Parameter determination method and apparatus for low-voltage insulating glove, computer device, readable storage medium, and program product

Abstract

A parameter determination method for a low-voltage insulating glove, comprising: on the basis of operation behavior parameters and operation environment parameters of a target operation scenario, obtaining operation parameters; inputting the operation parameters into a trained friction analysis model to obtain friction analysis results outputted by the friction analysis model and corresponding to hand regions in the target operation scenario; on the basis of the friction analysis results of the hand regions, obtaining glove reinforcement region parameters corresponding to the target operation scenario; collecting hand dimension data of operators in the target operation scenario, and on the basis of a data distribution of the hand dimension data, obtaining glove size category parameters; and on the basis of the glove reinforcement region parameters and the glove size category parameters, obtaining a target parameter set of a low-voltage insulating glove applied in the target operation scenario.

Description

Methods, apparatus, computer equipment, readable storage media, and program products for determining parameters of low-voltage insulating gloves.

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese patent application No. 2024119770420, filed on December 31, 2024, entitled “Method, Apparatus, Computer Equipment, Readable Storage Medium and Program Product for Determining Parameters of Low-Voltage Insulating Gloves”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of information technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining parameters of a low-voltage insulating glove. Background Technology

[0004] Low-voltage insulating gloves are typically used to prevent electric shock to workers' hands in low-voltage work scenarios within power systems. Compared to medium- and high-voltage work scenarios, low-voltage live-line work usually involves a narrower operating space, smaller tools, and more precise movements. Therefore, low-voltage work often presents a more complex working environment and places higher demands on the compatibility of low-voltage insulating gloves with the work environment.

[0005] However, low-voltage insulating gloves are usually designed and manufactured according to uniform parameter specifications, which lacks flexibility when facing different working scenarios, thus easily affecting the safety and efficiency of low-voltage operations. Summary of the Invention

[0006] According to various embodiments of this application, a method, apparatus, computer device, computer-readable storage medium, and computer program product for determining parameters of a low-voltage insulating glove are provided.

[0007] In a first aspect, this application provides a method for determining the parameters of a low-voltage insulating glove, including:

[0008] Based on the operational behavior parameters and operational environment parameters of the target operational scenario, the operational parameters of the target operational scenario are obtained; wherein, the operational behavior parameters include hand movement trajectory parameters and hand movement state parameters;

[0009] The operation parameters are input into the trained friction analysis model to obtain the friction analysis results corresponding to each hand area in the target operation scenario, as output by the friction analysis model; wherein, the friction analysis model is trained using operation parameter samples corresponding to the sample operation scenario; the operation parameter samples include operation behavior parameter samples, operation environment parameter samples, and friction data samples corresponding to the sample operation scenario;

[0010] Based on the friction analysis results of each of the aforementioned hand regions, the parameters of the glove reinforcement area corresponding to the target work scenario are obtained;

[0011] Collect hand size data of each worker in the target work scenario, and obtain glove size category parameters corresponding to the target work scenario based on the data distribution of the hand size data;

[0012] Based on the glove reinforcement area parameters and the glove size category parameters, a target parameter set for low-voltage insulating gloves applied to the target work scenario is obtained.

[0013] Secondly, this application also provides a parameter determination device for low-voltage insulating gloves, comprising:

[0014] The acquisition module is used to acquire the operation behavior parameters and operation environment parameters of the target operation scenario; wherein, the operation behavior parameters include hand movement trajectory parameters and hand movement state parameters;

[0015] The friction analysis module is used to input the work behavior parameters and the work environment parameters into a trained friction analysis model to obtain the friction analysis results of each hand area in the target work scenario output by the friction analysis model; wherein, the friction analysis model is trained using work parameter samples corresponding to the sample work scenario; the work parameter samples include work behavior parameter samples, work environment parameter samples, and friction data samples corresponding to the sample work scenario;

[0016] The first parameter determination module is used to obtain the glove reinforcement area parameters corresponding to the target work scenario based on the friction analysis results of each of the hand areas.

[0017] The second parameter determination module is used to collect hand size data of each worker in the target work scenario, and obtain the glove size category parameter corresponding to the target work scenario based on the data distribution of the hand size data.

[0018] The parameter set determination module is used to obtain the target parameter set of the low-voltage insulating glove applied to the target operation scenario based on the glove reinforcement area parameters and the glove size category parameters.

[0019] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0020] Based on the operational behavior parameters and operational environment parameters of the target operational scenario, the operational parameters of the target operational scenario are obtained; wherein, the operational behavior parameters include hand movement trajectory parameters and hand movement state parameters;

[0021] The operation parameters are input into a trained friction analysis model to obtain the friction analysis results corresponding to each hand region in the target operation scenario, as output by the friction analysis model; wherein, the friction analysis model is trained using operation parameter samples corresponding to the sample operation scenario; the operation parameter samples include operation behavior parameter samples, operation environment parameter samples, and friction data samples corresponding to the sample operation scenario;

[0022] Based on the friction analysis results of each of the aforementioned hand regions, the parameters of the glove reinforcement area corresponding to the target work scenario are obtained;

[0023] Collect hand size data of each worker in the target work scenario, and obtain glove size category parameters corresponding to the target work scenario based on the data distribution of the hand size data;

[0024] Based on the glove reinforcement area parameters and the glove size category parameters, a target parameter set for low-voltage insulating gloves applied to the target work scenario is obtained.

[0025] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0026] Based on the operational behavior parameters and operational environment parameters of the target operational scenario, the operational parameters of the target operational scenario are obtained; wherein, the operational behavior parameters include hand movement trajectory parameters and hand movement state parameters;

[0027] The operation parameters are input into the trained friction analysis model to obtain the friction analysis results corresponding to each hand area in the target operation scenario, as output by the friction analysis model; wherein, the friction analysis model is trained using operation parameter samples corresponding to the sample operation scenario; the operation parameter samples include operation behavior parameter samples, operation environment parameter samples, and friction data samples corresponding to the sample operation scenario;

[0028] Based on the friction analysis results of each of the aforementioned hand regions, the parameters of the glove reinforcement area corresponding to the target work scenario are obtained;

[0029] Collect hand size data of each worker in the target work scenario, and obtain glove size category parameters corresponding to the target work scenario based on the data distribution of the hand size data;

[0030] Based on the glove reinforcement area parameters and the glove size category parameters, a target parameter set for low-voltage insulating gloves applied to the target work scenario is obtained.

[0031] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0032] Based on the operational behavior parameters and operational environment parameters of the target operational scenario, the operational parameters of the target operational scenario are obtained; wherein, the operational behavior parameters include hand movement trajectory parameters and hand movement state parameters;

[0033] The operation parameters are input into the trained friction analysis model to obtain the friction analysis results corresponding to each hand area in the target operation scenario, as output by the friction analysis model; wherein, the friction analysis model is trained using operation parameter samples corresponding to the sample operation scenario; the operation parameter samples include operation behavior parameter samples, operation environment parameter samples, and friction data samples corresponding to the sample operation scenario;

[0034] Based on the friction analysis results of each of the aforementioned hand regions, the parameters of the glove reinforcement area corresponding to the target work scenario are obtained;

[0035] Collect hand size data of each worker in the target work scenario, and obtain glove size category parameters corresponding to the target work scenario based on the data distribution of the hand size data;

[0036] Based on the glove reinforcement area parameters and the glove size category parameters, a target parameter set for low-voltage insulating gloves applied to the target work scenario is obtained. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the disclosed drawings without creative effort.

[0038] Figure 1 is a flowchart illustrating a method for determining parameters of a low-voltage insulating glove in one embodiment;

[0039] Figure 2 is a flowchart illustrating the training process of a trained friction analysis model in one embodiment.

[0040] Figure 3 is a flowchart illustrating the process of obtaining job behavior parameters in one embodiment;

[0041] Figure 4 is a flowchart illustrating the process of obtaining the target parameter set in one embodiment;

[0042] Figure 5 is a flowchart illustrating the process of obtaining size category parameters in one embodiment;

[0043] Figure 6 is a structural block diagram of a parameter determination device for low-voltage insulating gloves in one embodiment;

[0044] Figure 7 is an internal structure diagram of a computer device in one embodiment. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] In one embodiment, as shown in Figure 1, a method for determining the parameters of a low-voltage insulating glove is provided. This embodiment illustrates the method by applying it to a server. It is understood that this method can also be applied to a terminal, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0047] Step S101: Obtain the operation parameters of the target operation scenario based on the operation behavior parameters and operation environment parameters of the target operation scenario.

[0048] Specifically, the target work scenario can be a work scenario that requires the use of low-voltage insulating gloves for low-voltage live-line operations. The work behavior parameters can include hand movement trajectory parameters and hand movement state parameters. The hand movement trajectory parameters can include the movement trajectory of the worker's hand at different positions (e.g., multiple different first hand sampling points distributed on the fingers and palm) during the target work operation. The hand movement state parameters can include parameters such as velocity and acceleration of the hand at different positions (e.g., multiple different second hand sampling points on the fingers and palm) during the target work operation. The work environment parameters can be parameters that reflect the environmental characteristics of the target work scenario, such as, but not limited to, obstacle information (e.g., the type, number, and relative position of obstacles in the work scenario), operating space information (e.g., the width, depth, and height range of the operating space), operating environment information (e.g., ambient temperature and humidity), and work tool type information.

[0049] In this step, the operation parameters can be obtained by combining the operation behavior parameters and operation environment parameters of the target operation scenario.

[0050] Step S102: Input the operation parameters into the trained friction analysis model to obtain the friction analysis results corresponding to each hand area in the target operation scenario output by the friction analysis model.

[0051] For example, the friction analysis model can be trained using multiple operational parameter samples corresponding to multiple sample work scenarios. Optionally, each operational parameter sample may include operational behavior parameter samples, operational environment parameter samples, and friction data samples corresponding to the respective sample work scenario. By training the neural network model using multiple operational parameter samples, a friction analysis model can be obtained that can accurately analyze the frictional forces experienced by different areas of the hand in different low-pressure work scenarios based on the operational parameters of those scenarios.

[0052] Optionally, the friction analysis model can be a neural network model based on a multilayer perceptron. In this step, the operation parameters of the target operation scenario can be converted into corresponding vectors and then input into the trained friction analysis model.

[0053] By inputting the operational parameters of the target work scenario into a trained friction analysis model, friction analysis results corresponding to each hand region can be obtained from the model's output. These results can include the magnitude and direction of the frictional force acting on that hand region, as well as information such as the ranking of the frictional force acting on that hand region among all corresponding hand regions.

[0054] Step S103: Based on the friction analysis results of each hand area, obtain the glove reinforcement area parameters corresponding to the target work scenario.

[0055] The glove reinforcement area parameter may include information indicating the glove reinforcement area that needs to be reinforced on the low-voltage insulating glove.

[0056] Optionally, in this step, based on the friction analysis results of each hand area, the hand areas can be sorted in descending order of the frictional force they experience, and then the hand areas ranked before the preset position can be identified as the glove reinforcement areas that need to be reinforced in the low-voltage insulating glove.

[0057] Optionally, in this step, based on the friction analysis results of each hand area, the hand areas with frictional forces greater than a preset threshold can be identified as glove reinforcement areas that need to be reinforced in the low-pressure insulating glove.

[0058] Step S104: Collect hand size data of each worker in the target work scenario, and obtain the glove size category parameters corresponding to the target work scenario based on the data distribution of the hand size data.

[0059] In this context, the workers in the target work scenario are those who need to wear low-voltage insulating gloves to perform low-voltage live-line operations. This step involves statistically analyzing the hand size data of each worker in the target work scenario to obtain the data distribution corresponding to the hand size data of all workers.

[0060] For example, the hand size data collected from workers can include various hand dimensions such as finger length, palm length, palm width, and palm thickness. These dimensions can be measured through methods such as manual measurement or 3D scanning.

[0061] After obtaining the hand size data of each worker, the hand size data can be divided into multiple preset hand size categories (such as XS, S, M, L, XL, etc.) by means of cluster analysis of each dimension data or by grouping them based on the mean and standard deviation of each dimension data.

[0062] Then, based on the value range of each dimension data corresponding to each hand size category, the glove size parameters that can be adapted to that hand size category can be determined. For example, glove size parameters may include parameters such as finger length, palm length, palm width, and palm thickness. Then, based on the glove size parameters corresponding to each hand size category, the glove size category parameters for low-voltage insulating gloves can be obtained.

[0063] Step S105: Based on the glove reinforcement area parameters and glove size category parameters, obtain the target parameter set for the low-voltage insulating glove applied to the target operation scenario.

[0064] Based on the glove reinforcement area parameters and glove size category parameters for the target work scenario, a target parameter set for low-voltage insulating gloves can be obtained. This target parameter set can then be applied to the design and manufacture of low-voltage insulating gloves for that target work scenario. For example, reinforcement treatment can be applied to relevant surface areas of the glove based on the glove reinforcement area parameters, such as by setting abrasion-resistant reinforcement structures; simultaneously, the specific dimensions of low-voltage insulating gloves of different size categories can be determined based on the glove size category parameters.

[0065] In the aforementioned method for determining the parameters of low-voltage insulating gloves, for a specific low-voltage work scenario, by collecting environmental parameters and behavioral parameters including hand movement trajectory and hand movement state parameters, work parameters reflecting the characteristics of the target work scenario can be obtained. Then, by inputting these work parameters into a trained friction analysis model, the model can accurately analyze the frictional forces experienced by different areas of the hand during the work operation in the target work scenario to obtain corresponding friction analysis results. Based on these results, the areas in the target work scenario that require reinforcement of the low-voltage insulating gloves can be determined, yielding the corresponding glove reinforcement area parameters. Simultaneously, by statistically analyzing the hand size data of various workers in the target work scenario and determining the glove size category parameters based on their distribution, glove size category parameters that match the hand sizes of most frontline workers in the target work scenario can be determined. Therefore, by utilizing the target parameter set obtained based on the glove reinforcement area parameters and glove size category parameters, low-voltage insulating gloves that can be designed and produced are compatible with the operational characteristics of the target work scenario and the hand size of frontline workers who need to perform the target work, thereby effectively ensuring the safety and efficiency of the target low-voltage work.

[0066] In an exemplary embodiment, as shown in Figure 2, the trained friction analysis model can be obtained through the following steps:

[0067] Step S201: Obtain the sample of operation parameters corresponding to the sample operation scenario.

[0068] The sample operation scenarios can include different scenarios requiring low-voltage live-line operations while wearing low-voltage insulating gloves. Test personnel can perform corresponding operations in these scenarios to collect behavioral and environmental parameters, which will serve as samples of these parameters for that specific scenario. Simultaneously, flexible pressure sensors mounted on the test personnel's gloves can monitor the pressure on different areas of the hand during operation, allowing analysis of the magnitude and direction of frictional forces acting on each area, thus obtaining friction data samples for that scenario.

[0069] In this step, multiple samples of operational parameters corresponding to different operational scenarios can be obtained and used as training data for the friction analysis model.

[0070] Step S202: Input the operation behavior parameter samples and operation environment parameter samples contained in the operation parameter samples into the neural network model based on the multilayer perceptron to obtain the friction analysis results corresponding to the sample operation scenario output by the neural network model.

[0071] In this step, a pre-built neural network model based on a multilayer perceptron can be used as the friction analysis model to be trained. The operation parameter samples, including operation behavior parameter samples and operation environment parameter samples, are converted into vectors and input into the model. The model processes the data and outputs the corresponding friction analysis results.

[0072] For example, the friction analysis model to be trained may include an input layer, a hidden layer, and an output layer. The input layer can be used to receive input feature vectors and transmit them to the hidden layer; the hidden layer can be a fully connected layer, which can be used to learn feature representations; the output layer can receive the output of the hidden layer and output the friction analysis results corresponding to each hand region.

[0073] For example, taking a friction analysis model to be trained that contains two hidden layers as an example, assuming there are a total of N feature vectors for the operation behavior parameter samples and the operation environment parameter samples, the process of the model processing the input data can be represented as follows:

[0074] First, the input layer receives the input feature vector and passes it to the first hidden layer. The output of the input layer can be represented as:

[0075] Then, the first hidden layer receives the feature vector x from the input layer, processes it to obtain the corresponding output h1, and then transmits it to the second hidden layer. This processing can be represented as: h1 = f1(W1x + b1)

[0076] In the formula, W1 is the weight matrix of the first hidden layer. H1 represents the number of neurons in the first hidden layer; b1 represents the bias vector of the first hidden layer. f1 is the activation function of the first hidden layer (e.g., ReLU, Sigmoid, etc.).

[0077] The second hidden layer receives vector h1 from the first hidden layer, processes it to obtain the corresponding output h2, and then transmits it to the output layer. This processing can be represented as: h2 = f2(W2h1 + b2).

[0078] In the formula, W2 is the weight matrix of the second hidden layer. H2 represents the number of neurons in the second hidden layer; b2 represents the bias vector of the second hidden layer. f2 is the activation function of the second hidden layer (e.g., ReLU, Sigmoid, etc.).

[0079] Subsequently, the output layer can process the vector h2 received from the second hidden layer to obtain the friction analysis results corresponding to each hand region. This process can be represented as:

[0080] Where W3 is the weight matrix of the output layer. M represents the number of neurons in the output layer; b3 represents the bias vector of the output layer. Optionally, the number of M can be set to be the same as the number of hand regions, and the output value of each neuron can correspond to the friction analysis result of a hand region.

[0081] Step S203: Based on the difference between the friction analysis results corresponding to the sample operation scenario and the friction data samples included in the operation parameter samples, the model parameters of the neural network model are adjusted using an optimization algorithm based on gradient descent to obtain a trained friction analysis model.

[0082] Specifically, based on the differences between the friction analysis results output by the friction analysis model to be trained and the friction data samples corresponding to the same sample operation scenario contained in the operation parameter sample, the model parameters of the model can be optimized to obtain the trained friction analysis model.

[0083] For example, in this step, mean squared error can be used as a loss function to measure the difference between the friction analysis results and the friction data samples based on the magnitude of the mean squared error:

[0084] Where y represents a friction data sample. The friction analysis results output by the model, The mean square error between the friction analysis results and the friction data samples represents the error between the two, where N is the number of operating parameter samples, and y is the mean square error between the two. i Let i be the friction data sample contained in the i-th operation parameter sample. This represents the friction analysis result corresponding to the i-th operational parameter sample.

[0085] Based on the aforementioned loss function, the model parameters can be optimized and adjusted using a gradient descent-based optimization algorithm to obtain model parameters that minimize the mean square error between the friction analysis results and the friction data samples, thereby obtaining a trained friction analysis model. For example, the optimization algorithm used in this step may include, but is not limited to, gradient descent, Adaptive Moment Estimation (Adam), and Root Mean Square Propagation (RMS Propagation).

[0086] In this embodiment, a neural network model based on a multilayer perceptron is constructed as the friction analysis model to be trained. This model is then trained using operational parameter samples corresponding to the sample work scenarios. This yields a model capable of accurately analyzing friction conditions in different low-pressure work scenarios. Furthermore, adjusting the model's parameters using a gradient descent-based optimization algorithm improves training efficiency and model accuracy. Therefore, the trained friction analysis model can output accurate friction analysis results even when facing target work scenarios different from the sample work scenarios.

[0087] In an exemplary embodiment, as shown in FIG3, before obtaining the operation parameters of the target operation scenario based on the operation behavior parameters and operation environment parameters of the target operation scenario, the method further includes:

[0088] Step S301: Use an inertial sensor to acquire the acceleration and angular velocity parameters of multiple first hand sampling points during the operation process in the target operation scenario, and obtain the hand motion state parameters based on the acceleration and angular velocity parameters.

[0089] The inertial measurement unit (IMU) measures the acceleration and angular velocity of an object. In this step, multiple inertial sensors worn on the hand can measure the acceleration and angular velocity parameters of each first hand sampling point during the operation in the target work scenario. Based on these parameters, the velocity, acceleration, and other parameters of each first hand sampling point can be calculated, thereby obtaining the hand motion state parameters of the target work scenario. For example, the first hand sampling points may include fingertips, finger roots, palms, etc.

[0090] Step S302: Use an optical tracking device to track the motion trajectory of multiple second hand sampling points during the operation, and obtain hand motion trajectory parameters based on the motion trajectory.

[0091] The optical tracking device can capture the precise position of the marker points in three-dimensional space and obtain their motion trajectory. In this step, marker points can be set at multiple second hand sampling points on the hand, and multiple miniature cameras can be used to track the operation process from all angles. This allows the motion trajectory of each second hand sampling point during the operation to be obtained, and thus the hand motion trajectory parameters of the target operation scene can be obtained. For example, the second hand sampling points can include multiple different locations on the fingers and palm.

[0092] Step S303: Obtain the operation behavior parameters based on the hand movement state parameters and hand movement trajectory parameters.

[0093] In this step, by combining the hand motion state parameters and the hand motion trajectory parameters, we can obtain job behavior parameters that can comprehensively and accurately reflect hand behavior in the target job scenario.

[0094] In this embodiment, for hand behavior in low-pressure operation scenarios, inertial sensors are used to obtain hand motion state parameters, and optical tracking devices are used to obtain hand motion trajectory parameters. This allows for the acquisition of multi-dimensional hand behavior data during the operation, better reflecting the hand behavior characteristics in the operation scenario. This data is beneficial for subsequent more accurate analysis of friction conditions in the target operation scenario.

[0095] In an exemplary embodiment, the operational parameter samples used to train the friction analysis model also include surface structure parameter samples and wear data samples of each surface region of the low-voltage insulating glove sample in the sample operational scenario; the operational parameters of the target operational scenario also include surface structure parameters of each surface region of the low-voltage insulating glove in the target operational scenario; the friction analysis results of each hand region output by the friction analysis model include friction force analysis data and wear analysis data of the hand region.

[0096] As shown in Figure 4, after obtaining the glove reinforcement area parameters corresponding to the target work scenario based on the friction analysis results of each hand area, it may also include:

[0097] Step S401: Determine the target reinforcement area of ​​the low-voltage insulating glove in the target operation scenario based on the glove reinforcement area parameters corresponding to the target operation scenario.

[0098] Step S402: If the friction analysis data and wear analysis data corresponding to the target reinforcement area do not meet the preset conditions, the friction analysis model is used to iteratively optimize the surface structure parameters of the target reinforcement area until the target surface structure parameters that make the friction analysis data and wear analysis data of the target reinforcement area meet the preset conditions are obtained, and then the iteration stops.

[0099] Based on the glove reinforcement area parameters and glove size category parameters, a target parameter set for low-voltage insulating gloves applied to the target operational scenario is obtained, which may also include:

[0100] Step S403: Based on the glove reinforcement area parameters, glove size category parameters, and target surface structure parameters of the target reinforcement area, obtain the target parameter set.

[0101] Since the friction force experienced by the hand area is usually correlated with the wear condition of the corresponding area of ​​the low-voltage insulating glove, this embodiment can use a trained friction analysis model to analyze the friction force experienced by each hand area and the wear condition of each area on the surface of the low-voltage insulating glove at the same time.

[0102] Specifically, the trained friction analysis model used in this embodiment can receive hand motion trajectory parameters, hand motion state parameters, and surface structure parameters of each surface area of ​​the low-voltage insulating glove as input data from the operational parameter sample. By processing this data, it outputs friction analysis results corresponding to each hand area. These friction analysis results can include friction force analysis data for the hand area and wear analysis data for the glove surface area corresponding to that hand area. For example, the surface structure parameters of the surface area can include the roughness, hardness, etc., of that surface area on the glove, and the wear analysis data can include numerical values ​​representing the degree of wear on the glove surface (e.g., the value range can be 0 to 100%, with smaller values ​​indicating lower wear and larger values ​​indicating higher wear).

[0103] The model can be trained using operation parameter samples, which include operation behavior parameter samples, operation environment parameter samples, and friction data samples in the sample operation scenario, as well as surface structure parameter samples and wear data samples of each surface area of ​​the low-voltage insulating glove sample.

[0104] For example, the friction analysis model can be a multi-task learning model based on a multilayer perceptron, which may include an input layer, hidden layers, and an output layer. Since there is a correlation between friction and wear, the same input layer and shared hidden layers can be used to process both tasks during model construction. The output layer receives the data input to the model and performs preliminary feature extraction from the first few hidden layers. Then, branches containing hidden layers and output layers can be constructed for each task. In one branch, the hidden layer can further learn features related to friction analysis based on the preliminary features extracted from the shared hidden layer and transmit them to the output layer of that branch. This output layer can output friction analysis data for each hand region. In the other branch, the hidden layer can further learn features related to wear analysis based on the preliminary features extracted from the shared hidden layer and transmit them to the output layer of that branch. This output layer can output wear analysis data corresponding to the glove surface areas for each hand region.

[0105] Understandably, by inputting the operational parameters of the target work scenario into a trained friction analysis model, the friction analysis results and wear analysis data output by the model can be obtained. Then, using these friction analysis results, the glove reinforcement region parameters corresponding to the target work scenario can be further obtained. These glove reinforcement region parameters can include information indicating the glove reinforcement region that needs to be reinforced on the low-voltage insulating glove, and this glove reinforcement region corresponds to the hand area subjected to greater friction.

[0106] Based on this, in step S401, the target reinforcement area that needs to be reinforced on the surface of the low-voltage insulating glove in the target operation scenario can be determined according to the glove reinforcement area parameters.

[0107] Then, in step S402, the friction analysis data and wear analysis data corresponding to the target reinforcement area can be analyzed to determine whether they meet the corresponding preset conditions. For example, to ensure the safety of low-voltage live-line work, low-voltage insulating gloves need to have certain anti-slip and anti-wear properties. Based on this, friction thresholds and wear thresholds can be set respectively. When the friction analysis data of the target reinforcement area indicates that its corresponding friction force is not less than the friction threshold, and the wear analysis data indicates that its corresponding wear degree is not greater than the wear threshold, it can be considered that the friction analysis data and wear analysis data meet the preset conditions.

[0108] Specifically, if the friction analysis data and wear analysis data do not meet the preset conditions, the surface structure parameters corresponding to the target reinforcement area can be adjusted. Then, the trained friction analysis model can be used to analyze the friction and wear conditions in the target working scenario again based on the adjusted surface structure parameters and the remaining data from the working parameters of the target working scenario. When the friction analysis data and wear analysis data output by the model meet the preset conditions, the adjusted surface structure parameters of the target reinforcement area can be used as the target surface structure parameters for that area.

[0109] Subsequently, in step S403, a target parameter set for the low-voltage insulating glove can be obtained based on the glove reinforcement area parameters, glove size category parameters, and target surface structure parameters of the target reinforcement area. Based on this target parameter set, in the subsequent design and manufacturing of the low-voltage insulating glove for the target operational scenario, more targeted reinforcement treatment can be performed on the relevant surface areas of the glove according to the target surface structure parameters of the target reinforcement area.

[0110] In this embodiment, taking advantage of the correlation between the frictional force experienced by the hand area and the wear condition of the corresponding surface area on the low-voltage insulating glove, a trained friction analysis model is used to simultaneously analyze the frictional force experienced by the hand area and the wear condition of the glove surface in the target work scenario. This allows for more accurate data reflecting the characteristics of low-voltage operations in the target work scenario. After determining the target reinforcement area of ​​the low-voltage insulating glove based on the glove reinforcement area parameters, the surface structure parameters of the target reinforcement area that do not meet the preset conditions in the frictional force analysis and wear analysis data are adjusted. Then, the friction analysis model is used again to perform friction and wear analyses based on the adjusted surface structure parameters. This allows for the enhancement of the wear resistance of the target reinforcement area while considering the glove's anti-slip performance, resulting in target surface structure parameters that simultaneously meet the wear resistance and anti-slip requirements of low-voltage operations. Therefore, by obtaining a target parameter set based on the glove reinforcement area parameters, glove size category parameters, and target surface structure parameters, it is beneficial to design and manufacture low-voltage insulating gloves with higher safety in the target work scenario.

[0111] In an exemplary embodiment, the surface structure parameters of the surface region may include the protrusion structure parameters of the surface region, or the sandblasting reinforcement structure parameters, or a combination of the protrusion structure parameters and the sandblasting reinforcement structure parameters.

[0112] The surface strengthening treatment of the glove can include one or more treatments such as setting raised structures on the glove surface or sandblasting the glove surface. Accordingly, the surface structure parameters of each surface area of ​​the glove can correspondingly include one or more of the raised structure parameters and sandblasting strengthening structure parameters.

[0113] For example, the raised structures on the glove surface can be arranged in an array on one or more surface areas of the glove. The corresponding parameters of the raised structures can include the distribution density, size, and shape of the raised structures. The shape of the raised structures can include hemispherical, cylindrical, conical, frustum-shaped, etc. The size range of the raised structures can be set with consideration for the comfort of using the glove. For example, its height can be set between 0.5 and 1.5 mm to avoid affecting the flexibility and comfort of wearing the gloves for work.

[0114] For example, sandblasting the surface of a glove can be performed by spraying one or more surface areas of the glove with alumina particles coated with silica, such as silica, to make them evenly distributed in the corresponding surface areas. The corresponding sandblasting reinforcement structural parameters may include particle size, spraying concentration, spraying thickness, etc.

[0115] Based on this, when adjusting the surface structure parameters corresponding to the target reinforcement area, adjustments can be made to whether to set a protruding structure in the target reinforcement area, the specific setting parameters of the protruding structure, whether to perform sandblasting treatment, and the specific parameters of sandblasting treatment, in order to determine the best reinforcement treatment method for the target reinforcement area.

[0116] In this embodiment, for reinforcement treatments such as setting raised structures or sandblasting on the glove surface, the parameters of the raised structure and the sandblasting reinforcement structure are included as part of the surface structure parameters. Thus, when adjusting the surface structure parameters of the target reinforcement area, the strength and roughness of the target reinforcement area on the glove surface can be adjusted by adding raised structures, performing sandblasting, or combining the two. Furthermore, the setting method of the raised structure or the sandblasting method can be further refined to obtain the target surface structure parameters that achieve the best balance between the anti-slip and wear resistance of the target reinforcement area.

[0117] In an exemplary embodiment, as shown in Figure 5, the hand size data of each worker in the target work scenario are statistically analyzed. Based on the data distribution of the hand size data, the size category parameters of the low-voltage insulating gloves are obtained, which may include:

[0118] Step S501: Collect the dimensional data corresponding to each hand dimension of each worker in the target work scenario, and obtain the hand size data based on the dimensional data corresponding to each hand dimension.

[0119] The hand size data can include dimensional data corresponding to each hand dimension, such as finger length, palm length, palm width, and palm thickness. In this step, the dimensions of each worker's hand can be measured using methods such as 3D scanning to collect the corresponding dimensional data, thereby obtaining the hand size data for the target work scenario.

[0120] Step S502: Calculate the average value and standard deviation of the dimensional data for each hand dimension.

[0121] For hand size data that includes multiple dimensions, the average value and standard deviation of each dimension can be calculated separately. For example, for each of the aforementioned hand dimensions, the average finger length, average palm length, average palm width, and average palm thickness can be obtained, as well as the standard deviations of finger length, palm length, palm width, and palm thickness.

[0122] Step S503: Based on the average value and standard deviation of the dimensional data corresponding to each hand dimension, determine the dimensional data range corresponding to each hand dimension for each size category.

[0123] This allows for the pre-setting of multiple size categories, such as XS, S, M, L, XL, etc. Then, based on the mean and standard deviation of the dimensional data for each hand dimension, the data can be divided into dimensional data intervals corresponding to each size category.

[0124] For example, taking the hand dimension as finger length, assuming its value range is [a1, a2], the average finger length is μ, and the standard deviation of finger length is σ, it can be divided into multiple dimensional data intervals corresponding to different size categories. For example, it can include: the dimensional data interval [a1, μ-σ) corresponding to size category XS, the dimensional data interval [μ-σ, μ-0.5σ) corresponding to size category S, the dimensional data interval [μ-0.5σ, μ+0.5σ] corresponding to size category M, the dimensional data interval (μ+0.5σ, μ+σ) corresponding to size category L, and the dimensional data interval (μ+σ, a2) corresponding to size category XL.

[0125] It can be understood that the way to divide dimensional data intervals is not unique. The number of size categories and the way intervals are divided can be adjusted according to the numerical range and distribution of the actual hand size data.

[0126] Step S504: Determine the target dimension parameters for each hand dimension corresponding to each size category based on the median value of the data range for each dimension corresponding to each size category.

[0127] In order to ensure that the dimensions of the low-voltage insulating gloves corresponding to each size category are compatible with the hand dimension data of the corresponding dimensional data range, this step can use the median value of the dimensional data range of each hand dimension as the target dimensional parameter for that hand dimension under that size category. That is, for each size category, the target dimensional parameters corresponding to finger length, palm length, palm width, and palm thickness can be determined separately.

[0128] Step S505: Obtain the size category parameters of the low-voltage insulating gloves based on the target dimension parameters corresponding to each size category.

[0129] Specifically, by determining the target dimension parameters corresponding to each hand dimension for each size category, the size category parameters for low-voltage insulating gloves can be obtained. Based on these glove size category parameters, the specific dimensions of different size categories of low-voltage insulating gloves suitable for workers in the target work scenario can be determined during the subsequent design and manufacturing of low-voltage insulating gloves.

[0130] In this embodiment, by statistically analyzing the dimensional data of various hand dimensions of workers in the target work scenario, more accurate and comprehensive hand size data reflecting the hand structure of workers can be obtained. Based on the average and standard deviation of these data, dimensional data intervals corresponding to each size category are divided, and target dimensional parameters corresponding to each hand dimension for each size category are further determined. This allows for the efficient and rapid determination of representative dimensions for each hand dimension within each size category. Subsequently, the size category parameters determined in this process can be applied to the design and manufacturing process of low-voltage insulating gloves to accurately obtain glove sizes for each size category. This enables the manufacture of low-voltage insulating gloves that better conform to the hand structure characteristics of workers in the target work scenario, offering higher fit and comfort.

[0131] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0132] Based on the same inventive concept, this application also provides a parameter determination device for low-voltage insulating gloves, used to implement the parameter determination method for low-voltage insulating gloves described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the parameter determination device for low-voltage insulating gloves provided below can be found in the limitations of the parameter determination method for low-voltage insulating gloves described above, and will not be repeated here.

[0133] In an exemplary embodiment, as shown in FIG6, a parameter determination device for a low-voltage insulating glove is provided, comprising:

[0134] The acquisition module 601 is used to acquire the operation behavior parameters and operation environment parameters of the target operation scenario; wherein, the operation behavior parameters include hand movement trajectory parameters and hand movement state parameters;

[0135] Friction analysis module 602 is used to input the work behavior parameters and the work environment parameters into a trained friction analysis model to obtain the friction analysis results of each hand area in the target work scenario output by the friction analysis model; wherein, the friction analysis model is trained using work parameter samples corresponding to the sample work scenario; the work parameter samples include work behavior parameter samples, work environment parameter samples, and friction data samples corresponding to the sample work scenario.

[0136] The first parameter determination module 603 is used to obtain the glove reinforcement area parameters corresponding to the target work scenario based on the friction analysis results of each of the hand areas.

[0137] The second parameter determination module 604 is used to collect hand size data of each worker in the target work scenario, and obtain the glove size category parameter corresponding to the target work scenario based on the data distribution of the hand size data.

[0138] The parameter set determination module 605 is used to obtain a target parameter set for low-voltage insulating gloves applied to the target work scenario based on the glove reinforcement area parameters and the glove size category parameters.

[0139] In an exemplary embodiment, the trained friction analysis model is trained through the following steps: obtaining the operation parameter samples corresponding to the sample operation scenario; inputting the operation behavior parameter samples and operation environment parameter samples contained in the operation parameter samples into a neural network model based on a multilayer perceptron to obtain the friction analysis result output by the neural network model corresponding to the sample operation scenario; adjusting the model parameters of the neural network model using a gradient descent-based optimization algorithm based on the difference between the friction analysis result corresponding to the sample operation scenario and the friction data samples contained in the operation parameter samples to obtain the trained friction analysis model.

[0140] In an exemplary embodiment, the device further includes: a state parameter acquisition module, configured to acquire acceleration parameters and angular velocity parameters of multiple first hand sampling points during the operation process in the target operation scenario using an inertial sensor, and obtain the hand motion state parameters based on the acceleration parameters and the angular velocity parameters; a trajectory parameter acquisition module, configured to track the motion trajectory of multiple second hand sampling points during the operation process using an optical tracking device, and obtain the hand motion trajectory parameters based on the motion trajectory; and a behavior parameter acquisition module, configured to obtain the operation behavior parameters based on the hand motion state parameters and the hand motion trajectory parameters.

[0141] In an exemplary embodiment, the operational parameter samples used to train the friction analysis model further include surface structure parameter samples and wear data samples of each surface region of the low-voltage insulating glove sample in the sample operational scenario; the operational parameters of the target operational scenario further include surface structure parameters of each surface region of the low-voltage insulating glove in the target operational scenario; the friction analysis results of each hand region output by the friction analysis model include friction force analysis data and wear analysis data of the hand region; the device further includes: a reinforcement region determination module, used to determine the reinforcement region parameters of the glove in the target operational scenario based on the glove reinforcement region parameters corresponding to the target operational scenario. The low-voltage insulating glove has a target reinforcement area; a structural parameter optimization module is used to iteratively optimize the surface structure parameters of the target reinforcement area using the friction analysis model when the friction analysis data and wear analysis data corresponding to the target reinforcement area do not meet the preset conditions, until the target surface structure parameters that make the friction analysis data and wear analysis data of the target reinforcement area meet the preset conditions are obtained, and then the iteration stops; the parameter set determination module 605 is further used to: obtain the target parameter set according to the glove reinforcement area parameters, the glove size category parameters and the target surface structure parameters of the target reinforcement area.

[0142] In an exemplary embodiment, the surface structure parameters of the surface region include protrusion structure parameters of the surface region, or sandblasting reinforcement structure parameters, or a combination of protrusion structure parameters and sandblasting reinforcement structure parameters.

[0143] In an exemplary embodiment, the second parameter determining module 604 is further configured to: statistically analyze the dimensional data corresponding to each hand dimension of each worker in the target work scenario; obtain the hand size data based on the dimensional data corresponding to each hand dimension; wherein the hand dimensions include finger length, palm length, palm width, and palm thickness; statistically analyze the average value and standard deviation of the dimensional data corresponding to each hand dimension; determine the dimensional data interval corresponding to each hand dimension for each size category based on the average value and standard deviation of the dimensional data corresponding to each hand dimension; determine the target dimensional parameter corresponding to each hand dimension for each size category based on the median of the data in each dimensional data interval corresponding to each size category; and obtain the size category parameter of the low-voltage insulating glove based on the target dimensional parameters corresponding to each size category.

[0144] The various modules in the aforementioned low-voltage insulating glove parameter determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0145] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram is shown in Figure 7. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data such as operational behavior parameters, operational environment parameters, and hand size data of each operator in the target work scenario. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for determining parameters of a low-voltage insulating glove.

[0146] Those skilled in the art will understand that the structure shown in Figure 7 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0147] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0148] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0149] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0150] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0151] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0152] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

A method for determining parameters of a low-voltage insulating glove, wherein, The method includes: Based on the operational behavior parameters and operational environment parameters of the target operational scenario, the operational parameters of the target operational scenario are obtained; wherein, the operational behavior parameters include hand movement trajectory parameters and hand movement state parameters; The operation parameters are input into the trained friction analysis model to obtain the friction analysis results corresponding to each hand area in the target operation scenario, as output by the friction analysis model; wherein, the friction analysis model is trained using operation parameter samples corresponding to the sample operation scenario; the operation parameter samples include operation behavior parameter samples, operation environment parameter samples, and friction data samples corresponding to the sample operation scenario; Based on the friction analysis results of each of the aforementioned hand regions, the parameters of the glove reinforcement area corresponding to the target work scenario are obtained; Collect hand size data of each worker in the target work scenario, and obtain glove size category parameters corresponding to the target work scenario based on the data distribution of the hand size data; and, Based on the glove reinforcement area parameters and the glove size category parameters, a target parameter set for low-voltage insulating gloves applied to the target work scenario is obtained. According to the method of claim 1, wherein, The trained friction analysis model is obtained through the following steps: Obtain the sample of operation parameters corresponding to the sample operation scenario; The operation behavior parameter sample and the operation environment parameter sample included in the operation parameter sample are input into a neural network model based on a multilayer perceptron to obtain the friction analysis results corresponding to the sample operation scenario output by the neural network model. and, Based on the difference between the friction analysis results corresponding to the sample operation scenario and the friction data samples included in the operation parameter sample, the model parameters of the neural network model are adjusted using a gradient descent-based optimization algorithm to obtain the trained friction analysis model. According to the method of claim 1, wherein, Before obtaining the operation parameters of the target operation scenario based on the operation behavior parameters and operation environment parameters of the target operation scenario, the method further includes: The acceleration and angular velocity parameters of multiple first hand sampling points are acquired during the operation process in the target operation scenario using inertial sensors. Based on the acceleration and angular velocity parameters, the hand motion state parameters are obtained. The motion trajectory of multiple second hand sampling points during the operation is tracked using an optical tracking device, and the hand motion trajectory parameters are obtained based on the motion trajectory; and, The operation behavior parameters are obtained based on the hand movement state parameters and the hand movement trajectory parameters. According to the method of claim 1, wherein, The operational parameter samples used to train the friction analysis model also include surface structure parameter samples and wear data samples of each surface region of the low-voltage insulating glove sample in the sample operational scenario; the operational parameters of the target operational scenario also include surface structure parameters of each surface region of the low-voltage insulating glove in the target operational scenario; the friction analysis results of each hand region output by the friction analysis model include friction force analysis data and wear analysis data of the hand region; After obtaining the glove reinforcement area parameters corresponding to the target work scenario based on the friction analysis results of each of the hand areas, the method further includes: Based on the glove reinforcement region parameters corresponding to the target work scenario, determine the target reinforcement region of the low-voltage insulating glove in the target work scenario; and, If the friction analysis data and wear analysis data corresponding to the target reinforcement area do not meet the preset conditions, the friction analysis model is used to iteratively optimize the surface structure parameters of the target reinforcement area until the target surface structure parameters that make the friction analysis data and wear analysis data of the target reinforcement area meet the preset conditions are obtained, and then the iteration stops. The step of obtaining a target parameter set for low-voltage insulating gloves applicable to the target work scenario based on the glove reinforcement area parameters and the glove size category parameters includes: The target parameter set is obtained based on the glove reinforcement area parameters, the glove size category parameters, and the target surface structure parameters of the target reinforcement area. The method according to claim 4, wherein: The surface structure parameters of the surface region include the protrusion structure parameters of the surface region, or the sandblasting reinforcement structure parameters, or a combination of the protrusion structure parameters and the sandblasting reinforcement structure parameters. The method according to any one of claims 1 to 5, wherein, The method involves collecting hand size data from various workers in the target work scenario, and then, based on the data distribution of the hand size data, obtaining the size category parameters of the low-voltage insulating gloves, including: The dimensional data corresponding to each hand dimension of each worker in the target work scenario are statistically analyzed, and the hand size data is obtained based on the dimensional data corresponding to each hand dimension; wherein, the hand dimensions include finger length, palm length, palm width, and palm thickness; Calculate the mean and standard deviation of the dimensional data for each of the aforementioned hand dimensions; Based on the average value and standard deviation of the dimensional data corresponding to each of the hand dimensions, the dimensional data range corresponding to each of the hand dimensions for each size category is determined; Based on the median value of the data in each dimension data interval corresponding to each size category, determine the target dimension parameter corresponding to each hand dimension for each size category; and, The size category parameters of the low-pressure insulating glove are obtained based on the target dimension parameters corresponding to each size category. A parameter determination device for a low-voltage insulating glove, wherein, The device includes: The acquisition module is used to acquire the operation behavior parameters and operation environment parameters of the target operation scenario; wherein, the operation behavior parameters include hand movement trajectory parameters and hand movement state parameters; The friction analysis module is used to input the work behavior parameters and the work environment parameters into a trained friction analysis model to obtain the friction analysis results of each hand area in the target work scenario output by the friction analysis model; wherein, the friction analysis model is trained using work parameter samples corresponding to the sample work scenario; the work parameter samples include work behavior parameter samples, work environment parameter samples, and friction data samples corresponding to the sample work scenario; The first parameter determination module is used to obtain the glove reinforcement area parameters corresponding to the target work scenario based on the friction analysis results of each of the hand areas. The second parameter determination module is used to collect hand size data of each worker in the target work scenario, and obtain the glove size category parameter corresponding to the target work scenario based on the data distribution of the hand size data. The parameter set determination module is used to obtain the target parameter set of the low-voltage insulating glove applied to the target operation scenario based on the glove reinforcement area parameters and the glove size category parameters. The apparatus according to claim 7, wherein, The trained friction analysis model is obtained through the following steps: Obtain the sample of operation parameters corresponding to the sample operation scenario; The operation behavior parameter sample and the operation environment parameter sample included in the operation parameter sample are input into a neural network model based on a multilayer perceptron to obtain the friction analysis results corresponding to the sample operation scenario output by the neural network model. Based on the difference between the friction analysis results corresponding to the sample operation scenario and the friction data samples included in the operation parameter sample, the model parameters of the neural network model are adjusted using a gradient descent-based optimization algorithm to obtain the trained friction analysis model. The apparatus according to claim 7, wherein, The device further includes: The state parameter acquisition module is used to acquire the acceleration parameters and angular velocity parameters of multiple first hand sampling points in the target work scene during the work process using an inertial sensor, and to obtain the hand motion state parameters based on the acceleration parameters and the angular velocity parameters. The trajectory parameter acquisition module is used to track the motion trajectory of multiple second hand sampling points during the operation using an optical tracking device, and obtain the hand motion trajectory parameters based on the motion trajectory. The behavior parameter acquisition module is used to obtain the operation behavior parameters based on the hand movement state parameters and the hand movement trajectory parameters. The apparatus according to claim 7, wherein, The operational parameter samples used to train the friction analysis model also include surface structure parameter samples and wear data samples of each surface region of the low-voltage insulating glove sample in the sample operational scenario; the operational parameters of the target operational scenario also include surface structure parameters of each surface region of the low-voltage insulating glove in the target operational scenario; the friction analysis results output by the friction analysis model for each hand region include friction force analysis data and wear analysis data of the hand region; the device also includes: The reinforcement area determination module is used to determine the target reinforcement area of ​​the low-voltage insulating glove in the target operation scenario based on the glove reinforcement area parameters corresponding to the target operation scenario. The structural parameter optimization module is used to iteratively optimize the surface structure parameters of the target reinforcement region using the friction analysis model when the friction analysis data and wear analysis data corresponding to the target reinforcement region do not meet the preset conditions, until the target surface structure parameters that make the friction analysis data and wear analysis data of the target reinforcement region meet the preset conditions are obtained, and then the iteration stops. The parameter set determination module is further configured to: obtain the target parameter set based on the glove reinforcement area parameters, the glove size category parameters, and the target surface structure parameters of the target reinforcement area. The apparatus according to claim 10, wherein, The surface structure parameters of the surface region include the protrusion structure parameters of the surface region, or the sandblasting reinforcement structure parameters, or a combination of the protrusion structure parameters and the sandblasting reinforcement structure parameters. The apparatus according to any one of claims 7 to 11, wherein, The second parameter determination module is also used for: The dimensional data corresponding to each hand dimension of each worker in the target work scenario are statistically analyzed, and the hand size data is obtained based on the dimensional data corresponding to each hand dimension; wherein, the hand dimensions include finger length, palm length, palm width, and palm thickness; Calculate the mean and standard deviation of the dimensional data for each of the aforementioned hand dimensions; Based on the average value and standard deviation of the dimensional data corresponding to each of the hand dimensions, the dimensional data range corresponding to each of the hand dimensions for each size category is determined; Based on the median value of the data in each dimension data interval corresponding to each size category, determine the target dimension parameter corresponding to each hand dimension for each size category; The size category parameters of the low-pressure insulating glove are obtained based on the target dimension parameters corresponding to each size category. A computer device includes a memory and a processor, wherein the memory stores a computer program, wherein When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6. A computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6. A computer program product includes a computer program, wherein, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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