Stone carving task management and control system based on Internet of Things

Abstract

The invention discloses a stone sculpture carving task management and control system based on the Internet of Things, relates to a task management and control technology, is used for solving the problems that abnormal vibration of a cutter and carving track deviation are prone to occurring in the carving process, and obtains the crack volume and the bubble volume by detecting internal cracks and bubbles before rough carving of carved stone. Generating a material defect value; meanwhile, the use duration of the rough carving cutter is collected, cutter vibration is monitored and processed, cutter abrasion is evaluated, and an abrasion evaluation result is generated. Material defect values and tool wear evaluation are synthesized, rough carving hidden danger characteristics are analyzed, and the machining quality and stability are improved. The fine carving duration is obtained, and whether a secondary fine carving task is added or not is judged in combination with the hidden danger features; and if so, collecting secondary engraving feedback data, and evaluating the effect of the secondary engraving feedback data. The method effectively improves the engraving quality and the machining efficiency, reduces the waste loss and enhances the intelligent level of machining.

Description

Technical Field

[0001] The present invention relates to the technical field of task management and control, and more specifically, to an Internet of Things-based stone carving task management and control system. Background Art

[0002] In traditional stone carving techniques, a two-stage processing strategy of rough carving and fine carving is usually adopted to improve carving efficiency and work quality. In the rough carving stage, the general outline of the original stone material is mainly stripped, and the fine carving stage is used for detail restoration and surface treatment. However, in practical applications, natural defects of the stone material (such as cracks and bubbles) often pose unforeseen breakage risks during the carving process. Especially in the rough carving stage, if the internal material structure defects of the stone material cannot be accurately identified, it is easy to cause material fragmentation or carving failure.

[0003] At present, the rough carving tool has significant wear under high-intensity operation, and the tool wear degree directly affects the surface quality of carving and the equipment stability. In the prior art, the evaluation of the tool service life mostly relies on manual experience, lacking a real-time monitoring and intelligent analysis mechanism for the tool vibration state, resulting in problems such as abnormal tool vibration and carving trajectory deviation during the carving process. Therefore, an Internet of Things-based stone carving task management and control system is proposed. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an Internet of Things-based stone carving task management and control system, which solves the problems raised in the above background art through the collaborative operation of multi-source data acquisition, state evaluation modeling, and intelligent task scheduling modules.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An Internet of Things-based stone carving task management and control system, including a defect detection module, a rough carving wear module, a hidden danger analysis module, and a secondary fine carving module;

[0007] The defect detection module is used to detect internal cracks and bubbles of the carving stone material before rough carving, obtain the crack volume and bubble volume of the carving stone material, and generate a material defect value of the carving stone material;

[0008] The rough carving wear module is used to collect the tool usage duration of the rough carving tool, monitor the tool carving vibration of the current rough carving tool and process it, evaluate the tool wear of the rough carving tool in combination with the tool usage duration, and generate a wear evaluation result;

[0009] The hidden danger analysis module is used to comprehensively analyze the material defect value of the carved stone material and the wear evaluation result of the rough carving tool to analyze the hidden danger characteristics of the current rough carving, obtain the duration of the current fine carving, and select whether to add a secondary fine carving task in combination with the hidden danger characteristics of the current rough carving;

[0010] The secondary fine carving module is used to collect feedback data after the carved stone material is secondarily fine carved when a secondary fine carving task is added, and evaluate the effect of the secondary fine carving task according to the feedback data.

[0011] In a preferred embodiment, the crack volume and the bubble volume are respectively the sum of all crack volumes and the sum of bubble volumes inside the carved stone material;

[0012] Generate the material defect value of the carved stone material using a neural network algorithm based on the crack volume and the bubble volume of the carved stone material.

[0013] In a preferred embodiment, when generating the material defect value of the carved stone material using a neural network algorithm, the calculation is performed through the neural network algorithm formula: , where w is the influencing factor adjustment parameter used to adjust the crack volume of the carved stone material to make it reach the same dimension as the bubble volume, l is the crack volume of the carved stone material, q is the bubble volume of the carved stone material, a is the material defect value of the carved stone material, and f is the neural network function relationship between l and q and a respectively.

[0014] In a preferred embodiment, when the carved stone material enters the rough carving stage, call the information of the rough carving tool currently used for carving the carved stone material, including the tool usage duration of the rough carving tool. During the rough carving process of the carved stone material, the tool carving vibration of the rough carving tool is monitored in real time and compared with a preset vibration threshold;

[0015] If the tool carving vibration of the rough carving tool exceeds the preset vibration threshold, it is determined that the rough carving tool has an abnormal vibration phenomenon and its carving vibration value is used as the abnormal value of the tool carving vibration of the rough carving tool;

[0016] After the rough carving stage is completed, calculate the average value of the abnormal values of the tool carving vibration of the rough carving tool during the rough carving stage. After standardizing the average value of the abnormal values of the tool carving vibration of the rough carving tool and the tool usage duration respectively using the logarithmic normalization algorithm, select the maximum value as the wear evaluation result b of the rough carving tool.

[0017] In a preferred embodiment, the material defect value of the carved stone material and the wear evaluation result of the rough carving tool are standardized and substituted into the logistic regression formula to calculate the hidden danger characteristics of the current rough carving. The specific formula is as follows:

[0018] ;

[0019] Wherein, L is the result of logistic regression calculation, i.e., the hidden danger feature of the current rough carving, e is the natural base, and y is the linear combination term of the logistic regression model. Specifically, y is set as:

[0020] ;

[0021] Wherein, is the bias term, is the material defect value of the carved stone material, is the wear evaluation result of the rough carving tool, and are the regression coefficients of the material defect value of the carved stone material and the wear evaluation result of the rough carving tool respectively;

[0022] After the first round of fine carving is completed, obtain the duration of the current fine carving.

[0023] In a preferred embodiment, record the system timestamps at the start and end of the fine carving respectively, calculate the difference between the fine carving end timestamp and the fine carving start timestamp to obtain the duration of the current fine carving;

[0024] Substitute the duration of the current fine carving and the hidden danger feature of the current rough carving into the principal component discriminant analysis model to obtain the added fine carving coefficient. The implementation steps are as follows:

[0025] A1: Standardize the original input variables, namely the duration of the current fine carving and the hidden danger feature of the current rough carving;

[0026] A2: Construct a sample matrix and perform principal component analysis;

[0027] A3: Perform linear discriminant analysis to obtain the added fine carving coefficient.

[0028] In a preferred embodiment, compare the added fine carving coefficient with a preset fine carving threshold. If the added fine carving coefficient is greater than or equal to the fine carving threshold, a secondary fine carving task needs to be added. If the added fine carving coefficient is less than the fine carving threshold, no fine carving needs to be added;

[0029] Add a secondary fine carving task according to the judgment result. When adding a secondary fine carving task, after the carved stone material is subjected to secondary fine carving, collect feedback data.

[0030] In a preferred embodiment, the feedback data includes the amount of cutting waste and the surface finish;

[0031] Through a weighing sensor installed on the carving equipment, real-time monitor the total amount of waste generated during cutting in the processing process, record the difference in waste data at the start and end of cutting, and obtain the amount of cutting waste;

[0032] After the stone material is carved, a non-contact surface roughness measuring instrument is used to sample the surface of the stone material to obtain surface height data. The surface roughness is calculated and the surface finish is obtained in the form of the reciprocal of the surface roughness.

[0033] In a preferred embodiment, the amount of cutting waste and the surface finish are standardized and substituted into a polynomial model to obtain a secondary fine carving evaluation coefficient.

[0034] In a preferred embodiment, the secondary fine carving evaluation coefficient is compared with a preset fine carving evaluation threshold. If the secondary fine carving evaluation coefficient is greater than or equal to the fine carving evaluation threshold, the effect of the secondary fine carving is evaluated as efficient. If the secondary fine carving evaluation coefficient is less than the fine carving evaluation threshold, the effect of the secondary fine carving is evaluated as inefficient.

[0035] The technical effects and advantages of a stone carving task management and control system based on the Internet of Things according to the present invention:

[0036] Before rough carving the stone material to be carved, the present invention detects the internal cracks and air bubbles in the stone material, obtains the crack volume and air bubble volume of the stone material and generates a material defect value of the carved stone material, collects the tool usage duration of the rough carving tool, monitors the tool carving vibration of the rough carving tool for the current time and processes it, evaluates the tool wear condition of the rough carving tool to generate a wear evaluation result, analyzes the hidden danger characteristics of the current rough carving by integrating the material defect value of the carved stone material and the wear evaluation result of the rough carving tool, improves the processing quality and stability, obtains the duration of the current fine carving and selects whether to add a secondary fine carving task in combination with the hidden danger characteristics of the current rough carving. When a secondary fine carving task is added, the feedback data after the secondary fine carving is collected, and the effect of the secondary fine carving task is evaluated according to the feedback data, thereby improving the carving efficiency of the stone carving task. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of a stone carving task management and control system based on the Internet of Things according to the present invention.

[0038] Figure 2 It is a schematic diagram of a stone carving task management and control system based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] Before rough carving the carving stone material, the present invention detects the internal cracks and air bubbles in the carving stone material, obtains the crack volume and air bubble volume of the stone material and generates the material defect value of the carving stone material, collects the tool usage duration of the rough carving tool, monitors and processes the tool carving vibration of the rough carving tool for the current time, evaluates the tool wear condition of the rough carving tool and generates a wear evaluation result, comprehensively analyzes the potential hazard characteristics of the current rough carving based on the material defect value of the carving stone material and the wear evaluation result of the rough carving tool, obtains the current fine carving duration, and selects whether to add a secondary fine carving task in combination with the potential hazard characteristics of the current rough carving. When adding a secondary fine carving task, it collects the feedback data after the secondary fine carving and evaluates the effect of the secondary fine carving task according to the feedback data, so as to improve the carving efficiency of the stone carving task.

[0041] Embodiment 1, An Internet of Things-based stone carving task management and control system, as Figure 1 and Figure 2 shown, includes a defect detection module, a rough carving wear module, a potential hazard analysis module, and a secondary fine carving module, and the modules are electrically connected to each other;

[0042] The functions of each module are as follows:

[0043] The defect detection module is used to detect the internal cracks and air bubbles in the carving stone material before rough carving the carving stone material, obtain the crack volume and air bubble volume of the carving stone material and generate the material defect value of the carving stone material;

[0044] The rough carving wear module is used to collect the tool usage duration of the rough carving tool, monitor and process the tool carving vibration of the rough carving tool for the current time, evaluate the tool wear of the rough carving tool in combination with the tool usage duration and generate a wear evaluation result;

[0045] The potential hazard analysis module is used to comprehensively analyze the potential hazard characteristics of the current rough carving based on the material defect value of the carving stone material and the wear evaluation result of the rough carving tool, obtain the current fine carving duration, and select whether to add a secondary fine carving task in combination with the potential hazard characteristics of the current rough carving;

[0046] The secondary fine carving module is used to collect feedback data after the secondary fine carving of the carving stone material and evaluate the effect of the secondary fine carving task according to the feedback data when adding a secondary fine carving task.

[0047] The specific implementation is as follows:

[0048] There are multiple task stages in the stone carving process, namely the design stage, the material preparation stage, the carving stage, the quality inspection stage, etc. Among them, the carving stage is divided into the rough carving stage and the fine carving stage;

[0049] The rough carving stage involves initially carving the stone material to create a general outline. The fine carving stage then performs detailed carving based on the design drawings generated in the design stage on the basis of the general outline of the stone material obtained from the rough carving. However, the state of different stone materials and the wear of carving tools will affect the final quality of the stone carving.

[0050] The defect detection module is used to detect internal cracks and air bubbles in the carving stone material before rough carving. An ultrasonic detector is used to detect internal cracks and air bubbles in the carving stone material and generate the crack volume and air bubble volume of the carving stone material.

[0051] It should be explained that the crack volume and air bubble volume are respectively the sum of all internal crack volumes and the sum of air bubble volumes in the carving stone material.

[0052] Generate the material defect value of the carving stone material using a neural network algorithm based on the crack volume and air bubble volume of the carving stone material: , where w is the influencing factor adjustment parameter used to adjust the crack volume of the carving stone material to make it have the same dimension as the air bubble volume, l is the crack volume of the carving stone material, q is the air bubble volume of the carving stone material, a is the material defect value of the carving stone material, and f is the neural network functional relationship between l, q and a.

[0053] The larger the crack volume and air bubble volume of the carving stone material, the larger the material defect value of the carving stone material, and the more secondary fine carving is required.

[0054] It should be noted that the above neural network functional relationship f needs to satisfy that the crack volume and air bubble volume are positively correlated with the material defect value. Since both the crack volume and air bubble volume of the carving stone material are non-negative, the above neural network functional relationship can be set to perform operations such as squaring, which will not be elaborated here.

[0055] When the carving stone material enters the rough carving stage, call the information of the rough carving tool currently used for carving the stone material, including the tool usage duration of the rough carving tool. During the rough carving process of the carving stone material, monitor the rough carving tool throughout the process, and monitor the tool carving vibration of the rough carving tool in real time and compare it with the preset vibration threshold.

[0056] If the tool carving vibration of the rough carving tool exceeds the preset vibration threshold, it is determined that the rough carving tool has an abnormal vibration phenomenon and its carving vibration value is used as the abnormal value of the tool carving vibration of the rough carving tool.

[0057] After the rough carving stage is completed, calculate the average value of the abnormal values of the tool carving vibration of the rough carving tool during the rough carving stage, and evaluate the tool wear of the rough carving tool in combination with the tool usage duration of the rough carving tool. The specific steps are as follows:

[0058] Standardize the average value of the abnormal tool carving vibration of the rough carving tool and the tool usage duration using the logarithmic normalization algorithm: , where is the average value of the abnormal tool carving vibration of the rough carving tool or the tool usage duration, is the result after standardizing the average value of the abnormal tool carving vibration of the rough carving tool or the tool usage duration; select the maximum value from the results after standardizing the average value of the abnormal tool carving vibration of the rough carving tool and the tool usage duration as the wear evaluation result b of the rough carving tool.

[0059] The carving tool goes through one round of rough carving and one round of fine carving. Before rough carving, analyze the material defect value of the stone being carved. After rough carving, analyze the wear evaluation result of the rough carving tool. After one round of fine carving, analyze whether to add a secondary fine carving task to the current tool;

[0060] Standardize the material defect value of the carved stone and the wear evaluation result of the rough carving tool. After standardization, all input variables will be transformed into the same range to ensure the balanced contribution of each input to the model;

[0061] It should be noted that the methods of standardization include but are not limited to standard linear transformation based on interval scaling, Z-Score standardization method based on statistics, or normalization method based on non-linear mapping function. The application methods of standardization will not be elaborated here;

[0062] Substitute the material defect value of the carved stone and the wear evaluation result of the rough carving tool into the logistic regression formula to calculate the hidden danger characteristics of the current rough carving. The specific formula is as follows:

[0063] ;

[0064] In the formula, L is the result of logistic regression calculation, that is, the hidden danger characteristics of the current rough carving, e is the natural logarithm base, y is the linear combination term of the logistic regression model, and specifically y is set as:

[0065] ;

[0066] In the formula, is the bias term, is the material defect value of the carved stone, is the wear evaluation result of the rough carving tool, and are the regression coefficients of the material defect value of the carved stone and the wear evaluation result of the rough carving tool respectively;

[0067] After the first round of fine carving is completed, obtain the duration of the current fine carving;

[0068] Among them, the current fine carving duration refers to the time consumed by the carving tool from startup to the completion of fine carving during the first round of fine carving tasks after rough carving and hidden danger feature analysis of the current carved stone material. Its acquisition logic is to record the system timestamps at the start and end of fine carving respectively, and calculate the difference between the fine carving end timestamp and the fine carving start timestamp to obtain the current fine carving duration;

[0069] Substitute the current fine carving duration and the hidden danger features of the current rough carving into the principal component discriminant analysis model to obtain the added fine carving coefficient;

[0070] Among them, the content about standardization processing has been described above and will not be elaborated here;

[0071] It should be noted that the principal component discriminant analysis model combines principal component analysis (PCA) and linear discriminant analysis (LDA): after standardizing multiple input variables, it compresses and reduces the dimension, while retaining the maximum variance feature to improve the model stability. At the same time, it makes a linear separable determination of "whether to add secondary fine carving" in the low-dimensional space and can handle the combined variables of "positive and negative correlation";

[0072] Its implementation steps are as follows:

[0073] A1: Standardize the original input variables, namely the current fine carving duration and the hidden danger features of the current rough carving;

[0074] A2: Construct a sample matrix and perform principal component analysis;

[0075] Specifically, let the standardized sample vector be:

[0076] ;

[0077] Calculate the covariance matrix:

[0078] ;

[0079] In the formula, is the covariance matrix, is the number of samples, is the sample data matrix, is the transpose of the sample data matrix;

[0080] Perform eigen decomposition on the covariance matrix to extract the principal components:

[0081] ;

[0082] In the formula, is the covariance matrix, An orthogonal matrix of the eigenvector matrix, containing all the unit eigenvectors of the covariance matrix (one per column), i.e., the main axis directions, reflecting the most significant change directions of the original variables. The inverse matrix of the eigenvector matrix, representing the transformation that converts the feature space back to the original space.

[0083] Take the first principal components to obtain the compressed representation Z.

[0084] It should be noted that the basis for selecting the principal components is to analyze the magnitudes of the eigenvalues (contribution rates) and select the principal components with a cumulative contribution rate of over 80% to ensure the representativeness and integrity of the data after dimensionality reduction. Further, the principal components Z after dimensionality reduction can be used as input variables for further constructing a linear discriminant analysis (LDA) model or other classification algorithms to achieve accurate determination of whether to add a secondary fine carving task, which will not be elaborated here.

[0085] A3: Linear discriminant analysis.

[0086] Use the training samples (known whether fine carving is added) to establish a linear discriminant function:

[0087] ;

[0088] In the formula, is the weight vector, reflecting the influence intensity of each variable on "adding fine carving". is the principal component vector after dimensionality reduction, obtained from the previous step of principal component analysis (PCA). is the bias term, used to adjust the translation position of the classification function and control the position of the discriminant boundary in the classification space. is the coefficient for adding fine carving.

[0089] Furthermore, in LDA, the calculation method of the weight vector is as follows:

[0090] ;

[0091] In the formula, , respectively represent the mean principal components of the samples in the "need to add fine carving" category and the "do not need to add fine carving" category. is the within-class scatter matrix, reflecting the variance of the data within each category.

[0092] Among them, this calculation method can obtain a stable and repeatable weight vector through the training samples, which will not be elaborated here.

[0093] Compare the added fine carving coefficient with the preset fine carving threshold. If the added fine carving coefficient is greater than or equal to the fine carving threshold, a secondary fine carving task needs to be added. If the added fine carving coefficient is less than the fine carving threshold, no fine carving needs to be added;

[0094] It should be noted that the fine carving threshold is obtained by the experimenters based on historical experimental data and actual process requirements through statistical analysis and empirical optimization methods, which will not be elaborated here;

[0095] Add a secondary fine carving task according to the judgment result. When adding a secondary fine carving task, after the carved stone material is secondarily carved, collect feedback data;

[0096] Among them, the feedback data includes the cutting waste amount and the surface finish;

[0097] The cutting waste amount refers to the volume or weight of the waste material generated during the secondary fine carving process, which is used to reflect the processing efficiency and material utilization rate. Its acquisition logic is to install a weighing sensor on the carving equipment to continuously monitor the total amount of waste generated during the processing, record the difference in waste data at the start and end of cutting, and obtain the cutting waste amount;

[0098] The surface finish refers to the smoothness of the surface of the carved stone material after processing, which is quantified using roughness parameters and reflects the processing quality. Its acquisition logic is to use a non-contact surface roughness measuring instrument to sample the surface of the carved stone material, obtain the surface height data, calculate the surface roughness, and use the reciprocal form of the surface roughness to obtain the surface finish;

[0099] Standardize the cutting waste amount and the surface finish, and substitute them into the polynomial model to obtain the secondary fine carving evaluation coefficient;

[0100] Among them, the standardization process has been described above and will not be elaborated here;

[0101] Among them, the polynomial regression calculation formula is as follows:

[0102] ;

[0103] In the formula, is the secondary fine carving evaluation coefficient, is the cutting waste amount, is the surface finish, is the constant term, and are the weights of the cutting waste amount and the surface finish respectively. The specific weight values are obtained by the experimenters based on the principle of sample goodness of fit and minimization of the sum of squared errors, which will not be elaborated here;

[0104] Compare the secondary fine carving evaluation coefficient with the preset fine carving evaluation threshold. If the secondary fine carving evaluation coefficient is greater than or equal to the fine carving evaluation threshold, the secondary fine carving effect is evaluated as efficient; if the secondary fine carving evaluation coefficient is less than the fine carving evaluation threshold, the secondary fine carving effect is evaluated as inefficient.

[0105] Among them, the fine carving evaluation threshold is determined by the experimenters based on historical processing data and process standards through statistical analysis and empirical optimization methods, which will not be elaborated here.

[0106] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0107] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and invention constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but this implementation should not be considered to exceed the scope of this application.

[0108] In addition, in each embodiment of the present application, the functional modules can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0109] As mentioned above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0110] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An Internet of Things-based stone carving task management and control system, characterized in that, It includes a defect detection module, a rough carving wear module, a hidden danger analysis module, and a secondary fine carving module; The defect detection module is used to detect internal cracks and air bubbles in the carving stone material before rough carving, obtain the crack volume and air bubble volume of the carving stone material, and generate a material defect value of the carving stone material; The rough carving wear module is used to collect the tool usage duration of the rough carving tool, monitor and process the tool carving vibration of the current rough carving tool, and evaluate the tool wear of the rough carving tool in combination with the tool usage duration to generate a wear evaluation result; The hidden danger analysis module is used to analyze the hidden danger characteristics of the current rough carving by integrating the material defect value of the carving stone material and the wear evaluation result of the rough carving tool, obtain the duration of the current fine carving, and select whether to add a secondary fine carving task in combination with the hidden danger characteristics of the current rough carving; The secondary fine carving module is used to collect feedback data after the carving stone material is secondarily finely carved when a secondary fine carving task is added, and evaluate the effect of the secondary fine carving task according to the feedback data.

2. The management and control system for stone carving tasks based on the Internet of Things according to claim 1, characterized in that: The crack volume and the air bubble volume are respectively the sum of all crack volumes and the sum of air bubble volumes inside the carving stone material; The material defect value of the carving stone material is generated by using a neural network algorithm based on the crack volume and air bubble volume of the carving stone material.

3. The management and control system for stone carving tasks based on the Internet of Things according to claim 1, characterized in that: When using the neural network algorithm to generate the material defect value of carved stone, the calculation is carried out through the neural network algorithm formula: , where w is the influencing factor adjustment parameter, which is used to adjust the crack volume of the carved stone to make it reach the same dimension as the bubble volume, l is the crack volume of the carved stone, q is the bubble volume of the carved stone, a is the material defect value of the carved stone, and f is the neural network function relationship between l and q and a respectively.

4. The management and control system for stone carving tasks based on the Internet of Things according to claim 1, characterized in that: When the carving stone material enters the rough carving stage, the information of the rough carving tool currently used for carving the carving stone material is called, including the tool usage duration of the rough carving tool. During the rough carving process of the carving stone material, the tool carving vibration of the rough carving tool is monitored in real time and compared with a preset vibration threshold; If the tool carving vibration of the rough carving tool exceeds the preset vibration threshold, it is determined that the rough carving tool has an abnormal vibration phenomenon, and its carving vibration value is used as the abnormal value of the tool carving vibration of the rough carving tool; After the rough carving stage ends, calculate the average value of the abnormal values of the tool carving vibration of the rough carving tool during the rough carving stage. After standardizing the average value of the abnormal values of the tool carving vibration of the rough carving tool and the tool usage duration respectively by using the logarithmic normalization algorithm, select the maximum value as the wear evaluation result b of the rough carving tool.

5. The management and control system for stone carving tasks based on the Internet of Things according to claim 4, characterized in that: The material defect value of the carving stone material and the wear evaluation result of the rough carving tool are standardized and substituted into the logistic regression formula to calculate the hidden danger characteristics of the current rough carving. The specific formula is as follows: ; In the formula, L is the result of logistic regression calculation, that is, the hidden danger characteristics of the current rough carving, e is the natural logarithm base, y is the linear combination term of the logistic regression model, and specifically y is set as: ; In the formula, is the bias term, is the material defect value of the carved stone, is the wear evaluation result of the rough carving tool, and are the regression coefficients of the material defect value of the carved stone and the wear evaluation result of the rough carving tool, respectively; After the first round of fine carving ends, obtain the duration of the current fine carving.

6. The management and control system for stone carving tasks based on the Internet of Things according to claim 5, characterized in that: Record the system timestamps at the start and end of the precision carving respectively. Calculate the difference between the end timestamp and the start timestamp of the precision carving to obtain the duration of the current precision carving. Substitute the duration of the current precision carving and the potential hazard features of the current rough carving into the principal component discriminant analysis model to obtain the added precision carving coefficient. The implementation steps are as follows: A1: Standardize the original input variables, namely the duration of the current precision carving and the potential hazard features of the current rough carving. A2: Construct a sample matrix and perform principal component analysis. A3: Perform linear discriminant analysis to obtain the added precision carving coefficient.

7. A stone carving task management and control system based on the Internet of Things according to claim 6, wherein: Compare the added precision carving coefficient with the preset precision carving threshold. If the added precision carving coefficient is greater than or equal to the precision carving threshold, a secondary precision carving task needs to be added. If the added precision carving coefficient is less than the precision carving threshold, no additional precision carving is required. Add a secondary precision carving task according to the judgment result. When adding a secondary precision carving task, after the stone material for carving is subjected to secondary precision carving, collect feedback data.

8. A stone carving task management and control system based on the Internet of Things according to claim 7, wherein: The feedback data includes the amount of cutting waste and the surface finish. Through a weighing sensor installed on the carving equipment, continuously monitor the total amount of waste generated during the cutting process in real time, record the difference in waste data at the start and end of the cutting, and obtain the amount of cutting waste. Use a non-contact surface roughness measuring instrument to sample the surface of the stone material after carving, obtain the surface height data, calculate the surface roughness, and use the reciprocal form of the surface roughness to obtain the surface finish.

9. A stone carving task management and control system based on the Internet of Things according to claim 8, wherein: Standardize the amount of cutting waste and the surface finish, and substitute them into the polynomial model to obtain the secondary precision carving evaluation coefficient.

10. A stone carving task management and control system based on the Internet of Things according to claim 9, wherein: Compare the secondary precision carving evaluation coefficient with the preset precision carving evaluation threshold. If the secondary precision carving evaluation coefficient is greater than or equal to the precision carving evaluation threshold, evaluate the effect of the secondary precision carving as efficient. If the secondary precision carving evaluation coefficient is less than the precision carving evaluation threshold, evaluate the effect of the secondary precision carving as inefficient.

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