Screwdriver service life prediction method and system based on artificial intelligence

Abstract

The application discloses a screwdriver service life prediction method and system based on artificial intelligence, and belongs to the technical field of computers. The technical scheme provided by the embodiment of the application obtains a first set of working parameters and a second set of working parameters of a target screwdriver in a current working mode. The first set of working parameters, the second set of working parameters and the screwdriver attributes of the target screwdriver are used to determine a first loss parameter for describing the loss degree of a motor and a second loss parameter for describing the loss degree of an accessory component, so as to realize the loss degree evaluation of the motor and the accessory component. Based on the first loss parameter, the second loss parameter, the current working mode and the initial loss parameter, the remaining service life of the target screwdriver is determined, the accuracy of the remaining service life is high, thereby providing an effective reference for the user, and facilitating the user to replace or maintain the electric screwdriver in time.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method and system for predicting the lifespan of a screwdriver based on artificial intelligence. Background Technology

[0002] Electric screwdrivers are key tools in industrial manufacturing and equipment maintenance, and their performance stability directly affects work efficiency and cost control. Traditional maintenance methods mostly rely on periodic inspections or replacing parts after a failure, making it difficult to proactively intervene in the early stages of equipment performance degradation.

[0003] In related technologies, some studies assess the condition of electric screwdrivers by monitoring single parameters such as motor current and speed, thereby estimating the remaining service life of the electric screwdriver.

[0004] However, the limited data used in related technologies leads to low accuracy in determining the remaining lifespan of electric screwdrivers. Therefore, a more accurate method for estimating the remaining lifespan of electric screwdrivers is needed to improve management precision and resource utilization. Summary of the Invention

[0005] This application provides a screwdriver lifespan prediction method and system based on artificial intelligence, which can more accurately determine the remaining lifespan of an electric screwdriver. The technical solution is as follows:

[0006] On the one hand, an artificial intelligence-based method for predicting the lifespan of a screwdriver is provided, the method comprising:

[0007] When the target screwdriver is in working condition, acquire a first set of working parameters and a second set of working parameters of the target screwdriver in the current working mode. The first set of working parameters includes the working output parameters of the target screwdriver, and the second set of working parameters includes the working feedback parameters of the target screwdriver. The current working mode is determined based on the working head type and working surface type of the working head installed on the target screwdriver. The target screwdriver is an electric screwdriver.

[0008] Based on the first set of working parameters, the second set of working parameters, and the screwdriver attributes of the target screwdriver, a first type of wear parameter and a second type of wear parameter of the target screwdriver are determined. The first type of wear parameter is used to describe the degree of motor wear of the target screwdriver, and the second type of wear parameter is used to describe the degree of wear of the auxiliary components of the target screwdriver.

[0009] Based on the first type of wear parameters, the second type of wear parameters, the current working mode, and the initial wear parameters of the target screwdriver, the remaining service life of the target screwdriver is determined.

[0010] In one possible implementation, acquiring the first set of working parameters and the second set of working parameters of the target screwdriver in the current working mode includes:

[0011] The motor speed, motor torque, and actual working torque of the target screwdriver in the current working mode are obtained to obtain the first type of working parameter set;

[0012] The motor temperature, vibration parameters, feedback pressure, working angle, and motor current of the target screwdriver in the current working mode are obtained to obtain the second type of working parameter set, wherein the feedback pressure is the pressure along the axial direction of the electric screwdriver.

[0013] In one possible implementation, determining the first type of wear parameters and the second type of wear parameters of the target screwdriver based on the first type of working parameter set, the second type of working parameter set, and the screwdriver attributes of the target screwdriver includes:

[0014] The first type of loss parameter is determined based on the motor speed, motor current, motor temperature, motor torque, feedback pressure, and motor attributes in the screwdriver attributes.

[0015] The second type of loss parameter is determined based on the motor torque, the actual working torque, the vibration parameters, and the auxiliary component attributes in the screwdriver properties.

[0016] In one possible implementation, determining the first type of loss parameters based on the motor speed, motor current, motor temperature, motor torque, feedback pressure, and motor attributes in the screwdriver attributes includes:

[0017] Based on the motor speed, the motor current, the motor temperature, and the motor torque, the first motor state parameters of the target screwdriver are determined;

[0018] Based on the motor speed, the motor torque, and the feedback pressure, the second motor state parameters of the target screwdriver are determined;

[0019] Based on the first motor state parameters, the second motor state parameters, and the motor attributes, the first type of loss parameters are determined.

[0020] In one possible implementation, determining the second type of loss parameter based on the motor torque, the actual operating torque, the vibration parameters, and the accessory component attributes in the screwdriver attributes includes:

[0021] Based on the motor torque and the actual working torque, determine the state parameters of the first auxiliary component of the target screwdriver;

[0022] Based on the actual working torque and the vibration parameters, determine the state parameters of the second auxiliary component of the target screwdriver;

[0023] The second type of loss parameter is determined based on the state parameters of the first auxiliary component, the state parameters of the second auxiliary component, and the attributes of the auxiliary component.

[0024] In one possible implementation, determining the remaining service life of the target screwdriver based on the first type of wear parameters, the second type of wear parameters, the current operating mode, and the initial wear parameters of the target screwdriver includes:

[0025] Based on the first type of loss parameters and the current working mode, the first reference loss level of the target screwdriver is determined;

[0026] Based on the second type of loss parameters and the current working mode, a second reference loss level of the target screwdriver is determined;

[0027] Based on the first reference wear level, the second reference wear level, and the initial wear parameter, the remaining service life of the target screwdriver is determined.

[0028] In one possible implementation, determining the first reference wear level of the target screwdriver based on the first type of wear parameters and the current operating mode includes:

[0029] A first loss correction parameter corresponding to the current working mode is determined, and the first loss correction parameter is used to correct the loss parameter corresponding to the motor; based on the first type of loss parameter and the first loss correction parameter, a first target loss parameter of the target screwdriver is determined; based on the first target loss parameter, a first reference loss degree of the target screwdriver is determined.

[0030] Determining the second reference wear level of the target screwdriver based on the second type of wear parameters and the current working mode includes:

[0031] A second wear correction parameter corresponding to the current working mode is determined, and the second wear correction parameter is used to correct the wear parameter corresponding to the auxiliary component; based on the second type of wear parameter and the second wear correction parameter, a second target wear parameter of the target screwdriver is determined; based on the second target wear parameter, a second reference wear level of the target screwdriver is determined.

[0032] In one possible implementation, determining the remaining service life of the target screwdriver based on the first reference wear level, the second reference wear level, and the initial wear parameter includes:

[0033] The first reference wear level and the second reference wear level are combined to obtain the third reference wear level of the target screwdriver;

[0034] Based on the third reference wear level and the initial wear parameters, the current wear parameters of the target screwdriver are obtained;

[0035] The remaining service life is determined based on the current loss parameters.

[0036] In one possible implementation, the method for determining the current operating mode includes:

[0037] Obtain the working head type and working surface type of the working head installed on the target screwdriver;

[0038] Based on the aforementioned working head type, multiple candidate working modes are determined;

[0039] Based on the working surface type, the current working mode is determined from the plurality of candidate working modes.

[0040] On the one hand, an artificial intelligence-based screwdriver lifespan prediction system is provided, the system comprising:

[0041] The acquisition unit is used to acquire a first set of working parameters and a second set of working parameters of the target screwdriver in the current working mode when the target screwdriver is in the working state. The first set of working parameters includes the working output parameters of the target screwdriver, and the second set of working parameters includes the working feedback parameters of the target screwdriver. The current working mode is determined based on the working head type and working surface type of the working head installed on the target screwdriver. The target screwdriver is an electric screwdriver.

[0042] The loss parameter determination unit is used to determine a first type of loss parameter and a second type of loss parameter of the target screwdriver based on the first type of working parameter set, the second type of working parameter set, and the screwdriver attributes of the target screwdriver. The first type of loss parameter is used to describe the degree of motor loss of the target screwdriver, and the second type of loss parameter is used to describe the degree of loss of the auxiliary components of the target screwdriver.

[0043] The remaining service life determination unit is used to determine the remaining service life of the target screwdriver based on the first type of wear parameters, the second type of wear parameters, the current working mode, and the initial wear parameters of the target screwdriver.

[0044] In one possible implementation, the acquisition unit is used to acquire the motor speed, motor torque, and actual working torque of the target screwdriver in the current working mode to obtain the first set of working parameters; and to acquire the motor temperature, vibration parameters, feedback pressure, working angle, and motor current of the target screwdriver in the current working mode to obtain the second set of working parameters, wherein the feedback pressure is the pressure along the axial direction of the electric screwdriver.

[0045] In one possible implementation, the loss parameter determination unit is used to determine the first type of loss parameters based on the motor speed, the motor current, the motor temperature, the motor torque, the feedback pressure, and the motor attributes in the screwdriver attributes; and to determine the second type of loss parameters based on the motor torque, the actual working torque, the vibration parameters, and the auxiliary component attributes in the screwdriver attributes.

[0046] In one possible implementation, the loss parameter determination unit is configured to determine a first motor state parameter of the target screwdriver based on the motor speed, the motor current, the motor temperature, and the motor torque; determine a second motor state parameter of the target screwdriver based on the motor speed, the motor torque, and the feedback pressure; and determine a first type of loss parameter based on the first motor state parameter, the second motor state parameter, and the motor attributes.

[0047] In one possible implementation, the loss parameter determination unit is configured to determine a first auxiliary component state parameter of the target screwdriver based on the motor torque and the actual working torque; determine a second auxiliary component state parameter of the target screwdriver based on the actual working torque and the vibration parameter; and determine a second type of loss parameter based on the first auxiliary component state parameter, the second auxiliary component state parameter, and the auxiliary component attributes.

[0048] In one possible implementation, the remaining service life determination unit is configured to determine a first reference wear level of the target screwdriver based on the first type of wear parameters and the current operating mode; determine a second reference wear level of the target screwdriver based on the second type of wear parameters and the current operating mode; and determine the remaining service life of the target screwdriver based on the first reference wear level, the second reference wear level, and the initial wear parameters.

[0049] In one possible implementation, the remaining service life determining unit is used to determine a first loss correction parameter corresponding to the current working mode, the first loss correction parameter being used to correct the loss parameter corresponding to the motor; based on the first type of loss parameter and the first loss correction parameter, a first target loss parameter of the target screwdriver is determined; based on the first target loss parameter, a first reference loss degree of the target screwdriver is determined.

[0050] The remaining service life determination unit is used to determine a second wear correction parameter corresponding to the current working mode. The second wear correction parameter is used to correct the wear parameter corresponding to the auxiliary component. Based on the second type of wear parameter and the second wear correction parameter, a second target wear parameter of the target screwdriver is determined. Based on the second target wear parameter, a second reference wear level of the target screwdriver is determined.

[0051] In one possible implementation, the remaining service life determination unit is configured to fuse the first reference wear level and the second reference wear level to obtain a third reference wear level of the target screwdriver; based on the third reference wear level and the initial wear parameter, obtain the current wear parameter of the target screwdriver; and based on the current wear parameter, determine the remaining service life.

[0052] In one possible implementation, the method for determining the current operating mode includes:

[0053] Obtain the working head type and working surface type of the working head installed on the target screwdriver;

[0054] Based on the aforementioned working head type, multiple candidate working modes are determined;

[0055] Based on the working surface type, the current working mode is determined from the plurality of candidate working modes.

[0056] On one hand, a computer device is provided, the computer device including one or more processors and one or more memories, the one or more memories storing at least one computer program, the computer program being loaded and executed by the one or more processors to implement the artificial intelligence-based screwdriver lifespan prediction method.

[0057] On one hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, the computer program being loaded and executed by a processor to implement the artificial intelligence-based screwdriver lifespan prediction method.

[0058] On the one hand, a computer program product or computer program is provided, which includes program code stored in a computer-readable storage medium. The processor of a computer device reads the program code from the computer-readable storage medium and executes the program code, causing the computer device to perform the above-mentioned artificial intelligence-based screwdriver life prediction method.

[0059] The technical solution provided in this application obtains a first set of working parameters and a second set of working parameters for the target screwdriver in its current working mode. Using these parameters, along with the screwdriver's attributes, a first set of wear parameters describing the degree of motor wear and a second set of wear parameters describing the degree of wear on auxiliary components are determined, thereby enabling an assessment of the wear levels of the motor and auxiliary components. Based on the first and second sets of wear parameters, the current working mode, and the initial wear parameters, the remaining service life of the target screwdriver is determined. The accuracy of the remaining service life is high, providing users with a useful reference and facilitating timely replacement or repair of the electric screwdriver. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic diagram of the implementation environment of an artificial intelligence-based screwdriver lifespan prediction method provided in an embodiment of this application;

[0062] Figure 2 This is a flowchart of a screwdriver lifespan prediction method based on artificial intelligence provided in an embodiment of this application;

[0063] Figure 3 This is a flowchart of another screwdriver lifespan prediction method based on artificial intelligence provided in an embodiment of this application;

[0064] Figure 4 This is a schematic diagram of the structure of an artificial intelligence-based screwdriver lifespan prediction device provided in an embodiment of this application;

[0065] Figure 5 This is a schematic diagram of the structure of a data processing unit provided in an embodiment of this application. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0067] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "nth," nor are there any restrictions on quantity or execution order.

[0068] Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain better results.

[0069] Machine learning (ML) is a multidisciplinary field involving probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers can simulate or implement human learning behavior to acquire new knowledge or skills, and reorganize existing knowledge sub-models to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to endow computers with intelligence; its applications span all areas of artificial intelligence.

[0070] An electric screwdriver, also known as an electric screwdriver, is a tool that uses electricity to tighten or loosen screws. It mainly consists of a motor, speed control mechanism, torque control device, handle, screwdriver bit, and power supply. The motor is the core power source, providing rotational power. The speed control mechanism adjusts the screwdriver's speed to suit different usage scenarios; for example, a higher speed may be needed to tighten small screws, while a lower speed may be needed to ensure sufficient torque for larger screws. The torque control device controls the amount of torque output by the screwdriver. The screwdriver bit (working head) matches the shape of the screw head; common types include slotted, Phillips, and hex heads, and can be changed according to the screw type. The power supply is usually a battery (rechargeable or disposable) or directly connected to AC power (requiring a power adapter).

[0071] In related technologies, the motor speed or current of an electric screwdriver is typically used to assess its condition and estimate its remaining lifespan. However, the remaining lifespan of an electric screwdriver is affected not only by the motor but also by other components. Therefore, the accuracy of estimating the remaining lifespan using the methods provided in these technologies is low, meaning there can be a significant discrepancy between the estimated and actual remaining lifespan of the screwdriver. Users are unlikely to trust the estimated lifespan, rendering the remaining lifespan estimation function useless.

[0072] The technical solution provided in this application can combine artificial intelligence technology to estimate the remaining service life of an electric screwdriver using multiple parameters. The accuracy of the estimated remaining service life is closer to the actual remaining service life of the electric screwdriver, thus providing users with a valid reference and facilitating timely replacement or repair of the electric screwdriver.

[0073] Figure 1 This is a schematic diagram illustrating the implementation environment of an artificial intelligence-based screwdriver lifespan prediction method provided in this application embodiment. See also... Figure 1 The implementation environment may include a data processing unit 101 and a data acquisition unit 102.

[0074] The data processing unit 101 is electrically connected to the data acquisition unit 102. The data processing unit 101 can acquire the data collected by the data acquisition unit 102, which refers to data related to the electric screwdriver. The data processing unit 101 has artificial intelligence-based data processing capabilities, so the data processing capability of the data processing unit 101 is strong. In some embodiments, the data processing unit 101 is an artificial intelligence processing chip.

[0075] The data acquisition unit 102 is electrically connected to multiple sensors in the electric screwdriver, and can acquire data through multiple sensors.

[0076] After introducing the implementation environment of the embodiments of this application, the application scenarios of the embodiments of this application will be described below.

[0077] The technical solution provided in this application can be applied to electric screwdrivers equipped with the aforementioned data processing unit 101 and data acquisition unit 102. Since the data processing unit 101 requires strong data processing capabilities, its cost is typically high. Therefore, the technical solution provided in this application is usually adopted in manufacturers' high-end product lines. Of course, with the development of science and technology, the price of artificial intelligence processing chips may continue to decrease. Provided that cost requirements are met, the technical solution provided in this application can also be applied to mid-range or even low-end product lines. This application does not limit this application in this regard.

[0078] The following describes the artificial intelligence-based screwdriver lifespan prediction method provided in the embodiments of this application. Figure 2 This is a flowchart of an artificial intelligence-based screwdriver lifespan prediction method provided in an embodiment of this application. See also... Figure 2 Taking the data processing unit as the executing entity as an example, the method includes the following steps.

[0079] 201. When the target screwdriver is in working condition, the data processing unit acquires a first set of working parameters and a second set of working parameters of the target screwdriver in the current working mode. The first set of working parameters includes the working output parameters of the target screwdriver, and the second set of working parameters includes the working feedback parameters of the target screwdriver. The current working mode is determined based on the working head type and working surface type of the working head installed on the target screwdriver. The target screwdriver is an electric screwdriver.

[0080] The target screwdriver being in a working state means that its motor is rotating and under load. The target screwdriver is an electric screwdriver equipped with a data processing unit and a data acquisition unit. Additionally, the electric screwdriver includes a motor and auxiliary components, which are components related to torque transmission. The target screwdriver has multiple working modes, each adapted to different working conditions; the current working mode is one of these modes. Both the first and second sets of working parameters include multiple working parameters. Output parameters refer to the parameters that affect the external environment during operation, while feedback parameters refer to the parameters collected from the interaction between the electric screwdriver and the external environment. The electric screwdriver can use different working heads to adapt to different working conditions. The working surface type refers to the type of working surface the electric screwdriver operates on.

[0081] 202. Based on the first set of working parameters, the second set of working parameters, and the screwdriver attributes of the target screwdriver, the data processing unit determines the first type of wear parameters and the second type of wear parameters of the target screwdriver. The first type of wear parameters are used to describe the degree of motor wear of the target screwdriver, and the second type of wear parameters are used to describe the degree of wear of the auxiliary components of the target screwdriver.

[0082] The screwdriver attributes refer to the hardware parameters of the target screwdriver. The first and second types of wear parameters describe the degree of wear on the motor and auxiliary components of the target screwdriver, respectively.

[0083] 203. The data processing unit determines the remaining service life of the target screwdriver based on the first type of loss parameter, the second type of loss parameter, the current working mode, and the initial loss parameter of the target screwdriver.

[0084] The initial wear parameter is the wear parameter of the target screwdriver when it leaves the factory, which can be regarded as the baseline of the wear degree of the target screwdriver.

[0085] The technical solution provided in this application obtains a first set of working parameters and a second set of working parameters for the target screwdriver in its current working mode. Using these parameters, along with the screwdriver's attributes, a first set of wear parameters describing the degree of motor wear and a second set of wear parameters describing the degree of wear on auxiliary components are determined, thereby enabling an assessment of the wear levels of the motor and auxiliary components. Based on the first and second sets of wear parameters, the current working mode, and the initial wear parameters, the remaining service life of the target screwdriver is determined. The accuracy of the remaining service life is high, providing users with a useful reference and facilitating timely replacement or repair of the electric screwdriver.

[0086] Steps 201-203 above are a brief introduction to the artificial intelligence-based screwdriver lifespan prediction method provided in the embodiments of this application. The following will provide a clearer explanation of the artificial intelligence-based screwdriver lifespan prediction method provided in the embodiments of this application, using some examples. See [link to relevant documentation]. Figure 3 Taking the data processing unit as the executing entity as an example, the method includes the following steps.

[0087] 301. When the target screwdriver is in working condition, the data processing unit acquires a first set of working parameters and a second set of working parameters of the target screwdriver in the current working mode. The first set of working parameters includes the working output parameters of the target screwdriver, and the second set of working parameters includes the working feedback parameters of the target screwdriver. The current working mode is determined based on the working head type and working surface type of the working head installed on the target screwdriver. The target screwdriver is an electric screwdriver.

[0088] The target screwdriver being in working condition means that its motor is rotating and under load. The target screwdriver is an electric screwdriver equipped with a data processing unit and a data acquisition unit. Additionally, the electric screwdriver includes a motor and auxiliary components. These auxiliary components are those related to torque transmission, such as a speed-changing mechanism, drive shaft, and torque control device. The target screwdriver has multiple working modes, each adapted to different working conditions; the current working mode is one of these modes. Both the first and second sets of working parameters include multiple working parameters. Output parameters refer to the parameters that affect the external environment during operation, while feedback parameters refer to the parameters collected from the interaction between the electric screwdriver and the external environment. The electric screwdriver can use different working heads to adapt to different working conditions. The working surface type refers to the type of working surface the electric screwdriver operates on.

[0089] In one possible implementation, when the target screwdriver is in operation, the data processing unit acquires the motor speed, motor torque, and actual working torque of the target screwdriver in the current operating mode to obtain the first set of operating parameters. The data processing unit also acquires the motor temperature, vibration parameters, feedback pressure, working angle, and motor current of the target screwdriver in the current operating mode to obtain the second set of operating parameters, where the feedback pressure is the pressure along the axial direction of the electric screwdriver.

[0090] The motor speed refers to the rotational speed of the motor of the target screwdriver. The motor torque is the output torque of the motor. The actual working torque is the torque output by the electric screwdriver to the outside. Since the motor torque is transmitted to the outside through the auxiliary components of the target screwdriver, there is torque loss during the transmission process. Therefore, the actual working torque is usually less than the motor torque. The motor temperature is the temperature of the motor when it is working. The vibration parameters are used to describe the vibration of the target screwdriver when it is working. The feedback pressure is the pressure along the axial direction of the electric screwdriver, which can be regarded as the pressure applied axially by the user holding the target screwdriver. The working angle is the angle between the target screwdriver and the working surface. The motor current is the current when the motor is working. It should be noted that the above-mentioned motor speed, motor torque, actual working torque, motor temperature, vibration parameters, feedback pressure, working angle, and motor current are all collections of data collected within a preset time period, not single data points. For example, taking motor speed as an example, motor speed includes multiple motor speeds collected within the preset time period. The preset time period is set by technicians according to the actual situation, and this application embodiment does not limit it. The reason for collecting data within a preset time period instead of single-point data is to avoid data spikes while displaying the trend of data changes, thereby making a more accurate judgment. In some embodiments, the vibration parameters are RMS (Root Mean Square) values ​​in the 10-500Hz frequency band instead of the original waveform data.

[0091] In some embodiments, the motor speed is acquired by the target screwdriver's data acquisition unit via a motor speed sensor; the motor torque is acquired by the data acquisition unit via a motor torque sensor; the actual working torque is acquired by the data acquisition unit via a working torque sensor; the motor temperature is acquired by the data acquisition unit via a temperature sensor on the motor; the vibration parameters are acquired by the data acquisition unit via a vibration sensor on the target screwdriver; the feedback pressure is acquired by the data acquisition unit via an axial pressure sensor on the target screwdriver; the working angle is acquired by the data acquisition unit via a gyroscope. Before the target screwdriver starts working, the data processing unit displays a first prompt text on the target screwdriver's screen, prompting the target screwdriver to be adjusted to a perpendicular position to the working surface to determine the initial angle of the target screwdriver, thereby determining the working angle subsequently; the motor current is acquired by the data acquisition unit via a current sensor on the motor. After acquiring the above-mentioned first type of working parameter set and second type of working parameter set, the data acquisition unit sends the first type of working parameter set and the second type of working parameter set to the data processing unit.

[0092] In some embodiments, the technical solution provided in this application requires the data processing unit to consume a lot of computing power. Frequent use will result in a short battery life of the target screwdriver, affecting the normal use of the target screwdriver. Therefore, the technical solution provided in this application is executed once after a preset time interval, or when the target screwdriver is under external power supply. The preset time interval is set by the technician or user according to the actual situation, such as one week or two weeks. This application does not limit this.

[0093] To provide a clearer explanation of the above implementation methods, the method for determining the current working mode in the above implementation methods will be described below.

[0094] In one possible implementation, the data processing unit acquires the head type and the surface type of the working head mounted on the target screwdriver. Based on the head type, the data processing unit determines multiple candidate working modes. Based on the surface type, the data processing unit determines the current working mode from the multiple candidate working modes.

[0095] The types of working heads include Phillips head, slotted head, starhead, pentagonal head, and hexagonal head. The working surface is the plane where the screw is located when the target screwdriver is used to install the screw. Working surface types include wood, metal, plastic, cement, and foam, etc. The target screwdriver will operate differently on different working surface types. Different working modes correspond to different control parameters; that is, the target screwdriver will operate in different states in different working modes to adapt to the corresponding working conditions. The working modes and corresponding control parameters are set by technicians according to actual conditions; this application embodiment does not limit this.

[0096] In this implementation, the current working mode is determined by the type of working head and the type of working surface. The current working mode matches the working conditions of the target screwdriver, and the determination of the current working mode does not need to be done manually, which is highly efficient.

[0097] For example, the data processing unit acquires the radio frequency signal of the working head mounted on the target screwdriver and determines the working head type based on the radio frequency signal. The data processing unit acquires an image of the working surface of the target screwdriver and determines the working surface type based on the image. The data processing unit uses this working head type to match multiple initial working modes, obtaining multiple candidate working modes, which are the initial working modes that match the working head type.

[0098] The screwdriver includes a radio frequency (RF) signal transmitting unit on its working head. When the working head is mounted on the target screwdriver, the target screwdriver powers the RF signal transmitting unit, allowing the RF signal to be received by the RF signal receiving unit on the target screwdriver. This process constitutes short-range communication. Different working surface types correspond to different materials, and different materials reflect light differently. The target screwdriver also includes a light-emitting unit that emits light onto the working surface before determining the current working mode. This allows the working surface type to be determined based on the acquired image of the working surface. Correspondingly, the target screwdriver also includes an image acquisition unit for acquiring images of the working surface.

[0099] For example, the data processing unit acquires the radio frequency (RF) signal of the working head mounted on the target screwdriver. The data processing unit parses the RF signal to obtain the working head identifier. Based on the working head identifier, the data processing unit determines the working head type. The data processing unit acquires an image of the working surface of the target screwdriver. The data processing unit inputs this working surface image into a working surface type recognition model, and extracts features from the working surface image using the model to obtain the working surface image features. The data processing unit uses the working surface type recognition model to perform fully connected and normalized operations on the working surface image features to obtain a probability set corresponding to the working surface image. This probability set includes multiple probabilities, each probability corresponding to a candidate working surface type. The data processing unit identifies the candidate working surface type with the highest probability among the multiple candidate working surface types as the working surface type of the target screwdriver. The data processing unit uses this working head type to match multiple initial working modes to obtain multiple candidate working modes, which are the initial working modes that match the working head type.

[0100] The working face recognition model is a multi-classification model that can determine the corresponding working face type based on the input working face image. The structure of the working face recognition model is not limited in this application embodiment.

[0101] 302. The data processing unit determines the first type of loss parameter based on the motor speed, motor current, motor temperature, motor torque, feedback pressure, and motor attributes in the screwdriver attributes. The first type of loss parameter is used to describe the degree of motor loss of the target screwdriver.

[0102] Among them, the screwdriver attribute is the hardware parameter of the target screwdriver, and correspondingly, the motor attribute is the hardware parameter of the motor, which is used to describe the motor's working capacity and normal working status.

[0103] In one possible implementation, the data processing unit determines first motor state parameters of the target screwdriver based on the motor speed, motor current, motor temperature, and motor torque. The data processing unit then determines second motor state parameters of the target screwdriver based on the motor speed, motor torque, and feedback pressure. Finally, the data processing unit determines a first type of loss parameter based on the first motor state parameters, the second motor state parameters, and the motor properties.

[0104] The first motor state parameter and the second motor state parameter are used to describe the working state of the motor in different aspects. The first motor state parameter focuses on describing the working state of the motor itself, while the second motor state parameter focuses on describing the working state of the motor under external feedback.

[0105] In this implementation, the first motor state parameters are determined using motor speed, motor current, motor temperature, and motor torque. The second motor state parameters are determined using motor speed, motor torque, and feedback pressure. Combining the first and second motor state parameters with the motor attributes, the first type of loss parameters are determined, exhibiting high accuracy.

[0106] To provide a clearer explanation of the above embodiments, the following description will be divided into several parts.

[0107] The first part, the data processing unit, determines the first motor state parameters of the target screwdriver based on the motor speed, motor current, motor temperature, and motor torque.

[0108] In one possible implementation, the data processing unit determines the motor's temperature state parameters based on the motor's rotational speed and temperature. The data processing unit also determines the motor's torque state parameters based on the motor's current and torque. Finally, the data processing unit fuses the motor's temperature and torque state parameters to obtain the first motor state parameters.

[0109] The motor speed, motor temperature, motor current, and motor torque are all data sets within a preset time period.

[0110] For example, the data processing unit encodes the motor speed and temperature based on an attention mechanism to obtain motor speed characteristics and motor temperature characteristics. The data processing unit fuses these motor speed and temperature characteristics to obtain temperature state characteristics. The data processing unit decodes these temperature state characteristics based on an attention mechanism to obtain temperature state parameters. Similarly, the data processing unit encodes the motor current and torque based on an attention mechanism to obtain motor current characteristics and motor torque characteristics. The data processing unit fuses these motor current and torque characteristics to obtain torque state characteristics. The data processing unit decodes these torque state characteristics based on an attention mechanism to obtain torque state parameters. Finally, the data processing unit performs a weighted fusion of the motor temperature state parameters and torque state parameters to obtain the first motor state parameters.

[0111] The weights corresponding to the temperature state parameter and the torque state parameter are set by technicians according to the actual situation, and this application embodiment does not limit this.

[0112] The second part, the data processing unit, determines the second motor state parameters of the target screwdriver based on the motor speed, the motor torque, and the feedback pressure.

[0113] In one possible implementation, the data processing unit determines the motor's load speed state parameters based on the motor's rotational speed and the feedback pressure. The data processing unit also determines the motor's load torque state parameters based on the motor's torque and the feedback pressure. Finally, the data processing unit fuses the motor's load speed state parameters and load torque state parameters to obtain the second motor state parameters.

[0114] The feedback pressure is a set of data within a preset time period.

[0115] For example, the data processing unit encodes the motor speed and the feedback pressure based on an attention mechanism to obtain motor speed characteristics and feedback pressure characteristics. The data processing unit fuses the motor speed characteristics and the feedback pressure characteristics to obtain load speed state characteristics. The data processing unit decodes the load speed state characteristics based on an attention mechanism to obtain load speed state parameters. The data processing unit encodes the feedback pressure and the motor torque based on an attention mechanism to obtain feedback pressure characteristics and motor torque characteristics. The data processing unit fuses the feedback pressure characteristics and the motor torque characteristics to obtain load torque state characteristics. The data processing unit decodes the load torque state characteristics based on an attention mechanism to obtain load torque state parameters. The data processing unit performs a weighted fusion of the motor load speed state parameters and the load torque state parameters to obtain the second motor state parameters.

[0116] The weights corresponding to the load speed state parameter and the load torque state parameter are set by technicians according to the actual situation, and this application embodiment does not limit this.

[0117] The third part, the data processing unit, determines the first type of loss parameter based on the first motor state parameter, the second motor state parameter, and the motor attribute.

[0118] In one possible implementation, the data processing unit fuses the first motor state parameters and the second motor state parameters to obtain the current motor state parameters. Based on the current motor state parameters and the motor attributes, the data processing unit determines the first type of loss parameter.

[0119] For example, the data processing unit performs a weighted fusion of the first motor state parameters and the second motor state parameters to obtain the current motor state parameters. The data processing unit then extracts features from the current motor state parameters and the motor attributes to obtain the current motor state features and motor attribute features. The data processing unit fuses the current motor state features and motor attribute features to obtain the first type of loss features of the motor. Finally, the data processing unit performs a fully connected and normalized operation on the first type of loss features to obtain the first type of loss parameters.

[0120] In some embodiments, the motor properties include the resistivity of the winding material, the remanence of the permanent magnet, and the coefficient of friction of the bearing.

[0121] 303. The data processing unit determines the second type of loss parameter based on the motor torque, the actual working torque, the vibration parameter, and the accessory component attribute in the screwdriver attribute. The second type of loss parameter is used to describe the degree of wear of the accessory components of the target screwdriver.

[0122] Among them, the auxiliary component attributes are the hardware parameters of the auxiliary component, which are used to describe the auxiliary component's working capabilities and normal working status.

[0123] In one possible implementation, the data processing unit determines a first accessory component state parameter of the target screwdriver based on the motor torque and the actual operating torque. The data processing unit then determines a second accessory component state parameter of the target screwdriver based on the actual operating torque and the vibration parameter. Finally, the data processing unit determines a second type of loss parameter based on the first accessory component state parameter, the second accessory component state parameter, and accessory component attributes.

[0124] The first and second auxiliary component state parameters describe the working state of the auxiliary component in different aspects. The first auxiliary component state parameter focuses on describing the working state of the auxiliary component itself, while the second auxiliary component state parameter focuses on describing the working state of the auxiliary component under external feedback. In some embodiments, the vibration parameters include triaxial vibration parameters.

[0125] In this implementation, the state parameters of the first auxiliary component are determined using the torque of the auxiliary component and the actual operating torque. The state parameters of the second auxiliary component are determined using the actual operating torque and the vibration parameters. By combining the state parameters of the first and second auxiliary components and the attributes of the auxiliary components, a second type of loss parameter is determined, which has high accuracy.

[0126] To provide a clearer explanation of the above embodiments, the following description will be divided into several parts.

[0127] The first part, the data processing unit, determines the state parameters of the first auxiliary component of the target screwdriver based on the motor torque and the actual working torque.

[0128] The motor torque and the actual working torque are both data sets within a preset time period.

[0129] In one possible implementation, the data processing unit determines the torque transmission ratio based on the motor torque and the actual operating torque. The data processing unit then determines a first torque transmission state parameter based on the motor torque and the torque transmission ratio. Finally, the data processing unit fuses the first torque transmission state parameter and the second torque transmission state parameter to obtain the state parameters of the first auxiliary component.

[0130] The torque transmission ratio is a set of data within the preset time period. The torque transmission ratio includes multiple transmission ratios, and each transmission ratio is the ratio between a motor torque and a corresponding actual working torque.

[0131] For example, the data processing unit determines the torque transmission ratio based on the motor torque and the actual operating torque. The data processing unit encodes the motor torque and the torque transmission ratio using an attention mechanism to obtain the motor torque characteristic and the transmission ratio characteristic. The data processing unit fuses the motor torque characteristic and the transmission ratio characteristic to obtain a first torque transmission state characteristic. The data processing unit decodes the first torque transmission state characteristic using an attention mechanism to obtain the first torque transmission state parameter. The data processing unit encodes the actual operating torque and the torque transmission ratio using an attention mechanism to obtain the actual operating torque characteristic and the transmission ratio characteristic. The data processing unit fuses the actual operating torque characteristic and the transmission ratio characteristic to obtain a second torque transmission state characteristic. The data processing unit decodes the second torque transmission state characteristic using an attention mechanism to obtain the second torque transmission state parameter. The data processing unit performs a weighted fusion of the first torque transmission state parameter and the second torque transmission state parameter to obtain the state parameter of the first auxiliary component.

[0132] The first torque transmission state parameter and the second torque transmission state parameter are set by technicians according to the actual situation, and this application embodiment does not limit this.

[0133] The second part, the data processing unit, determines the state parameters of the second auxiliary component of the target screwdriver based on the actual working torque and the vibration parameters.

[0134] In one possible implementation, the data processing unit determines the triaxial vibration intensity of the target screwdriver based on the vibration parameters. The data processing unit then determines the state parameters of the second auxiliary component of the target screwdriver based on the actual operating torque and the triaxial vibration intensity.

[0135] Among them, the triaxial vibration intensity is the data set within a preset time period.

[0136] For example, the data processing unit substitutes the vibration parameter into the first relational data to obtain the triaxial vibration intensity of the target screwdriver. The data processing unit encodes the triaxial vibration intensity and the actual working torque based on an attention mechanism to obtain the triaxial vibration characteristics of the triaxial vibration intensity and the actual working torque characteristics of the actual working torque. The data processing unit fuses the triaxial vibration characteristics and the actual working torque characteristics to obtain the state determination characteristics of the second auxiliary component. The data processing unit decodes the state determination characteristics of the second auxiliary component based on an attention mechanism to obtain the state parameters of the second auxiliary component.

[0137] The first relational data is used to represent the correspondence between vibration parameters and triaxial vibration intensity.

[0138] The third part, the data processing unit, determines the second type of loss parameter based on the state parameters of the first auxiliary component, the state parameters of the second auxiliary component, and the attributes of the auxiliary component.

[0139] In one possible implementation, the data processing unit fuses the state parameters of the first and second auxiliary components to obtain the current component state parameters of the auxiliary component. Based on the current component state parameters and the attributes of the auxiliary component, the data processing unit determines the second type of loss parameter.

[0140] The attributes of this auxiliary component include the gearbox reduction ratio, the spring stiffness of the impact mechanism, and the fatigue limit of the material of the bit connection.

[0141] For example, the data processing unit performs a weighted fusion of the state parameters of the first and second auxiliary components to obtain the current state parameters of the auxiliary component. The data processing unit then extracts features from the current state parameters and the attributes of the auxiliary component to obtain the current state features and attribute features of the auxiliary component. The data processing unit fuses the current state features and attribute features to obtain the second type of loss features of the auxiliary component. Finally, the data processing unit performs a fully connected and normalized operation on the first type of loss features to obtain the second type of loss parameters.

[0142] 304. The data processing unit determines the first reference wear level of the target screwdriver based on the first type of wear parameter and the current working mode.

[0143] In one possible implementation, the data processing unit determines a first loss correction parameter corresponding to the current operating mode, which is used to correct the corresponding loss parameters of the motor. Based on the first type of loss parameter and the first loss correction parameter, the data processing unit determines a first target loss parameter for the target screwdriver. Based on the first target loss parameter, the data processing unit determines a first reference loss level for the target screwdriver.

[0144] The first type of loss parameters determined by different working modes are different because the control parameters of the target screwdriver are different in different working modes. In order to eliminate this difference, the first type of loss parameters are corrected by the first loss correction parameter to minimize the influence of the working mode on the loss parameters.

[0145] For example, the data processing unit uses the current operating mode to perform a query and obtains the first loss correction parameter. The data processing unit multiplies the first type of loss parameter with the first loss correction parameter to obtain the first target loss parameter of the target screwdriver. The data processing unit substitutes the first target loss parameter into the second relational data to obtain the first reference loss level.

[0146] The second relationship data is used to represent the correspondence between the first target loss parameter and the first reference loss degree. The second relationship data is a function that is obtained by fitting multiple first sample loss parameters and the first sample loss degree corresponding to each first sample loss parameter.

[0147] 305. The data processing unit determines the second reference wear level of the target screwdriver based on the second type of wear parameter and the current working mode.

[0148] In one possible implementation, the data processing unit determines a second wear correction parameter corresponding to the current operating mode. This second wear correction parameter is used to correct the wear parameters corresponding to the auxiliary components. Based on the second type of wear parameter and the second wear correction parameter, the data processing unit determines a second target wear parameter for the target screwdriver. Based on the second target wear parameter, a second reference wear level for the target screwdriver is determined.

[0149] The second type of loss parameters determined by different working modes are different because the control parameters of the target screwdriver are different in different working modes. In order to eliminate this difference, the second type of loss parameters are corrected by the second loss correction parameters to minimize the influence of the working mode on the loss parameters.

[0150] For example, the data processing unit uses the current operating mode to perform a query and obtains the second loss correction parameter. The data processing unit multiplies the second type of loss parameter by the second loss correction parameter to obtain the first target loss parameter of the target screwdriver. The data processing unit substitutes the first target loss parameter into the third relational data to obtain the second reference loss level.

[0151] The third relationship data is used to represent the correspondence between the first target loss parameter and the second reference loss degree. The third relationship data is a function that is obtained by fitting multiple second sample loss parameters and the second sample loss degree corresponding to each second sample loss parameter.

[0152] 306. The data processing unit determines the remaining service life of the target screwdriver based on the first reference wear level, the second reference wear level, and the initial wear parameter.

[0153] The initial wear parameter is the wear parameter of the target screwdriver when it leaves the factory, which can be regarded as the baseline of the wear degree of the target screwdriver.

[0154] In one possible implementation, the data processing unit fuses the first reference wear level and the second reference wear level to obtain a third reference wear level for the target screwdriver. Based on the third reference wear level and the initial wear parameter, the data processing unit obtains the current wear parameter of the target screwdriver. Based on the current wear parameter, the data processing unit determines the remaining service life.

[0155] For example, the data processing unit fuses the first reference wear level and the second reference wear level to obtain the third reference wear level of the target screwdriver. Based on the initial wear parameter, the data processing unit determines the initial wear level of the target screwdriver. Based on the initial wear level and the third reference wear level, the data processing unit determines the current wear parameter of the target screwdriver. The data processing unit substitutes the current wear parameter into the fourth relational data to obtain the remaining service life.

[0156] For example, the data processing unit weights and fuses the first reference wear level and the second reference wear level to obtain the third reference wear level of the target screwdriver. The data processing unit substitutes the initial wear parameter into the fifth relational data to obtain the initial wear level of the target screwdriver. The data processing unit determines the current wear parameter of the target screwdriver as the ratio between the first difference between the third reference wear level and the initial wear level and the second difference between the preset wear level and the initial wear level. The data processing unit substitutes the current wear parameter into the fourth relational data to obtain the remaining service life.

[0157] The fourth relationship data represents the correspondence between the current loss parameter and the remaining service life. This fourth relationship data is a function obtained by fitting the current loss parameters of multiple samples to the remaining service life of each sample corresponding to its current loss parameter. The fifth relationship data represents the correspondence between the initial loss parameter and the initial loss level. This fifth relationship data is set by a technician according to the actual situation, and this application embodiment does not limit it.

[0158] Optionally, after step 306, once the remaining service life is determined, the data processing unit displays the remaining service life on the target screwdriver's screen for user viewing. Additionally, if the remaining service life is less than or equal to a preset remaining service life, the data processing unit triggers an alarm to remind the user to replace or repair the target screwdriver.

[0159] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0160] The technical solution provided in this application obtains a first set of working parameters and a second set of working parameters for the target screwdriver in its current working mode. Using these parameters, along with the screwdriver's attributes, a first set of wear parameters describing the degree of motor wear and a second set of wear parameters describing the degree of wear on auxiliary components are determined, thereby enabling an assessment of the wear levels of the motor and auxiliary components. Based on the first and second sets of wear parameters, the current working mode, and the initial wear parameters, the remaining service life of the target screwdriver is determined. The accuracy of the remaining service life is high, providing users with a useful reference and facilitating timely replacement or repair of the electric screwdriver.

[0161] Figure 4 This is a schematic diagram of an artificial intelligence-based screwdriver lifespan prediction system provided in an embodiment of this application. See also... Figure 4 The system includes: an acquisition unit 401, a loss parameter determination unit 402, and a remaining service life determination unit 403.

[0162] The acquisition unit 401 is used to acquire a first set of working parameters and a second set of working parameters of the target screwdriver in the current working mode when the target screwdriver is in the working state. The first set of working parameters includes the working output parameters of the target screwdriver, and the second set of working parameters includes the working feedback parameters of the target screwdriver. The current working mode is determined based on the working head type and working surface type of the working head installed on the target screwdriver. The target screwdriver is an electric screwdriver.

[0163] The loss parameter determination unit 402 is used to determine a first type of loss parameter and a second type of loss parameter of the target screwdriver based on the first type of working parameter set, the second type of working parameter set and the screwdriver attributes of the target screwdriver. The first type of loss parameter is used to describe the degree of motor loss of the target screwdriver, and the second type of loss parameter is used to describe the degree of loss of the auxiliary components of the target screwdriver.

[0164] The remaining service life determination unit 403 is used to determine the remaining service life of the target screwdriver based on the first type of wear parameter, the second type of wear parameter, the current working mode, and the initial wear parameter of the target screwdriver.

[0165] In one possible implementation, the acquisition unit 401 is used to acquire the motor speed, motor torque, and actual working torque of the target screwdriver in the current working mode, thereby obtaining the first set of working parameters. It also acquires the motor temperature, vibration parameters, feedback pressure, working angle, and motor current of the target screwdriver in the current working mode, thereby obtaining the second set of working parameters, wherein the feedback pressure is the pressure along the axial direction of the electric screwdriver.

[0166] In one possible implementation, the loss parameter determination unit 402 is used to determine the first type of loss parameters based on the motor speed, the motor current, the motor temperature, the motor torque, the feedback pressure, and the motor attributes in the screwdriver attributes. It also determines the second type of loss parameters based on the motor torque, the actual working torque, the vibration parameters, and the auxiliary component attributes in the screwdriver attributes.

[0167] In one possible implementation, the loss parameter determination unit 402 is used to determine first motor state parameters of the target screwdriver based on the motor speed, motor current, motor temperature, and motor torque. It then determines second motor state parameters of the target screwdriver based on the motor speed, motor torque, and feedback pressure. Finally, it determines a first type of loss parameter based on the first motor state parameters, the second motor state parameters, and the motor properties.

[0168] In one possible implementation, the loss parameter determination unit 402 is used to determine a first accessory component state parameter of the target screwdriver based on the motor torque and the actual operating torque. It then determines a second accessory component state parameter of the target screwdriver based on the actual operating torque and the vibration parameter. Finally, it determines a second type of loss parameter based on the first accessory component state parameter, the second accessory component state parameter, and accessory component properties.

[0169] In one possible implementation, the remaining service life determining unit 403 is configured to determine a first reference wear level of the target screwdriver based on the first type of wear parameter and the current operating mode; determine a second reference wear level of the target screwdriver based on the second type of wear parameter and the current operating mode; and determine the remaining service life of the target screwdriver based on the first reference wear level, the second reference wear level, and the initial wear parameter.

[0170] In one possible implementation, the remaining service life determining unit 403 is used to determine a first loss correction parameter corresponding to the current operating mode. This first loss correction parameter is used to correct the corresponding loss parameters of the motor. Based on the first type of loss parameter and the first loss correction parameter, a first target loss parameter for the target screwdriver is determined. Based on the first target loss parameter, a first reference loss level for the target screwdriver is determined.

[0171] The remaining service life determination unit 403 is used to determine a second wear correction parameter corresponding to the current operating mode. This second wear correction parameter is used to correct the wear parameters of the auxiliary components. Based on the second type of wear parameter and the second wear correction parameter, a second target wear parameter for the target screwdriver is determined. Based on the second target wear parameter, a second reference wear level for the target screwdriver is determined.

[0172] In one possible implementation, the remaining service life determining unit 403 is used to fuse the first reference wear level and the second reference wear level to obtain a third reference wear level for the target screwdriver. Based on the third reference wear level and the initial wear parameter, the current wear parameter of the target screwdriver is obtained. Based on the current wear parameter, the remaining service life is determined.

[0173] In one possible implementation, the method for determining the current operating mode includes:

[0174] Obtain the working head type and working surface type of the working head installed on the target screwdriver.

[0175] Based on this job head type, multiple candidate job modes are determined.

[0176] Based on the working surface type, the current working mode is determined from the multiple candidate working modes.

[0177] It should be noted that the AI-based screwdriver lifespan prediction system provided in the above embodiments is only illustrated by the division of the above functional modules when predicting lifespan. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the AI-based screwdriver lifespan prediction system and the AI-based screwdriver lifespan prediction method embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0178] The technical solution provided in this application obtains a first set of working parameters and a second set of working parameters for the target screwdriver in its current working mode. Using these parameters, along with the screwdriver's attributes, a first set of wear parameters describing the degree of motor wear and a second set of wear parameters describing the degree of wear on auxiliary components are determined, thereby enabling an assessment of the wear levels of the motor and auxiliary components. Based on the first and second sets of wear parameters, the current working mode, and the initial wear parameters, the remaining service life of the target screwdriver is determined. The accuracy of the remaining service life is high, providing users with a useful reference and facilitating timely replacement or repair of the electric screwdriver.

[0179] Figure 5 This is a schematic diagram of the structure of a data processing unit provided in an embodiment of this application. The data processing unit 500 can vary significantly due to different configurations or performance. It may include one or more processors (Central Processing Units, CPUs) 501 and one or more memories 502. The one or more memories 502 store at least one computer program, which is loaded and executed by the one or more processors 501 to implement the methods provided in the various method embodiments described above. Of course, the data processing unit 500 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The data processing unit 500 may also include other components for implementing device functions, which will not be elaborated upon here.

[0180] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including a computer program that can be executed by a processor to perform the artificial intelligence-based screwdriver lifespan prediction method in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0181] In an exemplary embodiment, a computer program product or computer program is also provided, which includes program code stored in a computer-readable storage medium. A processor of a computer device reads the program code from the computer-readable storage medium and executes the program code, causing the computer device to perform the above-described artificial intelligence-based screwdriver lifespan prediction method.

[0182] In some embodiments, the computer program involved in the present application embodiments may be deployed and executed on a computer device, or executed on multiple computer devices located in one location, or executed on multiple computer devices distributed in multiple locations and interconnected through a communication network. Multiple computer devices distributed in multiple locations and interconnected through a communication network may constitute a blockchain system.

[0183] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0184] The above are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting the lifespan of a screwdriver based on artificial intelligence, characterized in that, The method includes: With the target screwdriver in operation, the motor speed, motor torque, and actual working torque of the target screwdriver in the current operating mode are obtained to obtain a first set of operating parameters; the motor temperature, vibration parameters, feedback pressure, working angle, and motor current of the target screwdriver in the current operating mode are obtained to obtain a second set of operating parameters. The feedback pressure is the pressure along the axial direction of the electric screwdriver. The first set of operating parameters includes the working output parameters of the target screwdriver, and the second set of operating parameters includes the working feedback parameters of the target screwdriver. The current operating mode is determined based on the working head type and working surface type of the working head installed on the target screwdriver. The target screwdriver is an electric screwdriver. Based on the first set of working parameters, the second set of working parameters, and the screwdriver attributes of the target screwdriver, a first type of wear parameter and a second type of wear parameter of the target screwdriver are determined. The first type of wear parameter is used to describe the degree of motor wear of the target screwdriver, and the second type of wear parameter is used to describe the degree of wear of the auxiliary components of the target screwdriver. Based on the first type of wear parameters, the second type of wear parameters, the current working mode, and the initial wear parameters of the target screwdriver, the remaining service life of the target screwdriver is determined.

2. The method according to claim 1, characterized in that, The step of determining the first type of wear parameters and the second type of wear parameters of the target screwdriver based on the first type of working parameter set, the second type of working parameter set, and the screwdriver attributes of the target screwdriver includes: The first type of loss parameter is determined based on the motor speed, motor current, motor temperature, motor torque, feedback pressure, and motor attributes in the screwdriver attributes. The second type of loss parameter is determined based on the motor torque, the actual working torque, the vibration parameters, and the auxiliary component attributes in the screwdriver properties.

3. The method according to claim 2, characterized in that, The determination of the first type of loss parameters based on the motor speed, motor current, motor temperature, motor torque, feedback pressure, and motor attributes in the screwdriver attributes includes: Based on the motor speed, the motor current, the motor temperature, and the motor torque, the first motor state parameters of the target screwdriver are determined; Based on the motor speed, the motor torque, and the feedback pressure, the second motor state parameters of the target screwdriver are determined; Based on the first motor state parameters, the second motor state parameters, and the motor attributes, the first type of loss parameters are determined.

4. The method according to claim 2, characterized in that, The determination of the second type of loss parameters based on the motor torque, the actual working torque, the vibration parameters, and the auxiliary component attributes in the screwdriver attributes includes: Based on the motor torque and the actual working torque, determine the state parameters of the first auxiliary component of the target screwdriver; Based on the actual working torque and the vibration parameters, determine the state parameters of the second auxiliary component of the target screwdriver; The second type of loss parameter is determined based on the state parameters of the first auxiliary component, the state parameters of the second auxiliary component, and the attributes of the auxiliary component.

5. The method according to claim 1, characterized in that, The determination of the remaining service life of the target screwdriver based on the first type of wear parameters, the second type of wear parameters, the current working mode, and the initial wear parameters of the target screwdriver includes: Based on the first type of loss parameters and the current working mode, the first reference loss level of the target screwdriver is determined; Based on the second type of loss parameters and the current working mode, a second reference loss level of the target screwdriver is determined; Based on the first reference wear level, the second reference wear level, and the initial wear parameter, the remaining service life of the target screwdriver is determined.

6. The method according to claim 5, characterized in that, Determining the first reference wear level of the target screwdriver based on the first type of wear parameters and the current working mode includes: A first loss correction parameter corresponding to the current working mode is determined, and the first loss correction parameter is used to correct the loss parameter corresponding to the motor; based on the first type of loss parameter and the first loss correction parameter, a first target loss parameter of the target screwdriver is determined; based on the first target loss parameter, a first reference loss degree of the target screwdriver is determined. Determining the second reference wear level of the target screwdriver based on the second type of wear parameters and the current working mode includes: A second wear correction parameter corresponding to the current working mode is determined, and the second wear correction parameter is used to correct the wear parameter corresponding to the auxiliary component; based on the second type of wear parameter and the second wear correction parameter, a second target wear parameter of the target screwdriver is determined; based on the second target wear parameter, a second reference wear level of the target screwdriver is determined.

7. The method according to claim 5, characterized in that, Determining the remaining service life of the target screwdriver based on the first reference wear level, the second reference wear level, and the initial wear parameter includes: The first reference wear level and the second reference wear level are combined to obtain the third reference wear level of the target screwdriver; Based on the third reference wear level and the initial wear parameters, the current wear parameters of the target screwdriver are obtained; The remaining service life is determined based on the current loss parameters.

8. The method according to claim 1, characterized in that, The method for determining the current working mode includes: Obtain the working head type and working surface type of the working head installed on the target screwdriver; Based on the aforementioned working head type, multiple candidate working modes are determined; Based on the working surface type, the current working mode is determined from the plurality of candidate working modes.

9. A screwdriver lifespan prediction system based on artificial intelligence, characterized in that, The system includes: The acquisition unit is used to acquire, when the target screwdriver is in a working state, the motor speed, motor torque, and actual working torque of the target screwdriver in the current working mode to obtain a first set of working parameters; and to acquire, when the target screwdriver is in a working state, the motor temperature, vibration parameters, feedback pressure, working angle, and motor current of the target screwdriver in the current working mode to obtain a second set of working parameters. The feedback pressure is the pressure along the axial direction of the electric screwdriver. The first set of working parameters includes the working output parameters of the target screwdriver, and the second set of working parameters includes the working feedback parameters of the target screwdriver. The current working mode is determined based on the working head type and working surface type of the working head installed on the target screwdriver, and the target screwdriver is an electric screwdriver. The loss parameter determination unit is used to determine a first type of loss parameter and a second type of loss parameter of the target screwdriver based on the first type of working parameter set, the second type of working parameter set, and the screwdriver attributes of the target screwdriver. The first type of loss parameter is used to describe the degree of motor loss of the target screwdriver, and the second type of loss parameter is used to describe the degree of loss of the auxiliary components of the target screwdriver. The remaining service life determination unit is used to determine the remaining service life of the target screwdriver based on the first type of wear parameters, the second type of wear parameters, the current working mode, and the initial wear parameters of the target screwdriver.

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