nth three-dimensional model of bolts according to the load data;

[0052] A judgment module, used to judge the n three-dimensional models of bolts according to the load evaluation coefficient, obtain the judgment result, and generate a bolt optimization strategy according to the judgment result; the judgment result includes a normal bolt model and an abnormal bolt model;

[0053] The design optimization module performs parameter design optimization on the corresponding key design data in the abnormal bolt model based on the bolt optimization strategy to obtain the optimized bolt model.

[0054] Application module, using the anchor bolts produced by the optimized bolt model to fix the power equipment.

[0055] Technical effects and advantages of the present invention:

[0056] The present invention trains a bolt parameter design model by collecting parameter training data of anchor bolts, so that the model can accurately predict the key design data of the initial anchor bolts and improve the scientific nature of the design; by acquiring and calculating the load data of the bolt three-dimensional model, the load bearing capacity of different design schemes is evaluated to ensure the rationality of the anchor bolt design; according to the judgment result of the bolt three-dimensional model, a bolt optimization strategy is generated and implemented, and parametric design optimization is performed on abnormal bolt models, which is beneficial to improving design efficiency, accuracy and reliability, can significantly save costs, and meet the different needs of different power equipment, reduce the probability of power equipment being damaged due to quality problems of anchor bolts, and help improve the safety and reliability of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a method for fixing electric equipment based on parametric design of anchor bolts according to Example 1;

[0058] Figure 2 It is a structural schematic diagram of the power equipment fixing system based on the parametric design of anchor bolts according to Example 2;

[0059] Figure 3 This is a schematic diagram of an electronic device according to Embodiment 3;

[0060] Figure 4 A schematic diagram of a computer-readable storage medium of Example 4. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and a similar second unit may be referred to as a first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0064] Example 1

[0065] See also Figure 1 As shown, this embodiment discloses a method for fixing electric equipment based on parametric design of anchor bolts, including:

[0066] Step 1: Collect parameter training data of anchor bolts in historical installation scenarios of power equipment, where the parameter training data includes parameter feature data and corresponding key design data;

[0067] Specifically, the parameter characteristic data includes equipment basic data of the power equipment and installation environment data of the anchor bolts collected in historical installation scenarios; wherein the equipment basic data includes the type, weight and size of the power equipment; the installation environment data includes the mean salinity, mean humidity and mean temperature of the installation environment of the power equipment;

[0068] It should be noted that the types of power equipment include but are not limited to transformers, power towers, and distribution boxes. For the safety and stability of power equipment installation, power equipment is fixed by anchor bolts. Different types of power equipment have different requirements for anchor bolts. For example, a transformer requires four anchor bolts, a power tower requires eight anchor bolts, and a switchgear requires four anchor bolts.

[0069] The weight is the total weight of the power equipment. The total weight of the power equipment determines the load that the anchor bolts need to bear, which in turn affects the number of anchor bolts.

[0070] The dimensions are the length, width and height of the power equipment; the dimensions determine the number of anchor bolts required for the power equipment.

[0071] The power equipment type, weight and size of the equipment basic data are pre-stored in the system database, and the weight and size corresponding to the power equipment are automatically derived after the user selects the power equipment type.

[0072] It should be noted that: the salinity mean is the long-term average salt spray concentration of the power equipment installation environment. The historical salt spray concentration data is obtained through the online meteorological database, and the average value is calculated; the humidity mean is the long-term average humidity value of the power equipment installation environment. The historical humidity data is obtained through the online meteorological database, and the average value is calculated; the method for obtaining the temperature mean is the same as the method for obtaining the humidity mean, which is not described in detail. By considering the use environment of the anchor bolts through environmental factors such as the salinity mean, humidity mean, and temperature mean, the strength of the anchor bolts is reduced by the influence of environmental factors.

[0073] The key design data in the parameter training data include bolt diameter, strength and quantity; the key design data is obtained by continuously adjusting the bolt diameter, strength and quantity when the power equipment is installed and tested until the power equipment is installed stably; specifically, the bolt diameter is obtained by measuring the diameter of the anchor bolt with a caliper or a micrometer; the strength is obtained by using a universal material testing machine or by referring to the mechanical properties data of the anchor bolt material.

[0074] Step 2: Based on the parameter training data, a bolt parameter design model for predicting key design data of the initial anchor bolt is trained; the key design data of the initial anchor bolt predicted by the bolt parameter design model is imported into the 3D software to construct n bolt 3D models;

[0075] It should be noted that the 3D software includes but is not limited to AutoCAD, SolidWorks and Fusion 360.

[0076] In implementation, the method for training a bolt parameter design model for predicting key design data of an initial anchor bolt includes:

[0077] Each set of parameter feature data is converted into a feature vector, and all elements of the feature vector are used as inputs of the bolt parameter design model. The bolt parameter design model uses the bolt diameter, strength and quantity predicted for each set of parameter feature data as outputs, the actual bolt diameter, strength and quantity corresponding to each set of parameter feature data as prediction targets, and minimizing the sum of the prediction accuracies of all predicted bolt diameters, strengths and quantities as the training target; wherein the calculation formula for prediction accuracy is: , where a is the number of each set of parameter characteristic data, For prediction accuracy, For the The predicted bolt diameter, strength and number corresponding to the set of parameter characteristic data, For the The actual bolt diameter, strength and quantity corresponding to the group parameter feature data; the bolt parameter design model is trained until the sum of the prediction accuracies reaches convergence and the training is stopped; the bolt parameter design model is specifically a deep neural network model or a deep belief network model.

[0078] Step 3: Obtain load data of n three-dimensional models of bolts, and calculate the load evaluation coefficient of the nth three-dimensional model of bolts according to the load data;

[0079] It should be noted that: the load data includes bearing capacity and total load value; the bearing capacity is the maximum force borne by the three-dimensional model of the bolt, and the bearing capacity is calculated based on the yield strength and cross-sectional area of ​​the three-dimensional model of the bolt. The calculation formula of the bearing capacity is:

[0080] , ;

[0081] In the formula, is the bearing capacity of the initial anchor bolts, The yield strength of, H is the cross-sectional area, is the circumference of a circle, is the diameter of the initial anchor bolt.

[0082] The total load value includes the sum of all forces that the bolt three-dimensional model needs to bear, and the total load value includes static load and dynamic load. The static load is the weight of the power equipment itself and the weight of its accessories. The static load is obtained through the technical specifications provided by the equipment manufacturer, or the weight of the equipment is directly measured by a measuring tool. The dynamic load includes but is not limited to wind load and seismic load. The wind load is obtained through the meteorological department, and the seismic load is obtained through the seismic department.

[0083] In implementation, the method for calculating the load evaluation coefficient of the bolt three-dimensional model according to the load data includes:

[0084] The load capacity and total load values ​​in the load data are formulated to obtain the load assessment factor , and its calculation formula is:

[0085] ;

[0086] In the formula, represents the total load value, As represents the safety factor, and the safety factor is 1.5-2.0.

[0087] It should be noted that in order to ensure the safety of electrical equipment, a safety factor is added during the design to cope with potential overload conditions of the bolt three-dimensional model.

[0088] Step 4: According to the load evaluation coefficient, the three-dimensional models of n bolts are judged, the judgment results are obtained, and the bolt optimization strategy is generated according to the judgment results;

[0089] It should be noted that: the judgment result includes a normal bolt model and an abnormal bolt model.

[0090] In implementation, the method for judging the three-dimensional model of n bolts according to the load evaluation coefficient includes:

[0091] Preset coefficient threshold and , < , comparing the load assessment coefficient with a preset coefficient threshold;

[0092] like or < , then the nth bolt three-dimensional model is judged to be an abnormal bolt model;

[0093] like ≤ ≤ , then the nth bolt three-dimensional model is judged to be a normal bolt model;

[0094] The number of abnormal bolt models is counted to obtain S abnormal bolt models.

[0095] It should be noted that when evaluating the load performance of n three-dimensional bolt models, it was found that multiple bolt models were abnormal (such as deformation). In order to improve the overall stability of the power equipment, these abnormal bolt models need to be optimized.

[0096] In implementation, the method of obtaining the judgment result and generating the bolt optimization strategy according to the judgment result includes:

[0097] Acquire current optimization parameters of the abnormal bolt model according to the judgment result, wherein the optimization parameters include bolt diameter and strength;

[0098] The current optimization parameters of the abnormal bolt model are input into the pre-built simulation experiment model for simulation to generate the bolt optimization strategy.

[0099] It should be noted that the pre-built simulation experiment model is specifically a virtual simulation model of anchor bolts, which is generated based on various historical measured data of industrial production workshops, and performs real-time data updates and model updates based on a number of sensors. The various historical measured data include but are not limited to geometric data, equipment operation parameter data, object structure data, etc.; the pre-built simulation experiment model is implemented based on existing digital twin construction technology, such as 3DsMax, etc. Therefore, the present invention will not go into details about this.

[0100] Specifically, the current optimization parameters of the abnormal bolt model are input into a pre-built simulation experiment model for simulation, and the method for generating the bolt optimization strategy includes:

[0101] Step a1. Obtain the current number S of abnormal bolt models, take the bolt diameter of the bolt diameter and the strength as a fixed quantity, take the strength as a variable, and take the current optimization parameter of the strength as Q;

[0102] Step a2. Let S=S+1, Q=Q+E, and record the bolt deformation coefficient under the optimized value Q, where E is an integer greater than zero;

[0103] The bolt deformation coefficient generation method is as follows: the deformation data of S abnormal bolt models under the optimization value Q are obtained, and the deformation data are obtained by formula calculation; the deformation data includes the load evaluation coefficient, the bolt elongation and the original length of the bolt;

[0104] The calculation formula of the bolt deformation coefficient is:

[0105] ;

[0106] Where: is the bolt deformation coefficient, is the weight factor of the load assessment coefficient, is the bolt elongation, is the original length of the bolt.

[0107] It should be noted that: The larger the value, the more significant the bolt response under this load condition. The larger the value, the longer the bolt is under no-load conditions, and the larger the value of the bolt deformation coefficient; on the contrary, The larger the value, the greater the deformation of the bolt under load (reflecting the flexible response of the bolt). The larger it is, the larger the cross-sectional area of ​​the bolt is, and thus the smaller the value of the bolt deformation coefficient is.

[0108] Step a3. Repeat step a2. When Q is equal to the preset first optimization threshold, or S is equal to the preset number of abnormal bolt models, obtain K bolt deformation coefficients under the first optimization parameter, and jump to step a4, where K is an integer greater than zero;

[0109] Step a4. Take the strength of the bolt diameter and strength as a fixed quantity, the bolt diameter as a variable, and take the current optimization parameter of the bolt diameter as G;

[0110] Step a5. Reset Q, set G=G+F, S=S+1, and record the bolt deformation coefficient under the optimized value G, where F is an integer greater than zero;

[0111] Step a6. Repeat step a5, when G is equal to the preset second optimization threshold, or S is equal to the preset number of abnormal bolt models, obtain Z bolt deformation coefficients under the second optimization parameter, where Z is an integer greater than zero;

[0112] Step a7. fusion statistics of K bolt deformation coefficients under the first optimization parameter and Z bolt deformation coefficients under the second optimization parameter to obtain L bolt deformation coefficients, and sort the L bolt deformation coefficients from small to large values;

[0113] Step a8. Taking the optimized value of the bolt diameter and the optimized value of the strength of the anchor bolts corresponding to the first sort as the bolt optimization strategy.

[0114] It should be noted that when the number of statistically abnormal bolt models is zero, parametric design is not performed on the key design data of the bolt three-dimensional model.

[0115] Through the above-mentioned anchor bolt parametric design optimization method, the optimized bolt parameters can better disperse and bear the load, reduce the frequency of fatigue and fracture, extend the service life of the anchor bolts, and reduce the risk of failure of the anchor bolts used to fix the power equipment, thereby improving the reliability of the power equipment, while reducing the production materials and maintenance costs of the anchor bolts, and improving the overall benefits of the project. This data-driven optimization process can not only be applied to bolts, but can also be extended to the optimization of other similar engineering components, and has broad application prospects.

[0116] Step 5: Based on the bolt optimization strategy, parameter design optimization is performed on the corresponding key design data in the abnormal bolt model to obtain an optimized bolt model.

[0117] In a specific embodiment, parameter design optimization is performed on the corresponding key design data in the abnormal bolt model based on the bolt optimization strategy, including: parameter design is performed on each abnormal bolt model according to the current optimization value of each abnormal bolt model and the current number of abnormal bolt models;

[0118] In another specific embodiment, parameter design optimization is performed on the corresponding key design data in the abnormal bolt model based on the bolt optimization strategy, including: adjusting the quantity of abnormal bolt models according to the number of abnormal bolt models corresponding to the first sorting, the optimized value of the bolt diameter, and the optimized value of the strength.

[0119] Step 6: Use the anchor bolts produced by the optimized bolt model to fix the power equipment.

[0120] It should be noted that the production process of the anchor bolts must strictly follow the design and quality control standards. Whether the optimized model or the normal model is used, it is necessary to ensure that the size, material and processing accuracy of the bolts meet the regulations and ensure that the anchor bolts have sufficient strength and durability during use. The production process of the anchor bolts is not the invention point of this embodiment and will not be elaborated on in detail.

[0121] This embodiment trains a bolt parameter design model by collecting parameter training data of anchor bolts, so that it can accurately predict the key design data of the initial anchor bolts and improve the scientific nature of the design; by acquiring and calculating the load data of the bolt three-dimensional model, the load bearing capacity of different design schemes is evaluated to ensure the rationality of the anchor bolt design; according to the judgment result of the bolt three-dimensional model, a bolt optimization strategy is generated and implemented, and parametric design optimization is performed on abnormal bolt models, which is conducive to improving design efficiency, accuracy and reliability, while significantly saving costs and meeting the different needs of different power equipment, reducing the probability of power equipment being damaged due to quality problems of anchor bolts, and helping to improve the safety and reliability of the equipment.

[0122] Example 2

[0123] See also Figure 2 As shown, this embodiment provides a power equipment fixing system based on parametric design of anchor bolts, including:

[0124] A parameter collection module, used to collect parameter training data of anchor bolts in historical installation scenarios of power equipment, wherein the parameter training data includes parameter feature data and corresponding key design data;

[0125] The model training module trains a bolt parameter design model for predicting key design data of the initial anchor bolts based on the parameter training data; the key design data of the initial anchor bolts predicted by the bolt parameter design model are imported into the 3D software to construct n bolt 3D models;

[0126] A load evaluation module, used to obtain load data of n three-dimensional models of bolts, and calculate a load evaluation coefficient of the nth three-dimensional model of bolts according to the load data;

[0127] A judgment module, used to judge the n three-dimensional models of bolts according to the load evaluation coefficient, obtain the judgment result, and generate a bolt optimization strategy according to the judgment result; the judgment result includes a normal bolt model and an abnormal bolt model;

[0128] The design optimization module performs parameter design optimization on the corresponding key design data in the abnormal bolt model based on the bolt optimization strategy to obtain the optimized bolt model.

[0129] Application module, using the anchor bolts produced by the optimized bolt model to fix the power equipment.

[0130] Example 3

[0131] See also Figure 3 As shown, this embodiment provides an electronic device 500. The electronic device 500 may include one or more processors and one or more memories. The memories store computer readable codes, and when the computer readable codes are run by one or more processors, the power equipment fixing method based on the parametric design of anchor bolts as described above may be executed.

[0132] The system according to the embodiment of the present application can also be used Figure 3 The electronic device architecture shown in FIG. Figure 3 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store the power equipment fixing method based on the parametric design of the anchor bolts provided in the present application. Furthermore, the electronic device 500 may also include a user interface 508. Of course, Figure 3 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 3 One or more components of an electronic device are shown.

[0133] Example 4

[0134] See also Figure 4 As shown, this embodiment provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes the power equipment fixing method based on parametric design of anchor bolts of Embodiment 1.

[0135] The formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The weight factors in the formula and the preset thresholds in the analysis process are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data; the size of the weight factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the weight factor depends on the amount of sample data and the corresponding processing coefficient initially set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0136] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired network. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0137] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0138] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0139] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only one. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0142] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0143] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0144] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for fixing power equipment based on parametric design of anchor bolts, characterized in that: include: Step 1: Collect parameter training data of anchor bolts in historical installation scenarios of power equipment, where the parameter training data includes parameter feature data and corresponding key design data; Step 2: Based on the parameter training data, a bolt parameter design model for predicting key design data of the initial anchor bolt is trained; the key design data of the initial anchor bolt predicted by the bolt parameter design model is imported into the 3D software to construct n bolt 3D models; Step 3: Obtain load data of n three-dimensional models of bolts, and calculate the load evaluation coefficient of the nth three-dimensional model of bolts according to the load data; The load data includes bearing capacity and total load value; the bearing capacity is the maximum force borne by the three-dimensional model of the bolt, and the bearing capacity is calculated based on the yield strength and cross-sectional area of ​​the three-dimensional model of the bolt. The calculation formula of the bearing capacity is: ， ； In the formula, is the bearing capacity of the initial anchor bolts, The yield strength of, H is the cross-sectional area, is the circumference of a circle, is the diameter of the initial anchor bolt; The total load value is the sum of all forces that the bolt three-dimensional model needs to bear, and the total load value includes static load and dynamic load. The static load is the weight of the power equipment itself and the weight of accessories, and the dynamic load includes wind load and seismic load; Methods for calculating the load evaluation factor of the bolt 3D model based on load data include: The load capacity and total load values ​​in the load data are formulated to obtain the load assessment factor , and its calculation formula is: ； In the formula, represents the total load value, As represents the safety factor, and the value of the safety factor is 1.5-2.0; Step 4: According to the load evaluation coefficient, the three-dimensional models of n bolts are judged, the judgment results are obtained, and the bolt optimization strategy is generated according to the judgment results; the judgment results include a normal bolt model and an abnormal bolt model; The method of obtaining the judgment result and generating the bolt optimization strategy according to the judgment result includes: Acquire current optimization parameters of the abnormal bolt model according to the judgment result, wherein the optimization parameters include bolt diameter and strength; The current optimization parameters of the abnormal bolt model are input into the pre-built simulation experiment model for simulation to generate the bolt optimization strategy; The method of inputting the current optimization parameters of the abnormal bolt model into the pre-built simulation experiment model for simulation and generating the bolt optimization strategy includes: Step a1. Obtain the current number S of abnormal bolt models, take the bolt diameter of the bolt diameter and the strength as a fixed quantity, take the strength as a variable, and take the current optimization parameter of the strength as Q; Step a2. Let S=S+1, Q=Q+E, and record the bolt deformation coefficient under the optimized value Q, where E is an integer greater than zero; Step a3. Repeat step a2. When Q is equal to the preset first optimization threshold, or S is equal to the preset number of abnormal bolt models, obtain K bolt deformation coefficients under the first optimization parameter, and jump to step a4, where K is an integer greater than zero; Step a4. Take the strength of the bolt diameter and strength as a fixed quantity, the bolt diameter as a variable, and take the current optimization parameter of the bolt diameter as G; Step a5. Reset Q, set G=G+F, S=S+1, and record the bolt deformation coefficient under the optimized value G, where F is an integer greater than zero; Step a6. Repeat step a5, when G is equal to the preset second optimization threshold, or S is equal to the preset number of abnormal bolt models, obtain Z bolt deformation coefficients under the second optimization parameter, where Z is an integer greater than zero; Step a7. fusion statistics of K bolt deformation coefficients under the first optimization parameter and Z bolt deformation coefficients under the second optimization parameter to obtain L bolt deformation coefficients, and sort the L bolt deformation coefficients from small to large values; Step a8. Using the optimized value of the bolt diameter and the optimized value of the strength of the anchor bolts corresponding to the first sort as the bolt optimization strategy; The bolt deformation coefficient generation method is as follows: the deformation data of S abnormal bolt models under the optimization value Q are obtained, and the deformation data are obtained by formula calculation; the deformation data includes the load evaluation coefficient, the bolt elongation and the original length of the bolt; The calculation formula of the bolt deformation coefficient is: ； Where: is the bolt deformation coefficient, is the weight factor of the load assessment coefficient, is the bolt elongation, is the original length of the bolt; Step 5: Based on the bolt optimization strategy, parameter design optimization is performed on the corresponding key design data in the abnormal bolt model to obtain an optimized bolt model; Step 6: Use the anchor bolts produced using the optimized bolt model to fix the electrical equipment.

2. The method for fixing electric power equipment based on parametric design of anchor bolts according to claim 1 is characterized in that: The parameter characteristic data includes basic equipment data of the power equipment and installation environment data of the anchor bolts collected in historical installation scenarios; wherein the basic equipment data includes the type, weight and size of the power equipment; the installation environment data includes the mean salinity, mean humidity and mean temperature of the installation environment of the power equipment; The key design data in the parameter training data include bolt diameter, strength and quantity; the key design data is recorded and obtained by continuously adjusting the bolt diameter, strength and quantity when the power equipment is installed and tested until the power equipment is installed stably.

3. The method for fixing electric power equipment based on parametric design of anchor bolts according to claim 2 is characterized in that: The three-dimensional software is AutoCAD, SolidWorks or Fusion 360; The method of training a bolt parameter design model for predicting key design data of an initial anchor bolt includes: Each set of parameter feature data is converted into a feature vector, and all elements of the feature vector are used as inputs of a bolt parameter design model. The bolt parameter design model uses the bolt diameter, strength and quantity predicted for each set of parameter feature data as outputs, and uses the actual bolt diameter, strength and quantity corresponding to each set of parameter feature data as prediction targets, and uses minimizing the sum of prediction accuracies of all predicted bolt diameters, strengths and quantities as a training target; the bolt parameter design model is trained until the sum of prediction accuracies converges, and the training is stopped; the bolt parameter design model is a deep neural network model or a deep belief network model.

4. The method for fixing electric equipment based on parametric design of anchor bolts according to claim 3 is characterized in that: The judgment result includes a normal bolt model and an abnormal bolt model; According to the load evaluation coefficient, the method for judging the three-dimensional model of n bolts includes: Preset coefficient threshold and , < , comparing the load assessment coefficient with a preset coefficient threshold; like or < , then the nth bolt three-dimensional model is judged to be an abnormal bolt model; like ≤ ≤ , then the nth bolt three-dimensional model is judged to be a normal bolt model; The number of abnormal bolt models is counted to obtain S abnormal bolt models.

5. A power equipment fixing system based on parametric design of anchor bolts, used to implement the power equipment fixing method based on parametric design of anchor bolts as described in any one of claims 1 to 4, characterized in that: include: A parameter collection module, used to collect parameter training data of anchor bolts in historical installation scenarios of power equipment, wherein the parameter training data includes parameter feature data and corresponding key design data; The model training module trains a bolt parameter design model for predicting key design data of the initial anchor bolts based on the parameter training data; the key design data of the initial anchor bolts predicted by the bolt parameter design model are imported into the 3D software to construct n bolt 3D models; A load evaluation module, used to obtain load data of n three-dimensional models of bolts, and calculate a load evaluation coefficient of the nth three-dimensional model of bolts according to the load data; A judgment module, used to judge the n three-dimensional models of bolts according to the load evaluation coefficient, obtain the judgment result, and generate a bolt optimization strategy according to the judgment result; the judgment result includes a normal bolt model and an abnormal bolt model; The design optimization module performs parameter design optimization on the corresponding key design data in the abnormal bolt model based on the bolt optimization strategy to obtain the optimized bolt model; Application module, using the anchor bolts produced by the optimized bolt model to fix the power equipment.

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