Method and system for predicting service life of screwdriver based on artificial intelligence

Abstract

The invention discloses a screwdriver service life prediction method and system based on artificial intelligence, and belongs to the technical field of computers, and through the technical scheme provided by the embodiment of the invention, a first type working parameter set and a second type working parameter set of a target screwdriver in a current working mode are acquired. And determining a first type of loss parameters used for describing the loss degree of the motor and a second type of loss parameters used for describing the loss degree of the accessory component by using the first type of working parameter set, the second type of working parameter set and the screwdriver attribute of the target screwdriver, thereby realizing the loss degree evaluation of the motor and the accessory component. The remaining service life of the target screwdriver is determined based on the first type loss parameter, the second type loss parameter, the current working mode and the initial loss parameter, the accuracy of the remaining service life is high, and therefore effective reference is provided for a user, and the user can conveniently replace or maintain the electric screwdriver in time.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a method and system for predicting the service life of a screwdriver based on artificial intelligence. Background Art

[0002] As a key tool in fields such as industrial manufacturing and equipment maintenance, the performance stability of an electric screwdriver directly affects operation efficiency and cost control. Traditional maintenance methods mostly rely on regular inspections or component replacements after failures, and it is difficult to actively intervene at the initial stage of equipment performance decline.

[0003] In related technologies, some studies estimate the remaining service life of an electric screwdriver by monitoring single parameters such as motor current and rotation speed to evaluate the state of the electric screwdriver.

[0004] However, due to the relatively single data used in related technologies, the accuracy of determining the remaining service life of the electric screwdriver is low. Therefore, a method for more accurately estimating the remaining service life of the electric screwdriver is needed to improve the management accuracy and resource utilization rate of the electric screwdriver. Summary of the Invention

[0005] Embodiments of this application provide a method and system for predicting the service life of a screwdriver based on artificial intelligence, which can more accurately determine the remaining service life of an electric screwdriver. The technical solutions are as follows:

[0006] On the one hand, a method for predicting the service life of a screwdriver based on artificial intelligence is provided. The method includes:

[0007] When the target screwdriver is in a working state, obtain 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 type of the working head and the type of the working surface of the working head installed on the target screwdriver, and 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, determine a first type of loss parameter and a second type of loss parameter of the target screwdriver. The first type of loss parameter is used to describe the motor loss degree of the target screwdriver, and the second type of loss parameter is used to describe the loss degree of the accessory components of the target screwdriver;

[0009] Determine 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.

[0010] In a possible implementation manner, the obtaining the first type of working parameter set and the second type of working parameter set of the target screwdriver in the current working mode includes:

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

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

[0013] In a possible implementation manner, the determining the first type of loss parameter and the 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 attribute of the target screwdriver includes:

[0014] Determine the first type of loss parameter based on the motor speed, the motor current, the motor temperature, the motor torque, the feedback pressure, and the motor attribute in the screwdriver attribute;

[0015] Determine 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.

[0016] In a possible implementation manner, the determining the first type of loss parameter based on the motor speed, the motor current, the motor temperature, the motor torque, the feedback pressure, and the motor attribute in the screwdriver attribute includes:

[0017] Determine the first motor state parameter of the target screwdriver based on the motor speed, the motor current, the motor temperature, and the motor torque;

[0018] Determine the second motor state parameter of the target screwdriver based on the motor speed, the motor torque, and the feedback pressure;

[0019] Determine the first type of loss parameter based on the first motor state parameter, the second motor state parameter, and the motor attribute.

[0020] In a possible implementation, determining the second type of loss parameter based on the motor torque, the actual working torque, the vibration parameter, and the accessory component attribute among the screwdriver attributes includes:

[0021] Determining a first accessory component state parameter of the target screwdriver based on the motor torque and the actual working torque;

[0022] Determining a second accessory component state parameter of the target screwdriver based on the actual working torque and the vibration parameter;

[0023] Determining the second type of loss parameter based on the first accessory component state parameter, the second accessory component state parameter, and the accessory component attribute.

[0024] In a possible implementation, determining 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 includes:

[0025] Determining a first reference loss degree of the target screwdriver based on the first type of loss parameter and the current working mode;

[0026] Determining a second reference loss degree of the target screwdriver based on the second type of loss parameter and the current working mode;

[0027] Determining the remaining service life of the target screwdriver based on the first reference loss degree, the second reference loss degree, and the initial loss parameter.

[0028] In a possible implementation, determining the first reference loss degree of the target screwdriver based on the first type of loss parameter and the current working mode includes:

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

[0030] The determining the second reference loss degree of the target screwdriver based on the second type of loss parameter and the current working mode includes:

[0031] Determine a second loss correction parameter corresponding to the current working mode, where the second loss correction parameter is used to correct the loss parameter corresponding to the accessory component; based on the second type of loss parameter and the second loss correction parameter, determine the second target loss parameter of the target screwdriver; based on the second target loss parameter, determine the second reference loss degree of the target screwdriver.

[0032] In a possible implementation manner, the determining the remaining service life of the target screwdriver based on the first reference loss degree, the second reference loss degree, and the initial loss parameter includes:

[0033] Fuse the first reference loss degree and the second reference loss degree to obtain a third reference loss degree of the target screwdriver;

[0034] Based on the third reference loss degree and the initial loss parameter, obtain the current loss parameter of the target screwdriver;

[0035] Based on the current loss parameter, determine the remaining service life.

[0036] In a possible implementation manner, the method for determining the current working mode includes:

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

[0038] Based on the tool bit type, determine a plurality of candidate working modes;

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

[0040] On the one hand, a system for predicting the service life of a screwdriver based on artificial intelligence is provided, and the system includes:

[0041] An acquisition unit, configured to, when the target screwdriver is in a working state, acquire a first type of working parameter set and a second type of working parameter set of the target screwdriver in the current working mode, where the first type of working parameter set includes the working output parameter of the target screwdriver, and the second type of working parameter set includes the working feedback parameter of the target screwdriver, the current working mode is determined based on the tool bit type of the tool bit installed on the target screwdriver and the working surface type, and the target screwdriver is an electric screwdriver;

[0042] A loss parameter determination unit, configured 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, where the first type of loss parameter is used to describe the motor loss degree of the target screwdriver, and the second type of loss parameter is used to describe the loss degree of the accessory components of the target screwdriver;

[0043] A remaining service life determination unit, configured to determine 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.

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

[0045] In a possible implementation manner, the loss parameter determination unit is configured to determine the first type of loss parameter 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; determine the second type of loss parameter based on the motor torque, the actual working torque, the vibration parameter, and the accessory component attributes in the screwdriver attributes.

[0046] In a possible implementation manner, 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; determine the first type of loss parameter based on the first motor state parameter, the second motor state parameter, and the motor attributes.

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

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

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

[0050] The remaining service life determination unit is configured to determine a second wear correction parameter corresponding to the current working mode, where the second wear correction parameter is used to correct the wear parameter corresponding to the accessory component; determine a second target wear parameter of the target screwdriver based on the second type of wear parameter and the second wear correction parameter; and determine the second reference wear degree of the target screwdriver based on the second target wear parameter.

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

[0052] In a possible implementation, the method for determining the current working mode includes:

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

[0054] Determine a plurality of candidate working modes based on the tool bit type;

[0055] Determine the current working mode from the plurality of candidate working modes based on the working surface type.

[0056] On the one hand, a computer device is provided, which includes one or more processors and one or more memories. At least one computer program is stored in the one or more memories, and the computer program is loaded and executed by the one or more processors to implement the method for predicting the service life of a screwdriver based on artificial intelligence.

[0057] On the one hand, a computer-readable storage medium is provided, in which at least one computer program is stored, and the computer program is loaded and executed by a processor to implement the method for predicting the service life of a screwdriver based on artificial intelligence.

[0058] On the one hand, a computer program product or a computer program is provided. The computer program product or the computer program includes program code, the program code is stored in a computer-readable storage medium, a processor of a computer device reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the computer device executes the above-mentioned method for predicting the service life of a screwdriver based on artificial intelligence.

[0059] Through the technical solution provided by the embodiments of the present application, a first set of working parameters and a second set of working parameters of a target screwdriver in the current working mode are obtained. By using the first set of working parameters, the second set of working parameters and the screwdriver attributes of the target screwdriver, a first type of loss parameter for describing the degree of motor loss and a second type of loss parameter for describing the degree of loss of accessory components are determined, so as to realize the evaluation of the loss degree of the motor and accessory components. Based on the first type of loss parameter, the second type of loss parameter, the current working mode and the initial loss parameter, the remaining service life of the target screwdriver is determined, and the accuracy of the remaining service life is relatively high, so as to provide an effective reference for users and facilitate users to replace or repair the electric screwdriver in time. Description of the Drawings

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0061] Figure 1 It is a schematic diagram of the implementation environment of a method for predicting the service life of a screwdriver based on artificial intelligence provided by the embodiments of the present application;

[0062] Figure 2 It is a flowchart of a method for predicting the service life of a screwdriver based on artificial intelligence provided by the embodiments of the present application;

[0063] Figure 3 It is a flowchart of another method for predicting the service life of a screwdriver based on artificial intelligence provided by an embodiment of the present application;

[0064] Figure 4 It is a schematic structural diagram of a device for predicting the service life of a screwdriver based on artificial intelligence provided by an embodiment of the present application;

[0065] Figure 5 It is a schematic structural diagram of a data processing unit provided by an embodiment of the present application. Detailed implementation manners

[0066] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0067] In the present application, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and effects. It should be understood that there is no logical or chronological dependency between "first", "second", and "nth", nor are the quantity and execution order limited.

[0068] Artificial Intelligence (AI) is a 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 an interdisciplinary subject involving multiple fields such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge sub-models to continuously improve their own performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent, and its applications cover all fields of artificial intelligence.

[0070] Electric screwdriver: Also known as an electric drill, it is a tool that uses electricity as a power source to tighten or loosen screws. It mainly consists of a motor, a speed-changing mechanism, a torque control device, a handle, a bit, and a power source, etc. Motor: It is the power core of the electric screwdriver, providing rotational power. Speed-changing mechanism: It can adjust the rotation speed of the screwdriver to adapt to different usage scenarios. For example, a higher rotation speed may be required when tightening small screws, while a lower rotation speed may be needed for larger screws to ensure sufficient torque. Torque control device: It is used to control the torque output of the screwdriver. Bit (working head): It matches the shape of the screw head. Common types of bits include flat, Phillips, and hexagon bits, etc., and can be replaced according to the screw type. Power source: Usually a battery (rechargeable battery or disposable battery) or directly connected to the mains power (a power adapter is required).

[0071] In the related art, the motor speed or motor current of the electric screwdriver is usually used to evaluate the state of the electric screwdriver, so as to estimate the remaining service life of the electric screwdriver. However, the remaining service life of the electric screwdriver is affected not only by the motor but also by other accessory components. The accuracy of estimating the remaining service life of the electric screwdriver using the technical solutions provided in the related art is relatively low, that is, there is a large deviation between the estimated remaining service life and the actual remaining service life of the electric screwdriver. Users will not believe the estimated remaining service life, resulting in the function of estimating the remaining service life becoming a mere formality.

[0072] By adopting the technical solution provided in the embodiment of the present application, it is possible to utilize artificial intelligence technology to estimate the remaining service life of the electric screwdriver by using multiple parameters. The accuracy of the estimated remaining service life is closer to the actual remaining service life of the electric screwdriver, thereby providing an effective reference for users and facilitating users to replace or repair the electric screwdriver in a timely manner.

[0073] Figure 1 It is a schematic diagram of the implementation environment of a method for predicting the service life of a screwdriver based on artificial intelligence provided by an embodiment of the present application. Refer to Figure 1 In this implementation environment, it 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 obtain the data collected by the data acquisition unit 102. The data refers to data related to the electric screwdriver. The data processing unit 101 has data processing capabilities based on artificial intelligence. Therefore, the data processing capabilities of the data processing unit 101 are relatively 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 obtain data through the multiple sensors.

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

[0077] The technical solution provided by the embodiments of the present application can be applied to an electric screwdriver configured with the above data processing unit 101 and data acquisition unit 102. Since the data processing unit 101 needs to have strong data processing capabilities, the cost of the data processing unit 101 is usually high. Therefore, the technical solution provided by the embodiments of the present application is usually adopted in the high-end product lines of manufacturers. Of course, with the development of science and technology, the price of artificial intelligence processing chips may continue to decline. On the premise of meeting the cost requirements, the technical solution provided by the embodiments of the present application can also be extended to mid-range or even low-end product lines. The embodiments of the present application do not limit this.

[0078] The following describes the method for predicting the service life of a screwdriver based on artificial intelligence provided by the embodiments of the present application. Figure 2 is a flowchart of a method for predicting the service life of a screwdriver based on artificial intelligence provided by the embodiments of the present application. Refer to Figure 2 , taking the data processing unit as the execution subject, the method includes the following steps.

[0079] 201. When the target screwdriver is in a working state, 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 the working surface type of the working head installed on the target screwdriver, and the target screwdriver is an electric screwdriver.

[0080] Among them, the target screwdriver being in the working state means that the motor of the target screwdriver rotates and there is a load. The target screwdriver is an electric screwdriver configured with a data processing unit and a data acquisition unit. In addition, the electric screwdriver further includes a motor and accessory components, and the accessory components refer to the components related to torque transmission in the electric screwdriver. The target screwdriver has multiple working modes, and different working modes are adapted to different working conditions, and the current working mode is one of the multiple working modes. The first type of working parameter set and the second type of working parameter set each include multiple working parameters. The working output parameter refers to the working parameter that can affect the outside world when the electric screwdriver is working, and the working feedback parameter refers to the working parameter collected when the electric screwdriver interacts with the outside world during work. The electric screwdriver can replace different working heads to adapt to different working conditions. The working surface type refers to the type of the working surface on which the electric screwdriver acts during work.

[0081] 202. The data processing unit determines the first type of loss parameter and the 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 attribute of the target screwdriver. The first type of loss parameter is used to describe the loss degree of the motor of the target screwdriver, and the second type of loss parameter is used to describe the loss degree of the accessory components of the target screwdriver.

[0082] Among them, the screwdriver attribute is the hardware parameter of the target screwdriver. The first type of loss parameter and the second type of loss parameter are respectively used to describe the loss degrees of the motor and the accessory components of the target screwdriver.

[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] Among them, the initial loss parameter is the loss parameter of the target screwdriver when it leaves the factory, and can be regarded as the baseline of the loss degree of the target screwdriver.

[0085] Through the technical solution provided by the embodiments of the present application, the first type of working parameter set and the second type of working parameter set of the target screwdriver in the current working mode are obtained. Using the first type of working parameter set, the second type of working parameter set, and the screwdriver attribute of the target screwdriver, the first type of loss parameter used to describe the loss degree of the motor and the second type of loss parameter used to describe the loss degree of the accessory components are determined, so as to realize the evaluation of the loss degrees of the motor and the accessory components. Based on the first type of loss parameter, the second type of loss parameter, the current working mode, and the initial loss parameter, the remaining service life of the target screwdriver is determined, and the accuracy of the remaining service life is relatively high, thus providing an effective reference for users and facilitating users to replace or repair the electric screwdriver in a timely manner.

[0086] The above steps 201-203 are a brief introduction to the artificial intelligence-based screwdriver service life prediction method provided by the embodiments of the present application. Below, some examples will be combined to more clearly illustrate the artificial intelligence-based screwdriver service life prediction method provided by the embodiments of the present application. See Figure 3 , taking the execution entity as the data processing unit as an example, the method includes the following steps.

[0087] 301. When the target screwdriver is in a working state, 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 the working surface type of the working head installed on the target screwdriver. The target screwdriver is an electric screwdriver.

[0088] Among them, the target screwdriver being in a working state means that the motor of the target screwdriver is rotating and there is a load. The target screwdriver is an electric screwdriver configured with a data processing unit and a data acquisition unit. In addition, the electric screwdriver further includes a motor and accessory components. The accessory components refer to the components related to torque transmission in the electric screwdriver. For example, the accessory components include a speed change mechanism, a transmission shaft, and a torque control device, etc. The target screwdriver has multiple working modes, and different working modes are adapted to different working conditions. The current working mode is one of the multiple working modes. The first set of working parameters and the second set of working parameters each include multiple working parameters. The working output parameters refer to the working parameters that can affect the outside world when the electric screwdriver is working, and the working feedback parameters refer to the working parameters collected when the electric screwdriver interacts with the outside world during work. The electric screwdriver can replace different working heads to adapt to different working conditions. The working surface type refers to the type of the working surface on which the electric screwdriver acts during work.

[0089] In a possible implementation manner, when the target screwdriver is in a working state, the data processing unit acquires 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. The data processing unit acquires 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. The feedback pressure is the pressure along the axial direction of the electric screwdriver.

[0090] Among them, the motor speed is the rotational speed of the motor of the target screwdriver. The motor torque is the output torque of the motor, and the actual working torque is the torque output by the electric screwdriver to the outside world. Since the motor torque will be transmitted to the outside through the accessory components of the target screwdriver and there is torque loss during the transmission process, the actual working torque is usually less than the motor torque. The motor temperature is the temperature when the motor is working, and the vibration parameter is used to describe the vibration condition when the target screwdriver is working. The feedback pressure is the pressure along the axis of the electric screwdriver, which can be regarded as the pressure applied by the user holding the target screwdriver axially. 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 motor speed, motor torque, actual working torque, motor temperature, vibration parameter, feedback pressure, working angle, and motor current are all sets of data collected within a preset time period, not single data. For example, taking the motor speed as an example, the motor speed includes multiple rotational speeds of the motor collected within a preset time period. The preset time period is set by technicians according to the actual situation, and the embodiments of the present application do not limit this. The reason for collecting data within a preset time period instead of single-point data is to avoid data spikes and at the same time show the trend of data changes, so as to make a more accurate judgment. In some embodiments, the vibration parameter uses the RMS (Root Mean Square) value in the 10 - 500 Hz frequency band instead of the original waveform data.

[0091] In some embodiments, the motor speed is collected by the data acquisition unit of the target screwdriver through a motor speed sensor; the motor torque is collected by the data acquisition unit through a motor torque sensor; the actual working torque is collected by the data acquisition unit through a working torque sensor; the motor temperature is collected by the data acquisition unit through a temperature sensor on the motor; the vibration parameter is collected by the data acquisition unit through a vibration sensor on the target screwdriver; the feedback pressure is collected by the data acquisition unit through an axial pressure sensor of the target screwdriver; the working angle is collected by the data acquisition unit through a gyroscope. Before the target screwdriver starts working, the data processing unit will display a first prompt text on the screen of the target screwdriver. The first prompt text is used to prompt to adjust the target screwdriver to a state perpendicular to the working surface so as to determine the initial angle of the target screwdriver, thereby determining the working angle subsequently; the motor current is collected by the data acquisition unit through a current sensor of the motor. After the data acquisition unit obtains the first type of working parameter set and the second type of working parameter set, it 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 by the embodiments of the present application requires the data processing unit to consume a large amount of computing power. Frequent use will cause the battery life of the target screwdriver to be too short, affecting the normal use of the target screwdriver. Therefore, the technical solution provided by the embodiments of the present application is executed once after a preset time interval, or executed when the target screwdriver is externally powered. The preset time interval is set by technicians or users according to the actual situation, such as set to one week or two weeks, etc. The embodiments of the present application do not limit this.

[0093] To illustrate the above embodiments more clearly, the method for determining the current working mode in the above embodiments will be described below.

[0094] In a possible embodiment, the data processing unit obtains the type of the working head installed on the target screwdriver and the type of the working surface of the working surface. The data processing unit determines a plurality of candidate working modes based on the type of the working head. The data processing unit determines the current working mode from the plurality of candidate working modes based on the type of the working surface.

[0095] Among them, the types of working heads include cross, flat, flower, internal pentagon, and internal hexagon, etc. The working surface is the plane where the screw is located when using the target screwdriver to install the screw. The types of working surfaces include wood, metal, plastic, cement, and foam, etc. The situation of the target screwdriver working under different types of working surfaces is different. Different working modes correspond to different control parameters, that is, the target screwdriver will work in different states under different working modes to adapt to the working conditions corresponding to the working mode. The working mode and the control parameters corresponding to the working mode are set by technicians according to the actual situation. The embodiments of the present application do not limit this.

[0096] In this embodiment, the current working mode is determined by using the type of the working head and the type of the 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 manual, and the efficiency is high.

[0097] For example, the data processing unit obtains the radio frequency signal of the working head installed on the target screwdriver, and determines the type of the working head based on the radio frequency signal. The data processing unit obtains the working surface image of the working surface of the target screwdriver, and determines the type of the working surface based on the working surface image. The data processing unit uses the type of the working head to match among a plurality of initial working modes to obtain the plurality of candidate working modes, and the plurality of candidate working modes are initial working modes matching the type of the working head.

[0098] Among them, there is a radio frequency signal transmitting unit on the working head. When the working head is installed on the target screwdriver, the target screwdriver can supply power to the radio frequency signal transmitting unit, so as to receive radio frequency signals through the radio frequency signal receiving unit on the target screwdriver. This process is a short-distance communication process. Different working surface types correspond to different materials, and different materials have differences in the reflection of light. The target screwdriver also includes a light emitting unit that emits light towards the working surface before determining the current working mode, so that the working surface type can be determined based on the collected working surface image. Correspondingly, the target screwdriver also includes an image acquisition unit for acquiring the working surface image.

[0099] For example, the data processing unit obtains the radio frequency signal of the working head installed on the target screwdriver. The data processing unit analyzes the radio frequency signal to obtain the working head identifier of the working head. The data processing unit determines the working head type of the working head based on the working head identifier. The data processing unit obtains the working surface image of the working surface of the target screwdriver. The data processing unit inputs the working surface image into the working surface type recognition model, and extracts features from the working surface image through the working surface type recognition model to obtain the working surface image features of the working surface image. The data processing unit performs full connection and normalization on the working surface image features through the working surface type recognition model to obtain a probability set corresponding to the working surface image. The probability set includes multiple probabilities, and one probability corresponds to one candidate working surface type. The data processing unit determines the candidate working surface type with the highest corresponding probability among the multiple candidate working surface types as the working surface type of the working surface. The data processing unit matches the working head type in multiple initial working modes to obtain the multiple candidate working modes, and the multiple candidate working modes are the initial working modes matching the working head type.

[0100] Among them, the working surface recognition model is a multi-classification model that can determine the corresponding working surface type according to the input working surface image. The structure of the working surface recognition model in the embodiments of the present application is not limited.

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

[0102] Among them, the screwdriver attribute is the hardware parameter of the target screwdriver. Correspondingly, the motor attribute is the hardware parameter of the motor, which is used to describe the working ability and normal working state of the motor.

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

[0104] Among them, the first motor state parameter and the second motor state parameter are used to describe the working states of the motor in different aspects. The first motor state parameter focuses on describing the working state of the motor itself, and 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 parameter is determined by using the motor speed, the motor current, the motor temperature, and the motor torque. The second motor state parameter is determined by using the motor speed, the motor torque, and the feedback pressure. By combining the first motor state parameter, the second motor state parameter, and the motor attributes, the first type of loss parameter is determined, and the accuracy of the first type of loss parameter is relatively high.

[0106] To illustrate the above implementation more clearly, the above implementation will be described in several parts below.

[0107] Part 1: The data processing unit determines a first motor state parameter of the target screwdriver based on the motor speed, the motor current, the motor temperature, and the motor torque.

[0108] In a possible implementation, the data processing unit determines a temperature state parameter of the motor based on the motor speed and the motor temperature. The data processing unit determines a torque state parameter of the motor based on the motor current and the motor torque. The data processing unit fuses the temperature state parameter and the torque state parameter of the motor to obtain the first motor state parameter.

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

[0110] For example, the data processing unit encodes the motor speed and the motor temperature based on the attention mechanism, and obtains the motor speed feature of the motor speed and the motor temperature feature of the motor temperature. The data processing unit fuses the motor speed feature and the motor temperature feature to obtain the temperature state feature. The data processing unit decodes the temperature state feature based on the attention mechanism to obtain the temperature state parameter. The data processing unit encodes the motor current and the motor torque based on the attention mechanism, and obtains the motor current feature of the motor current and the motor torque feature of the motor torque. The data processing unit fuses the motor current feature and the motor torque feature to obtain the torque state feature. The data processing unit decodes the torque state feature based on the attention mechanism to obtain the torque state parameter. The data processing unit performs weighted fusion on the temperature state parameter and the torque state parameter of the motor to obtain the first motor state parameter.

[0111] Wherein, the weights corresponding to the temperature state parameter and the torque state parameter are set by technicians according to actual situations, and the embodiments of the present application do not limit this.

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

[0113] In a possible implementation manner, the data processing unit determines the load speed state parameter of the motor based on the motor speed and the feedback pressure. The data processing unit determines the load torque state parameter of the motor based on the motor torque and the feedback pressure. The data processing unit fuses the load speed state parameter and the load torque state parameter of the motor to obtain the second motor state parameter.

[0114] Wherein, the feedback pressure is a data set within a preset time period.

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

[0116] Among them, 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 the embodiments of the present application do not limit this.

[0117] In 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 a possible implementation manner, the data processing unit fuses the first motor state parameter and the second motor state parameter to obtain the current motor state parameter of the motor. The data processing unit determines the first type of loss parameter based on the current motor state parameter and the motor attribute.

[0119] For example, the data processing unit performs weighted fusion on the first motor state parameter and the second motor state parameter to obtain the current motor state parameter of the motor. The data processing unit extracts features from the current motor state parameter and the motor attribute to obtain the current motor state feature of the motor and the motor attribute feature. The data processing unit fuses the current motor state feature and the motor attribute feature to obtain the first type of loss feature of the motor. The data processing unit performs full connection and normalization on the first type of loss feature to obtain the first type of loss parameter.

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

[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 accessory component loss degree of the target screwdriver.

[0122] Among them, the accessory component attribute is the hardware parameter of the accessory component, which is used to describe the working ability and normal working state of the accessory component.

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

[0124] Among them, the first accessory component state parameter and the second accessory component state parameter are used to describe the working states of the accessory component in different aspects. The first accessory component state parameter focuses on describing the working state of the accessory component itself, and the second accessory component state parameter focuses on describing the working state of the accessory component under external feedback. In some embodiments, the vibration parameter includes triaxial vibration parameters.

[0125] In this implementation manner, the first accessory component state parameter is determined by using the accessory component torque and the actual working torque. The second accessory component state parameter is determined by using the actual working torque and the vibration parameter. The second type of loss parameter is determined by combining the first accessory component state parameter, the second accessory component state parameter, and the accessory component attribute, and the accuracy of the second type of loss parameter is relatively high.

[0126] In order to illustrate the above implementation manner more clearly, the above implementation manner will be described in several parts below.

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

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

[0129] In a possible implementation manner, the data processing unit determines the torque transmission ratio based on the motor torque and the actual working torque. The data processing unit determines the first torque transmission state parameter based on the motor torque and the torque transmission ratio. The data processing unit determines the second torque transmission state parameter based on the actual working torque and the torque transmission ratio. The data processing unit fuses the first torque transmission state parameter and the second torque transmission state parameter to obtain the first accessory component state parameter.

[0130] Among them, the torque transmission ratio is a data set within the preset time period. The torque transmission ratio includes multiple transmission ratios, and one 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 working torque. The data processing unit encodes the motor torque and the torque transmission ratio based on the attention mechanism to obtain the motor torque feature of the motor torque and the transmission ratio feature of the torque transmission ratio. The data processing unit fuses the motor torque feature and the transmission ratio feature to obtain the first torque transmission state feature. The data processing unit decodes the first torque transmission state feature based on the attention mechanism to obtain the first torque transmission state parameter. The data processing unit encodes the actual working torque and the torque transmission ratio based on the attention mechanism to obtain the actual working torque feature of the actual working torque and the transmission ratio feature of the torque transmission ratio. The data processing unit fuses the actual working torque feature and the transmission ratio feature to obtain the second torque transmission state feature. The data processing unit decodes the second torque transmission state feature based on the attention mechanism to obtain the second torque transmission state parameter. The data processing unit performs weighted fusion on the first torque transmission state parameter and the second torque transmission state parameter to obtain the first accessory component state parameter.

[0132] Among them, the first torque transmission state parameter and the second torque transmission state parameter are set by technicians according to actual situations, and the embodiments of the present application do not limit this.

[0133] Second part: The data processing unit determines the second accessory component state parameter of the target screwdriver based on the actual working torque and the vibration parameter.

[0134] In a possible implementation manner, the data processing unit determines the three-axis vibration intensity of the target screwdriver based on the vibration parameter. The data processing unit determines the second accessory component state parameter of the target screwdriver based on the actual working torque and the three-axis vibration intensity.

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

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

[0137] Among them, the first relational data is used to represent the corresponding relationship between the vibration parameter and the three-axis vibration intensity.

[0138] In the third part, the data processing unit determines the second type of loss parameter based on the first accessory component state parameter, the second accessory component state parameter, and the accessory component attribute.

[0139] In a possible implementation manner, the data processing unit fuses the first accessory component state parameter and the second accessory component state parameter to obtain the current component state parameter of the accessory component. The data processing unit determines the second type of loss parameter based on the current component state parameter and the accessory component attribute.

[0140] Among them, the accessory component attributes include the gearbox reduction ratio, the impact mechanism spring stiffness, and the fatigue limit of the bit connection part material.

[0141] For example, the data processing unit performs weighted fusion on the first accessory component state parameter and the second accessory component state parameter to obtain the current component state parameter of the accessory component. The data processing unit extracts features from the current component state parameter and the accessory component attribute to obtain the current accessory component state characteristics of the accessory component and the accessory component attribute characteristics. The data processing unit fuses the current accessory component state characteristics and the accessory component attribute characteristics to obtain the second type of loss characteristics of the accessory component. The data processing unit performs full connection and normalization on the first type of loss characteristics to obtain the second type of loss parameter.

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

[0143] In a possible implementation, the data processing unit determines a first loss correction parameter corresponding to the current working mode, and the first loss correction parameter is used to correct the loss parameter corresponding to the motor. The data processing unit determines a first target loss parameter of the target screwdriver based on the first type of loss parameter and the first loss correction parameter. The data processing unit determines a first reference loss degree of the target screwdriver based on the first target loss parameter.

[0144] Among them, the first type of loss parameters determined by different working modes are different because the control parameters of the target screwdriver are different under different working modes. To eliminate this difference, the first loss correction parameter is used to correct the first type of loss parameters, and the influence of the working mode on the loss parameters is eliminated as much as possible.

[0145] For example, the data processing unit queries using the current working mode to obtain the first loss correction parameter. The data processing unit multiplies the first type of loss parameter by 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 relationship data to obtain the first reference loss degree.

[0146] Among them, the second relationship data is used to represent the corresponding relationship between the first target loss parameter and the first reference loss degree, and the second relationship data is a function obtained by fitting based on multiple first sample loss parameters and the first sample loss degrees corresponding to the respective first sample loss parameters.

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

[0148] In a possible implementation, the data processing unit determines a second loss correction parameter corresponding to the current working mode, and the second loss correction parameter is used to correct the loss parameter corresponding to the accessory component. The data processing unit determines a second target loss parameter of the target screwdriver based on the second type of loss parameter and the second loss correction parameter. Based on the second target loss parameter, the second reference loss degree of the target screwdriver is determined.

[0149] Among them, the second type of loss parameters determined by different working modes are different because the control parameters of the target screwdriver are different under different working modes. To eliminate this difference, the second loss correction parameter is used to correct the second type of loss parameters, and the influence of the working mode on the loss parameters is eliminated as much as possible.

[0150] For example, the data processing unit queries using the current working mode to obtain 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 relationship data to obtain the second reference loss degree.

[0151] Among them, the third relationship data is used to represent the corresponding relationship between the first target loss parameter and the second reference loss degree. The third relationship data is a function, which is obtained by fitting based on multiple second sample loss parameters and the second sample loss degrees 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 loss degree, the second reference loss degree, and the initial loss parameter.

[0153] Among them, the initial loss parameter is the loss parameter of the target screwdriver when it leaves the factory, and can be regarded as the baseline of the loss degree of the target screwdriver.

[0154] In a possible implementation manner, the data processing unit fuses the first reference loss degree and the second reference loss degree to obtain the third reference loss degree of the target screwdriver. The data processing unit obtains the current loss parameter of the target screwdriver based on the third reference loss degree and the initial loss parameter. The data processing unit determines the remaining service life based on the current loss parameter.

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

[0156] For instance, the data processing unit performs weighted fusion on the first reference loss degree and the second reference loss degree to obtain the third reference loss degree of the target screwdriver. The data processing unit substitutes the initial loss parameter into the fifth relationship data to obtain the initial loss degree of the target screwdriver. The data processing unit determines the ratio of the first difference between the third reference loss degree and the initial loss degree to the second difference between the preset loss degree and the initial loss degree as the current loss parameter of the target screwdriver. The data processing unit substitutes the current loss parameter into the fourth relationship data to obtain the remaining service life.

[0157] Among them, the fourth relationship data is used to represent the corresponding relationship between the current loss parameter and the remaining service life. The fourth relationship data is a function, and the fourth relationship data is obtained by fitting based on the current loss parameters of multiple samples and the remaining service life of each sample corresponding to the current loss parameter of the sample. The fifth relationship data is used to represent the corresponding relationship between the initial loss parameter and the initial loss degree. The fifth relationship data is set by technicians according to the actual situation, and the embodiments of the present application do not limit this.

[0158] Optionally, after step 306, after determining the remaining service life, the data processing unit displays the remaining service life on the screen of the target screwdriver for the user to view. Additionally, in the case where the remaining service life is less than or equal to the preset remaining service life, the data processing unit triggers an alarm to remind the user to replace or repair the target screwdriver.

[0159] All the above optional technical solutions can be combined arbitrarily to form optional embodiments of the present application, which will not be elaborated one by one here.

[0160] Through the technical solution provided by the embodiments of the present application, a first type of working parameter set and a second type of working parameter set of the target screwdriver in the current working mode are obtained. By using the first type of working parameter set, the second type of working parameter set, and the screwdriver attribute of the target screwdriver, a first type of loss parameter for describing the loss degree of the motor and a second type of loss parameter for describing the loss degree of the accessory components are determined, thereby realizing the evaluation of the loss degree of the motor and the accessory components. Based on the first type of loss parameter, the second type of loss parameter, the current working mode, and the initial loss parameter, the remaining service life of the target screwdriver is determined, and the accuracy of the remaining service life is relatively high, thereby providing an effective reference for the user and facilitating the user to replace or repair the electric screwdriver in a timely manner.

[0161] Figure 4 is a schematic structural diagram of a screwdriver service life prediction system based on artificial intelligence provided by the embodiments of the present application. Refer to 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 configured to, when the target screwdriver is in a working state, acquire a first type of working parameter set and a second type of working parameter set of the target screwdriver in the current working mode. The first type of working parameter set includes the working output parameters of the target screwdriver, and the second type of working parameter set includes the working feedback parameters of the target screwdriver. The current working mode is determined based on the working head type and the working surface type of the working head installed on the target screwdriver, and the target screwdriver is an electric screwdriver.

[0163] A loss parameter determination unit 402 is configured 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 motor loss degree of the target screwdriver, and the second type of loss parameter is used to describe the loss degree of the accessory components of the target screwdriver.

[0164] A remaining service life determination unit 403 is configured to determine 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.

[0165] In a possible implementation manner, the acquisition unit 401 is configured to acquire the motor speed, motor torque, and actual working torque of the target screwdriver in the current working mode to obtain the first type of working parameter set. The motor temperature, vibration parameter, feedback pressure, working angle, and motor current of the target screwdriver in the current working mode are acquired to obtain the second type of working parameter set, where the feedback pressure is the pressure along the axial direction of the electric screwdriver.

[0166] In a possible implementation manner, the loss parameter determination unit 402 is configured to determine the first type of loss parameter 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. The second type of loss parameter is determined based on the motor torque, the actual working torque, the vibration parameter, and the accessory component attributes in the screwdriver attributes.

[0167] In a possible implementation manner, the loss parameter determination unit 402 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. A second motor state parameter of the target screwdriver is determined based on the motor speed, the motor torque, and the feedback pressure. The first type of loss parameter is determined based on the first motor state parameter, the second motor state parameter, and the motor attributes.

[0168] In a possible implementation manner, the loss parameter determination unit 402 is configured to determine a first accessory component state parameter of the target screwdriver based on the motor torque and the actual working torque. A second accessory component state parameter of the target screwdriver is determined based on the actual working torque and the vibration parameter. The second type of loss parameter is determined based on the first accessory component state parameter, the second accessory component state parameter, and the accessory component attributes.

[0169] In a possible implementation manner, the remaining service life determination unit 403 is configured to determine a first reference wear degree of the target screwdriver based on the first type of wear parameter and the current working mode. Determine a second reference wear degree of the target screwdriver based on the second type of wear parameter and the current working mode. Determine the remaining service life of the target screwdriver based on the first reference wear degree, the second reference wear degree, and the initial wear parameter.

[0170] In a possible implementation manner, the remaining service life determination unit 403 is configured to determine a first wear correction parameter corresponding to the current working mode, and the first wear correction parameter is used to correct the motor corresponding wear parameter. Determine a first target wear parameter of the target screwdriver based on the first type of wear parameter and the first wear correction parameter. Determine the first reference wear degree of the target screwdriver based on the first target wear parameter.

[0171] The remaining service life determination unit 403 is configured to determine a second wear correction parameter corresponding to the current working mode, and the second wear correction parameter is used to correct the accessory component corresponding wear parameter. Determine a second target wear parameter of the target screwdriver based on the second type of wear parameter and the second wear correction parameter. Determine the second reference wear degree of the target screwdriver based on the second target wear parameter.

[0172] In a possible implementation manner, the remaining service life determination unit 403 is configured to fuse the first reference wear degree and the second reference wear degree to obtain a third reference wear degree of the target screwdriver. Obtain the current wear parameter of the target screwdriver based on the third reference wear degree and the initial wear parameter. Determine the remaining service life based on the current wear parameter.

[0173] In a possible implementation manner, the method for determining the current working mode includes:

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

[0175] Determine a plurality of candidate working modes based on the working head type.

[0176] Determine the current working mode from the plurality of candidate working modes based on the working surface type.

[0177] It should be noted that: when predicting the service life by the service life prediction system of the screwdriver based on artificial intelligence provided in the above embodiments, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the service life prediction system of the screwdriver based on artificial intelligence provided in the above embodiments and the embodiments of the service life prediction method of the screwdriver based on artificial intelligence belong to the same concept. The specific implementation process can be seen in the method embodiments and will not be elaborated here.

[0178] Through the technical solution provided by the embodiments of the present application, a first type of working parameter set and a second type of working parameter set of the target screwdriver in the current working mode are obtained. By using the first type of working parameter set, the second type of working parameter set, and the screwdriver attributes of the target screwdriver, a first type of loss parameter for describing the motor loss degree and a second type of loss parameter for describing the loss degree of the accessory components are determined, so as to realize the evaluation of the loss degrees of the motor and the accessory components. Based on the first type of loss parameter, the second type of 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 relatively high, thus providing an effective reference for users and facilitating users to replace or repair the electric screwdriver in a timely manner.

[0179] Figure 5 It is a schematic structural diagram of a data processing unit provided by the embodiments of the present application. The data processing unit 500 may vary greatly due to different configurations or performances, and may include one or more processors (Central Processing Units, CPUs) 501 and one or more memories 502. Among them, at least one computer program is stored in the one or more memories 502, and the at least one computer program is loaded and executed by the one or more processors 501 to implement the methods provided by the above various method embodiments. Of course, the data processing unit 500 may also have components such as wired or wireless network interfaces, keyboards, and input / output interfaces for input / output. The data processing unit 500 may also include other components for implementing the functions of the device, which will not be elaborated here.

[0180] In an exemplary embodiment, a computer-readable storage medium is further provided, such as a memory including a computer program, and the computer program can be executed by a processor to complete the method for predicting the service life of a screwdriver based on artificial intelligence in the above embodiment. 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, a floppy disk, and an optical data storage device, etc.

[0181] In an exemplary embodiment, a computer program product or a computer program is further provided. The computer program product or the computer program includes program code, and the program code is stored in a computer-readable storage medium. The processor of the computer device reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the computer device executes the method for predicting the service life of a screwdriver based on artificial intelligence.

[0182] In some embodiments, the computer program involved in the embodiments of the present application can be deployed to be executed on a single computer device, or on multiple computer devices located at one place. Or, it can be executed on multiple computer devices distributed at multiple locations and interconnected through a communication network. The multiple computer devices distributed at multiple locations and interconnected through a communication network can form a blockchain system.

[0183] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above embodiments can be completed by hardware, or can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a magnetic disk, or an optical disc, etc.

[0184] The above are only optional embodiments of the present application, and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting the service life of a screwdriver based on artificial intelligence, characterized in that, The method includes: When the target screwdriver is in the working state, obtaining a first set of working parameters and a second set of working parameters of the target screwdriver in the current working mode, where 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 type of the working head and the type of the working surface of the working head installed on the target screwdriver, and 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, determining a first type of loss parameter and a second type of loss parameter of the target screwdriver, where the first type of loss parameter is used to describe the motor loss degree of the target screwdriver, and the second type of loss parameter is used to describe the loss degree of the accessory components 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, determining the remaining service life of the target screwdriver.

2. The method according to claim 1, wherein The obtaining of the first set of working parameters and the second set of working parameters of the target screwdriver in the current working mode includes: Obtaining 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; Obtaining the motor temperature, vibration parameter, feedback pressure, working angle, and motor current of the target screwdriver in the current working mode to obtain the second set of working parameters, where the feedback pressure is the pressure along the axial direction of the electric screwdriver.

3. The method according to claim 2, wherein The determining of the first type of loss parameter and the second type of loss parameter of the target screwdriver based on the first set of working parameters, the second set of working parameters, and the screwdriver attributes of the target screwdriver includes: 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, determining the first type of loss parameter; Based on the motor torque, the actual working torque, the vibration parameter, and the accessory component attributes in the screwdriver attributes, determining the second type of loss parameter.

4. The method according to claim 3, characterized in that, The determining of the first type of loss parameter 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 includes: Based on the motor speed, the motor current, the motor temperature, and the motor torque, determining the first motor state parameter of the target screwdriver; Based on the motor speed, the motor torque, and the feedback pressure, determining the second motor state parameter of the target screwdriver; Based on the first motor state parameter, the second motor state parameter, and the motor attributes, determining the first type of loss parameter.

5. The method according to claim 3, characterized in that, The determining of the second type of loss parameter based on the motor torque, the actual working torque, the vibration parameter, and the accessory component attributes in the screwdriver attributes includes: Determine the first accessory component status parameter of the target screwdriver based on the motor torque and the actual working torque; Determine the second accessory component status parameter of the target screwdriver based on the actual working torque and the vibration parameter; Determine the second type of loss parameter based on the first accessory component status parameter, the second accessory component status parameter, and the accessory component attributes; 6. The method according to claim 1, wherein The determining of 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 includes: Determine the first reference loss degree of the target screwdriver based on the first type of loss parameter and the current working mode; Determine the second reference loss degree of the target screwdriver based on the second type of loss parameter and the current working mode; Determine the remaining service life of the target screwdriver based on the first reference loss degree, the second reference loss degree, and the initial loss parameter; 7. The method according to claim 6, characterized in that, The determining of the first reference loss degree of the target screwdriver based on the first type of loss parameter and the current working mode includes: Determine the first loss correction parameter corresponding to the current working mode, where 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, determine the first target loss parameter of the target screwdriver; based on the first target loss parameter, determine the first reference loss degree of the target screwdriver; The determining of the second reference loss degree of the target screwdriver based on the second type of loss parameter and the current working mode includes: Determine the second loss correction parameter corresponding to the current working mode, where the second loss correction parameter is used to correct the loss parameter corresponding to the accessory component; based on the second type of loss parameter and the second loss correction parameter, determine the second target loss parameter of the target screwdriver; based on the second target loss parameter, determine the second reference loss degree of the target screwdriver; 8. The method according to claim 6, characterized in that, The determining of the remaining service life of the target screwdriver based on the first reference loss degree, the second reference loss degree, and the initial loss parameter includes: Fuse the first reference loss degree and the second reference loss degree to obtain the third reference loss degree of the target screwdriver; Based on the third reference loss degree and the initial loss parameter, obtain the current loss parameter of the target screwdriver; Determine the remaining service life based on the current loss parameter; 9. The method according to claim 1, wherein The determining method of the current working mode includes: Obtain the type of the working head installed on the target screwdriver and the type of the working surface of the working surface; Based on the type of the working head, determine multiple candidate working modes; Based on the type of the working surface, determine the current working mode from the multiple candidate working modes; 10. An artificial intelligence-based service life prediction system for screwdrivers, characterized in that, The system includes: An acquisition unit, configured to acquire, when a target screwdriver is in a working state, a first set of working parameters and a second set of working parameters of the target screwdriver in a current working mode, where the first set of working parameters includes working output parameters of the target screwdriver, the second set of working parameters includes working feedback parameters of the target screwdriver, the current working mode is determined based on a working head type and a working surface type of a working head installed on the target screwdriver, and the target screwdriver is an electric screwdriver; A loss parameter determination unit, configured to determine a first type of loss parameter and a second type of loss parameter of the target screwdriver based on the first set of working parameters, the second set of working parameters, and the screwdriver attributes of the target screwdriver, where the first type of loss parameter is used to describe the motor loss degree of the target screwdriver, and the second type of loss parameter is used to describe the loss degree of the accessory components of the target screwdriver; A remaining service life determination unit, configured to determine 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.

Images