Optimizing electric motor shutdown timing and associated methods and systems

Abstract

Systems and methods of optimizing electric motor shutdown timing are disclosed. One system includes a machine comprising an electric motor, at least one processor, and at least one memory. The electric motor is configured to automatically shut off after a first time period. The memory stores instructions that, when executed by the processor, cause the system to determine a value pattern based on one or more parameters of the machine and / or one or more parameters of the electric motor, generate a second time period based on the value pattern, and automatically shut off the electric motor based on the second time period. In some embodiments, the value pattern is based on one or more parameters of a user profile associated with an operator of the machine.

Description

Technical Field

[0001] This application relates to electric motors, and more specifically, to methods and systems for optimizing the timing of electric motor shutdown. Background Technology

[0002] Many machines use electric motors to drive various mechanical operations. In some cases, a machine may perform operations that require the electric motor to run for a certain period of time (e.g., to drive a mechanical operation), followed by a period of inactivity. Typically, the motor stops after this period of inactivity (e.g., automatically via control circuitry). When, for example, the machine needs to perform subsequent operations involving the motor, this necessitates restarting the motor, which can cause undesirable delays in machine operation and may lead to operator dissatisfaction.

[0003] US Patent 10082771B2 discloses a machine learning device configured to learn operating commands for an electric motor. However, this patent does not disclose modifying the motor's shut-off timing based on the likelihood of motor activity and / or operator actions. Summary of the Invention

[0004] The disclosed technology provides methods and systems for optimizing the timing of an electric motor shutdown. In some embodiments, a system for optimizing the timing of an electric motor shutdown includes a machine comprising an electric motor, at least one hardware processor, and at least one non-transitory memory. The electric motor is configured to automatically shut down after a first time period. The at least one non-transitory memory stores instructions that, when executed by the at least one hardware processor, cause the system to: (i) determine a value pattern based on one or more parameters of the machine and / or one or more parameters of the electric motor, wherein the value pattern indicates the action of the electric motor and / or the machine; (ii) generate a second time period based on the value pattern; and (iii) automatically shut down the electric motor based on the second time period.

[0005] In some embodiments, a method for optimizing the timing of an electric motor shutdown includes: (i) identifying a time period associated with an electric motor that automatically shuts down a machine; (ii) determining one or more parameters of the machine, one or more parameters of the electric motor, and / or one or more parameters of a user profile associated with an operator of the machine; (iii) determining a value pattern based on the one or more parameters, wherein the value pattern indicates the action of the electric motor and / or the machine; (iv) modifying the time period based on the value pattern; and (iv) automatically shutting down the electric motor based on the modified time period.

[0006] In some embodiments, a non-transitory computer-readable storage medium includes instructions thereon that, when executed by at least one data processor of the system, cause the system to: (i) determine a value pattern based on one or more parameters of a machine and / or one or more parameters of an electric motor of the machine, wherein the electric motor is configured to automatically shut off after a certain time period, and wherein the value pattern indicates the action of the electric motor and / or the machine; (ii) modify the time period based on the value pattern; and (iii) automatically shut off the electric motor based on the modified time period. Attached Figure Description

[0007] Figure 1 This is a block diagram illustrating a system configured according to some embodiments of the present technology.

[0008] Figure 2 This is a flowchart illustrating a method for optimizing the timing of electric motor shutdown according to some embodiments of the present technology.

[0009] Figure 3 This is a flowchart illustrating a method for optimizing the timing of electric motor shutdown according to some embodiments of the present technology.

[0010] Figure 4 This is a flowchart of a method for generating a shutdown delay time according to some embodiments of the present technology.

[0011] Figure 5 This is a block diagram illustrating a machine learning (ML) system according to some embodiments of the present technology.

[0012] Figure 6 This is a block diagram illustrating an overview of an environment in which the disclosed technology, according to some embodiments of the present technology, can operate.

[0013] Figure 7 This is a block diagram illustrating a computer system according to some embodiments of the present technology.

[0014] The technology described herein will become more apparent to those skilled in the art upon studying the specific embodiments in conjunction with the accompanying drawings. Embodiments or implementations describing various aspects of the invention are shown by way of example, and the same references may indicate similar elements. While the drawings depict various embodiments for illustrative purposes, those skilled in the art will recognize that alternative implementations may be employed without departing from the principles of the technology. Therefore, although specific embodiments are shown in the drawings, the technology is readily adaptable to various modifications. Detailed Implementation

[0015] The disclosed technology provides methods and systems for optimizing the timing of electric motor shutdown. Industrial machinery and / or vehicles, such as many cranes, excavators, pavers, compactors, etc., typically use electric motors to perform certain mechanical operations. When the machine does not require the operation of the electric motor (e.g., the machine is not performing a motor-driven mechanical operation), the motor can be configured (e.g., via control logic) to shut down after a certain period of time (also known as a “delay time” or “shutdown delay”) to save energy and / or reduce energy waste and reduce motor wear. However, in some cases, this can lead to operational delays and operator dissatisfaction because the motor needs to be restarted for subsequent use.

[0016] The disclosed technology addresses these and other problems by providing a system for optimizing the shutdown timing of an electric motor. For the purposes of this application, the terms "optimizing" and / or "optimize" refer to determining the shutdown time of an electric motor such that energy waste and motor wear associated with running the motor are reduced, while also minimizing operational delays and / or operator dissatisfaction associated with the need to restart the electric motor after shutdown. That is, the disclosed technology is configured to determine a shutdown timing that balances (i) running the motor long enough to allow immediate or near-immediate use when needed by the operator and / or the machine, and (ii) shutting down the motor to mitigate energy waste and / or motor wear.

[0017] In some embodiments, the system includes a machine (e.g., an asphalt paver, compactor, or other industrial machine) comprising an electric motor, at least one hardware processor, and at least one non-transitory memory. The electric motor is configured to automatically shut down after a first time period. The non-transitory memory stores instructions that, when executed by the hardware processor, cause the system to determine (e.g., arithmetic, calculation, etc.) a value pattern indicating an expected and / or imminent / prospective action (i.e., operation) of the electric motor and / or machine, generate a second time period based on the value pattern, and automatically shut down the electric motor based on the second time period. As further discussed herein, the value pattern may be based on the recent behavior and / or history of operating the machine.

[0018] In some embodiments, the value pattern is based on one or more parameters of the machine. For example, in some embodiments, one or more parameters of the machine may include the machine's actions, the machine's state, input from the operator, the machine's movement, the machine's location, the machine's positioning, the machine's startup, the machine's shutdown, the machine's runtime, and / or the tasks assigned to the machine.

[0019] In some embodiments, the value pattern is based on one or more parameters of the electric motor. For example, in some embodiments, one or more parameters of the electric motor may include the motor's running time, motor action, motor status, and / or input from the operator.

[0020] In some embodiments, the value pattern is based on one or more parameters of a user profile associated with the operator of the machine. For example, in some embodiments, one or more parameters of the user profile may include the time since the operator previously turned off the electric motor, the time since the operator previously turned on the electric motor, the operator's experience, the operator's certification and / or qualifications, the frequency with which the operator starts and / or turns off the electric motor, and / or the tasks assigned to the operator.

[0021] In some embodiments, the value pattern includes the time since the electric motor was previously turned off, the time since the electric motor was previously started, the previous running time of the electric motor, the frequency of starting and / or turning off the electric motor, the type of operation of the electric motor, the type of operation of the machine, the time since the machine was previously turned off, the time since the machine was previously started, the previous running time of the machine, the frequency of starting and / or turning off the machine, the probability of machine operation and / or the probability of electric motor operation.

[0022] In some embodiments, at least one machine learning algorithm is used to determine value patterns. For example, value patterns may be determined via at least one machine learning algorithm trained on at least one dataset associated with value patterns previously determined based on one or more parameters of a machine, one or more parameters of an electric motor, and / or one or more parameters of a user profile. In some embodiments, value patterns are determined using one or more lookup tables.

[0023] In some embodiments, the system is configured to determine the probability of one or more actions of the electric motor (e.g., pressurizing a hydraulic system, raising and / or lowering machine components, increasing / decreasing motor speed, etc.) based on value patterns. In such embodiments, the system may be configured to generate a second time period based on the probability of one or more actions of the electric motor.

[0024] In some embodiments, the system is configured to determine the likelihood of one or more actions of the machine (e.g., moving and / or repositioning one or more parts of the machine) based on value patterns. For example, the system may be configured to determine the likelihood of actuating material feeding into the system and may generate a second time period based on the likelihood of actuation.

[0025] In some embodiments, the system is configured to associate one or more parameters (e.g., a first set of parameters) of a machine and / or electric motor with a first action of the machine and / or electric motor, associate one or more parameters (e.g., a second set of parameters) with a second action of the electric motor and / or machine, and determine values ​​and / or action patterns based on the first and second sets of parameters indicating the expected actions (e.g., the first and / or second actions) of the electric motor and / or machine. In such embodiments, the time period can be modified based on the determined values ​​and / or action patterns.

[0026] In some embodiments, the system is configured to determine multiple value patterns and can generate one or more time periods (e.g., a second time period, a third time period, a fourth time period, etc.) based on one or more of the multiple value patterns. For example, the system may be configured to determine a first value pattern corresponding to a first action of an electric motor and / or a first action of a machine (e.g., driving and / or repositioning the machine), and generate a second time period based on the first value pattern. The system may further determine a second value pattern corresponding to a second action of an electric motor and / or a second action of a machine (e.g., actuating a hopper), and generate a third time period based on the second value pattern. The system may be configured to automatically shut down the electric motor after the second or third time period.

[0027] The descriptions and associated drawings are illustrative examples and should not be construed as limiting. This disclosure provides certain details to enable a thorough understanding and implementation of these examples. However, those skilled in the art will understand that the invention can be practiced without many of these details. Similarly, those skilled in the art will understand that the invention may include well-known structures or features not shown or described in detail to avoid unnecessarily obscuring the description of the examples.

[0028] Figure 1 This is a block diagram illustrating a system 100 configured according to some embodiments of the present technology. In this embodiment, system 100 comprises a machine 102 including an electric motor 104 and a controller 106, wherein the electric motor 104 may be configured to drive one or more components 110a-c of the machine 102. For example, the machine 102 may be an electrified (i.e., having a battery or other power source 140) industrial machine and / or vehicle, such as an asphalt paver, compactor, excavator, crane, etc., and the electric motor 104 may be configured to drive mechanical operation of one or more components of the industrial machine and / or vehicle, such as a material feeding function, an auger, a hopper, etc.

[0029] The electric motor 104 is configured to automatically shut down after a certain period of time (e.g., a shutdown delay time). In some embodiments, the period of time corresponds to a period of inactivity for the electric motor 104 and / or the machine 102. For example, the electric motor 104 may be configured to automatically shut down after 3 minutes of not receiving a signal corresponding to an action of the electric motor (e.g., actuating a hopper, driving an auger, and / or operating a material feed system). In some embodiments, the period of time is not less than: 0 seconds (e.g., immediate shutdown), 5 seconds, 10 seconds, 15 seconds, 30 seconds, and / or 45 seconds. In some embodiments, the period of time may be, for example, a range between about 0-5 seconds, 5-15 seconds, 15-45 seconds, and / or 45-60 seconds. In some embodiments, the period of time is not less than: 0 minutes (e.g., immediate shutdown), 1 minute, 3 minutes, and / or 5 minutes. In some embodiments, the period of time may be between about 0-1 minute, 1-3 minutes, and / or 3-5 minutes.

[0030] Controller 106 is configured to control (e.g., increase, decrease, modify, and / or maintain) the amount / length of the time period. In some embodiments, controller 106 (e.g., via control logic) is configured to predict the actions of electric motor 104 and / or machine 102 and adjust the time period based on the expected actions. For example, if machine 102 frequently drives and stops in a relatively short, continuous sequence, controller 106 can modify (e.g., increase) the time period when a subsequent drive is anticipated, such that electric motor 104 runs for a longer period after each stop of machine 102. This avoids the unnecessary number of times motor 104 must be restarted during a particular job or part of a job (e.g., after each intermittent stop).

[0031] As another example, an electrified asphalt paver may need to intermittently pause a first operation (e.g., paving operation) to coordinate with other industrial machinery (e.g., trucks delivering asphalt material). During these pauses, the electric motor 104 can be automatically shut off (e.g., to reduce energy waste / consumption and / or mitigate motor wear). However, the paver may periodically initiate a second operation requiring the electric motor 104 to operate (e.g., actuating a material feed function) during the intermittent pauses of the first operation, which may be indicated by machine parameters (e.g., material feed height within a predetermined range). The controller 106 can recognize this pattern of operation of the electric motor 104 and / or the machine 102, associate this pattern with the first and second operations, and modify (e.g., extend) the time period based on this pattern so that the motor 104 does not unnecessarily shut down after the first operation pause. That is, the motor 104 can remain running for an extended period to support the anticipated second operation. Of course, if the second operation does not occur (for example, the paving operation is paused and the material feed height is within the predetermined range, but the material feed function is not activated), the modified shutdown delay time will pass, and the electric motor 104 will be turned off.

[0032] To predict the actions of electric motor 104 and / or machine 102, controller 106 is configured to identify and / or determine one or more value patterns (i.e., inputs) associated with the actions of electric motor 104 and / or machine 102. In some embodiments, these value patterns are based on one or more parameters of machine 102, one or more parameters of electric motor 104, and user profile 134 of the operator (or team member) of machine 102 (in... Figure 1 The instructions in the middle are one or more parameters of the user profile data 134 and / or task / job / assignment 132 (in the user profile data 134) and / or task / job / assignment 132. Figure 1 The controller 106 associates one or more parameters of the machine 102, electric motor 104, task / job / assignment 132, and / or user profile 134 with a specific action of the electric motor 104 and / or the machine 102. For example, based on historical and real-time inputs associated with (1) operator input 120 of the machine 102 (e.g., position of a lever or switch), (2) state of the motor 104, (3) job 132 assigned to the machine 102, and / or (4) user profile 134 of the operator of the machine 102, the controller 106 may determine a value pattern associated with an impending or near-impending action of the machine 102 and / or the motor 104 (e.g., actuating a material feed function). The shutdown delay time is then modified (e.g., the shutdown delay time is increased) in response to the impending or near-impending action to ensure that the motor 104 is still running when the expected action occurs.

[0033] In some embodiments, the identified and / or determined value patterns include: time since the electric motor was previously turned off, time since the electric motor was previously started, previous running time of the electric motor, frequency of starting and / or turning off the electric motor, type of operation of the electric motor, type of operation of the machine, time since the machine was previously turned off, time since the machine was previously started, previous running time of the machine, frequency of starting and / or turning off the machine, probability of machine operation and / or probability of electric motor operation.

[0034] In some embodiments, computer 130 (e.g., Figure 5 Computer device 528 and / or Figure 7 A computer system 700 is configured to provide external data to machine 102. For example, the external data may include inputs associated with industrial vehicle / truck routes and associated telematics data. In some embodiments, the external data may include parameters of machine 102, parameters of electric motor 104, parameters of task data 132, and / or parameters of user profile data 134. In some embodiments, one or more parameters of machine 102, electric motor 104, task data 132, and / or user profile data 134 are provided and / or determined directly from components of machine 102 (e.g., first component 110a, second component 110b, third component 110c) and / or inputs (e.g., operator input 120, positioning / GPS data 122, operating status 124, motion 126, and / or other sensors).

[0035] In some embodiments, one or more parameters of machine 102 include: machine 102's operation, state 124 (e.g., machine in startup, shutdown, neutral, etc.), operator input 120, movement / motion 126, position / location 122, machine runtime, and / or tasks assigned to the machine. For example, controller 106 may determine a value pattern based on the height corresponding to a first component 110a (e.g., a material feed component), the speed of a second component 110b (e.g., an auger), the weight load status of a third component 110c (e.g., a loading box of a material transfer vehicle), operator input 120 (e.g., the position of a drive lever), machine 102's state 124 (e.g., idling), and inputs associated with one or more support vehicles, such as historical and real-time inputs on positioning / location and / or payload. In some embodiments, one or more machine parameters of machine 102 may include inputs from one or more optical detection / sensing systems (e.g., cameras, radar systems, LiDAR systems, etc.).

[0036] In some embodiments, one or more parameters of the electric motor 104 include: operating time, operation of the electric motor 104, state of the electric motor 104, and / or input from the operator to the motor 104. For example, the controller 106 may determine a value pattern based on the operating time (e.g., 10 seconds) corresponding to the motor 104, followed by historical and real-time inputs of the motor 104's state, such as idling.

[0037] In some embodiments, one or more parameters of the user profile data 134 include: time since the operator previously turned off the electric motor 104, time since the operator previously turned on the electric motor 104, the operator's experience, the operator's certification and / or qualifications, the frequency at which the operator starts and / or turns off the electric motor 104, and / or tasks assigned to the operator. For example, the controller 106 may determine a value pattern based on historical and real-time inputs corresponding to specific operating characteristics of the user associated with the user profile data 134, such as the frequency at which the user prefers to start and / or operate the motor 104 and / or the machine 102.

[0038] In some embodiments, one or more parameters of the task data 132 include: task type, task length, materials associated with the task, team size associated with the task, expected follow-up tasks, and / or task history.

[0039] In some embodiments, the value pattern is based on the type of machine 102 (e.g., asphalt paver, crane, etc.). In some embodiments, the value pattern is based on additional external input (not shown). For example, the time period of the electric motor 104 of an electrified asphalt compactor may be adjusted based on compaction distribution map data provided to the compactor.

[0040] Figure 2 This is a flowchart illustrating a method 200 for optimizing the shut-off timing of an electric motor according to some embodiments of the present technology. In some embodiments, method 200 includes... Figure 1 The features and / or components of system 100 are substantially similar to / the same as those of other systems.

[0041] At box 202, a time period (i.e., a shutdown delay time) associated with shutting down the electric motor of the machine is identified. In some embodiments, the time period corresponds to a period of inactivity of the electric motor and / or the machine. For example, the electric motor may be configured to automatically shut down after 3 minutes if no signal corresponding to the action of the electric motor is received. In some embodiments, the time period is not less than: 0 seconds (e.g., immediate shutdown), 5 seconds, 10 seconds, 15 seconds, 30 seconds, and / or 45 seconds. In some embodiments, the time period may be, for example, a range between about 0-5 seconds, 5-15 seconds, 15-45 seconds, and / or 45-60 seconds. In some embodiments, the time period is not less than: 0 minutes (e.g., immediate shutdown), 1 minute, 3 minutes, and / or 5 minutes. In some embodiments, the time period may be between about 0-1 minute, 1-3 minutes, and / or 3-5 minutes.

[0042] At box 204, a value pattern indicating the expected action of the electric motor is determined (e.g., arithmetic, calculation, etc.). In some embodiments, the value pattern is determined based on historical and real-time inputs of one or more parameters corresponding to the machine, one or more parameters of the electric motor, one or more parameters of the user profile associated with the machine operator, and / or one or more parameters of the machine's task / job / assignment. Figure 4 (Further discussion in the text).

[0043] At box 206, the time period associated with shutting down the electric motor is modified (e.g., increased or decreased) based on a determined value pattern. In some embodiments, this time period is modified at least in part based on determined parameters of the user profile, electric motor, machine, and / or task. That is, inputs associated with one or more parameters of the user profile, electric motor, machine, and / or task are used to modify the time period separately from the value pattern. For example, a value pattern based on historical and real-time inputs of the machine's material feed height and the machine's drive lever position could cause the shutdown delay time to increase (e.g., from 1 minute to 3 minutes). However, the task type can be used (e.g., by reducing the modified time from 3 minutes to 2 minutes) to further adjust or "tune" the shutdown delay time. At box 208, the electric motor is automatically shut down based on the modified time period.

[0044] Figure 3 This is a flowchart illustrating a method 300 for optimizing the shut-off timing of an electric motor according to some embodiments of the present technology. In some embodiments, method 300 includes... Figure 1 The features and / or components of system 100 are substantially similar to / the same as those of other systems.

[0045] At box 302, a first time period (e.g., a shutdown delay time) associated with shutting down the electric motor of the machine is identified. For example, the electric motor can be configured to automatically shut down after 3 minutes if no signal corresponding to the action of the electric motor is received.

[0046] At box 304, one or more parameters of the machine, one or more parameters of the electric motor, one or more parameters of the user profile, and / or one or more parameters of the task data (collectively referred to as a first set of parameters in some embodiments) are associated with a first action of the machine and / or the motor. For example, for an asphalt paver, a first position of the drive lever (e.g., a machine parameter), a first material feed height (e.g., a machine parameter), a previous run time of the electric motor (e.g., an electric motor parameter), operator authentication of the machine (e.g., parameters of the user profile), and a specific paving assignment (e.g., parameters of the task data) may be associated with a drive operation (e.g., the first action).

[0047] At box 306, one or more parameters of the machine, one or more parameters of the electric motor, one or more parameters of the user profile, and / or one or more parameters of the task data (collectively referred to as a second set of parameters in some embodiments) are associated with a second action of the machine and / or the motor. For example, continuing with the asphalt paver example above, a second position of the drive lever (e.g., machine parameters), a second material feed height (e.g., machine parameters), the current operating operation of the electric motor (e.g., electric motor parameters), the preferred operating characteristics of the machine operator (e.g., parameters of the user profile), and other industrial vehicles assigned tasks (e.g., parameters of the task data) can be associated with the actuation of the material feed system (e.g., the second action).

[0048] At box 308, an action mode is determined (e.g., operation, calculation, etc.) based on one or more parameters associated with the first action and one or more parameters associated with the second action. For example, continuing with the asphalt paver example above, a value mode can be determined based on the frequency and / or timing of parameters associated with drive operations that occur in relation to the frequency and / or timing of parameters associated with the actuation of the material feed system.

[0049] At block 310, a second time period is determined based on a value pattern. For example, based on the timing of the material feed system actuation parameters relative to the drive operation parameters (i.e., the value pattern), anticipating that material feed system actuation and / or drive operation is imminent or nearing imminent, a second shutdown delay time longer than the first shutdown delay time can be determined. In some embodiments, the second time period is the same as the first time period. At block 312, the electric motor is automatically shut down based on the second time period.

[0050] Figure 4This is a flowchart of a method 400 for generating a shutdown delay time according to some embodiments of the present technology. In some embodiments, method 400 includes... Figure 1 The features and / or components of system 100 are substantially similar to / the same as those of other systems.

[0051] At box 402, (for example, by...) Figure 1 The controller 106 determines a first value for the first parameter. In some embodiments, the first parameter is a first parameter of the machine, electric motor, user profile, and / or task data (regarding...). Figure 1 (For a more comprehensive description). For example, the first parameter could be the position of the machine's drive lever, and the first value could be a "stop" or "idle" value. At box 403, a second value for the second parameter is determined. The second parameter can be the same as or a different type of the first parameter (e.g., the first parameter could be a parameter of the machine, and the second parameter could be a different parameter of the machine, or a parameter of an electric motor, user profile, and / or task data). For example, the second parameter could be the material feed height, and the second value could be a "low" or "high" value.

[0052] At box 404, a third value for the third parameter is determined. The third parameter may be of the same or different type as the first and / or second parameter. For example, the third parameter may be the first historical / previous running time of the electric motor. At box 405, a fourth value for the third parameter is determined. For example, the fourth value may be the second historical / previous running time of the electric motor, wherein the second historical / previous running time of the electric motor is closer in time (i.e., more simultaneous) to the active operation of the machine and / or the electric motor.

[0053] At box 406 (for example, by...) Figure 1 The controller 106 identifies value patterns based on a first, second, third, and fourth value. For example, for an asphalt paver, the controller 106 can identify a repeating sequence of the drive lever being in a "stop" position (i.e., the first value), the material feed height being "low" (i.e., the second value), and the electric motor running for a first running time (i.e., the third value), followed by the motor running for a second running time (i.e., the fourth value). At block 408, the value pattern is associated with the operation of the machine and / or the electric motor. For example, the repeating sequence discussed herein can be associated with actuating the material feed function. In some embodiments, value patterns are identified based on information / data stored as a lookup table. For example, the controller 106 can compare the values ​​of various types of parameters (e.g., first, second, and third parameters associated with first, second, and third machine parameters, respectively) and the first, second, third, and fourth values ​​with a lookup table to identify value patterns.

[0054] At box 410, the probability of a relevant action is determined (e.g., by controller 106). In some embodiments, the probability of a relevant action is based at least in part on a value pattern. For example, this can be done (e.g., via a lookup table or a machine learning system, e.g., Figure 5 The ML system 500 compares value patterns with previously identified value patterns, and the probability can be based on the similarity between value patterns.

[0055] At box 412, a shutdown delay time for the electric motor is generated based on the probability of the associated action. In some embodiments, the shutdown delay time is based on the probability of the associated action meeting or exceeding a threshold probability (e.g., meeting or exceeding 50%, 40%, 30%, etc., the probability of occurrence and / or being initiated by the operator). In some embodiments, the generated shutdown delay time is proportional to the determined probability. For example, if the determined probability is 50%, the shutdown delay time may be increased by 50%. As another example, if the determined probability is 40%, the shutdown delay time may be increased by 10%.

[0056] In some embodiments, the generated shutdown delay time is a modification of the existing and / or previous shutdown delay time of the electric motor. For example, the generated shutdown delay time may include increasing or decreasing the existing and / or previous shutdown delay time of the electric motor.

[0057] Figure 5 This is a block diagram illustrating an ML system 500 according to one or more embodiments. The ML system 500 uses references... Figure 7 The computer system 700 is implemented using components shown and described in more detail. Different embodiments of the ML system 500 include different and / or additional components and are connected in different ways. The ML system 500 is sometimes referred to as an ML module.

[0058] ML System 500 includes a reference for use. Figure 7 The feature extraction module 508 of the computer system 700 is shown and described in more detail. In some embodiments, the feature extraction module 508 extracts feature vectors 512 from input data 504. For example, the input data 504 includes data from a machine (e.g., Figure 1 The input data 504 includes input data from machine 102, input data from the machine's electric motor (e.g., electric motor 104), input data from the machine operator's user profile (e.g., user profile data 134), and / or input data from the machine's tasks / jobs / assignments (e.g., task data 132). In some embodiments, the input data 504 includes a previously established correlation between one or more actions / operations of the machine and / or electric motor and various input data 504 (e.g., from the machine, electric motor, user profile, and / or task data).

[0059] For example, input data 504 from the machine may include machine actions, machine status, operator input, machine movement, machine position, machine positioning, machine startup, machine shutdown, machine running time, and / or tasks assigned to the machine. Input data 504 from the electric motor may include electric motor running time, electric motor actions, electric motor status, and / or operator input. Input data 504 from the user profile may include the time since the operator previously shut down the electric motor, the time since the operator previously started the electric motor, the operator's experience, operator certification and / or qualifications, the frequency with which the operator starts and / or shuts down the electric motor, and / or tasks assigned to the operator. Input data from the machine's tasks may include task type, task length, materials associated with the task, team size associated with the task, anticipated follow-up tasks, and / or task history.

[0060] Feature vector 512 includes features 512a, 512b, ..., 512n. Feature extraction module 508 reduces redundancy, such as duplicate data values, in input data 504 to transform input data 504 into a reduced set of features 512 (e.g., features 512a, 512b, ..., 512n). Feature vector 512 contains relevant information from input data 504, enabling ML model 516 to identify events of interest or data value thresholds using the reduced representation. In some embodiments, feature extraction module 508 uses the following dimensionality reduction techniques: Independent Component Analysis, Isomap, Kernel Principal Component Analysis (PCA), Latent Semantic Analysis, Partial Least Squares, PCA, Multifactor Dimensionality Reduction, Nonlinear Dimensionality Reduction, Multilinear PCA, Multilinear Subspace Learning, Semidefinite Embedding, Autoencoder, and Deep Feature Synthesis.

[0061] In an alternative embodiment, the ML model 516 performs deep learning (also known as deep structured learning or hierarchical learning) directly on the input data 504 to learn data representations, rather than using task-specific algorithms. In deep learning, explicit feature extraction is not performed; features 512 are implicitly extracted by the ML system 500. For example, the ML model 516 uses a cascade of multiple layers of non-linear processing units for implicit feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Thus, the ML model 516 learns in supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) modes. The ML model 516 learns multiple representation levels corresponding to different levels of abstraction, where the different levels form a conceptual hierarchy. The multiple representation levels configure the ML model 516 to distinguish features of interest from background features.

[0062] In an alternative embodiment, ML model 516 (e.g., in the form of a CNN) generates output 524 directly from input data 504 without feature extraction. Output 524 is provided to computer device 528. Computer device 528 uses a reference... Figure 7 The components of the computer system 700, shown and described in more detail, include servers, computers, tablets, smartphones, etc. In some embodiments, steps performed by the ML system 500 are stored in memory on the computer device 528 for execution. In other embodiments, output 524 is displayed on the electronic display of the computer device 528.

[0063] A CNN is a feedforward artificial neural network in which the connection patterns between its neurons are inspired by the organization of the visual cortex. Individual cortical neurons respond to stimuli in confined spatial regions called receptive fields. The receptive fields of different neurons partially overlap, causing them to tile the visual field. The response of an individual neuron to stimuli within its receptive field is mathematically approximated by convolution operations. CNNs are based on biological processes and are variants of multilayer perceptrons designed to use minimal preprocessing.

[0064] In this embodiment, ML model 516 is a CNN that includes both convolutional layers and max-pooling layers. For example, the architecture of ML model 516 is "fully convolutional," meaning that variable-sized sensor data vectors are fed into it. For the convolutional layers, ML model 516 specifies the kernel size, the stride of the convolution, and the amount of zero-padding applied to the input of that layer. For the pooling layers, model 516 specifies the kernel size and stride of the pooling.

[0065] In some embodiments, the ML system 500 trains an ML model 516 based on training data 520 to correlate feature vectors 512 with the expected output in the training data 520. As part of training the ML model 516, the ML system 500 forms a training set of features and training labels by identifying a positive training set of features that have been determined to have the desired property in question, and in some embodiments, forms a negative training set of features that do not have the property in question.

[0066] ML system 500 applies ML techniques to train ML model 516, which, when applied to feature vector 512, outputs an indication of whether feature vector 512 possesses one or more associated desired properties (e.g., the probability that feature vector 512 has a specific Boolean property, or an estimate of a scalar property). In an embodiment, ML system 500 further applies dimensionality reduction (e.g., via linear discriminant analysis (LDA), PCA, etc.) to reduce the amount of data in feature vector 512 to a smaller, more representative dataset.

[0067] In an embodiment, the ML system 500 uses supervised ML to train the ML model 516, where feature vectors from both the positive and negative training sets serve as inputs. In some embodiments, various ML techniques are used, such as linear support vector machines (linear SVMs), augmentations of other algorithms (e.g., AdaBoost), logistic regression, Naive Bayes, memory-based learning, random forests, bagged trees, decision trees, augmented trees, augmented stumps, neural networks, CNNs, etc. In some embodiments, the validation set 532 is formed by additional features beyond those in the training data 520, which have been determined to have or not have the properties in question. The ML system 500 applies the trained ML model 516 to the features of the validation set 532 to quantify the accuracy of the ML model 516. Common metrics used in accuracy measurements include precision and recall, where precision refers to the number of correctly predicted outcomes among all outcomes predicted by the ML model 516, and recall is the number of correctly predicted outcomes among the total number of features having the desired properties in question. In some embodiments, the ML system 500 iteratively retrains the ML model 516 until a stopping condition is met (e.g., an accuracy measurement indicating that the ML model 516 is sufficiently accurate, or a certain number of training epochs have occurred). In embodiments, the validation set 532 includes data corresponding to the confirmed mechanical properties and / or weighted / constant combinations thereof. This allows the validation set 532 to be used to validate the detected values. The validation set 532 is generated based on the analysis to be performed.

[0068] Figure 6 This is a block diagram illustrating an overview of an environment 600 in which the disclosed technology according to some embodiments of the present invention may operate. Environment 600 may include one or more client computing devices 605A-D, examples of which may include computer system 700. Client computing devices 605 may operate in a networked environment using a logical connection via network 630 to one or more remote computers, such as server computing device 610.

[0069] In some implementations, server 610 may be an edge server that receives client requests and coordinates the fulfillment of those requests through other servers such as servers 620A-C. Server computing devices 610 and 620 may include computing systems, such as system 700. Although each server computing device 610 and 620 logically appears as a single server, each server computing device may be a distributed computing environment comprising multiple computing devices located in the same or geographically different physical locations. In some implementations, each server 620 corresponds to a group of servers.

[0070] Client computing device 605 and server computing devices 610 and 620 can each act as a server or client for other server / client devices. Server 610 can connect to database 615. Servers 620A-C can each connect to corresponding databases 625A-C. As discussed above, each server 620 can correspond to a group of servers, and each of these servers can share a database or have its own database. Databases 615 and 625 can store (e.g., store) information, such as machine (e.g., Figure 1 The databases 615 and 625 may store one or more parameters of the machine 102, one or more parameters of the electric motor (e.g., electric motor 104), one or more parameters of the user profile associated with the machine operator (e.g., user profile data 134), and / or one or more parameters of the machine's tasks / jobs / assignments (e.g., task data 132). In some embodiments, the databases 615 and 625 may store previously established relationships between one or more actions / operations of the machine and / or electric motor and various parameters (e.g., from the machine, electric motor, user profile, and / or task data).

[0071] For example, one or more parameters of a machine may include the machine's actions, machine status, input from the operator, machine movement, machine position, machine positioning, machine startup, machine shutdown, machine running time, and / or tasks assigned to the machine. One or more parameters of an electric motor may include the electric motor's running time, electric motor actions, electric motor status, and / or input from the operator. One or more parameters of a user profile may include the time since the operator previously shut down the electric motor, the time since the operator previously started the electric motor, the operator's experience, operator certification and / or qualifications, the frequency with which the operator starts and / or shuts down the electric motor, and / or tasks assigned to the operator. One or more parameters of a machine's task may include the task type, task length, materials associated with the task, team size associated with the task, anticipated follow-up tasks, and / or task history.

[0072] Although databases 615 and 625 logically appear as a single unit, they can each be a distributed computing environment containing multiple computing devices, located within their respective servers, or in the same or geographically different physical locations.

[0073] Network 630 can be a local area network (LAN) or a wide area network (WAN), but it can also be other wired or wireless networks. Network 630 can be the Internet or some other public or private network. Client computing device 605 can connect to network 630 via a network interface, for example, via wired or wireless communication. Although the connection between server 610 and server 620 is shown as a separate connection, these connections can be any type of LAN, WAN, wired or wireless network, including network 630 or a separate public or private network.

[0074] Figure 7 This is a block diagram illustrating a computer system 700 according to one or more embodiments. The components of the computer system 700 are used for implementation respectively. Figure 2 –4. One or more portions of methods 200, 300, and / or 400, and / or perform the analysis and calculations described throughout this document. In some embodiments, components of the computer system 700 are used to implement the reference… Figure 5 The ML system 500 is shown and described in more detail. At least some of the operations described herein are implemented on the computer system 700.

[0075] Computer system 700 includes one or more central processing units (“processors”) 702, main memory 706, non-volatile memory 710, network adapter 712 (e.g., network interface), video display 718, input / output devices 720, control devices 722 (e.g., keyboard and pointing devices), drive unit 724 including storage medium 726, and signal generation device 720, all communicatively connected to bus 716. Bus 716 is shown as an abstraction representing one or more physical buses and / or point-to-point connections connected via appropriate bridges, adapters, or controllers. In embodiments, bus 716 includes a system bus, a Peripheral Component Interconnect (PCI) bus or PCI-Express bus, HyperTransport or Industry Standard Architecture (ISA) bus, Small Computer System Interface (SCSI) bus, Universal Serial Bus (USB), IIC (I2C) bus, or IEEE Standard 1394 bus (also known as “FireWire”).

[0076] In this embodiment, computer system 700 shares a computer processor architecture similar to that of a desktop computer, tablet computer, personal digital assistant (PDA), mobile phone, game console, music player, wearable electronic device (e.g., watch), networked (“smart”) device (e.g., television or home assistant device), virtual / augmented reality system (e.g., head-mounted display), or another electronic device capable of executing (sequentially or otherwise) a set of instructions that specifies the actions to be taken by computer system 700.

[0077] Although main memory 706, non-volatile memory 710, and storage medium 726 (also referred to as "machine-readable medium") are shown as a single medium, the terms "machine-readable medium" and "storage medium" should be considered to include a single medium or multiple media (e.g., a centralized / distributed database and / or associated caches and servers) storing one or more instruction sets 728. The terms "machine-readable medium" and "storage medium" should also be understood to include any medium capable of storing, encoding, or carrying instruction sets for execution by computer system 700.

[0078] Generally, routines executed to implement embodiments of the present disclosure are implemented as part of an operating system, or a particular application, component, program, object, module, or sequence of instructions (collectively, a “computer program”). A computer program typically includes one or more instructions (e.g., instructions 704, 708, 728) set at various times in various memories and storage devices within a computer apparatus. When read and executed by one or more processors 702, the instructions cause the computer system 700 to perform operations to execute elements relating to various aspects of the present disclosure.

[0079] Furthermore, although embodiments have been described in the context of a fully functional computer device, those skilled in the art will understand that various embodiments can be distributed as a program product in various forms. This disclosure applies regardless of the particular type of machine or computer-readable medium used to actually implement the distribution.

[0080] Other examples of machine-readable storage media, machine-readable media, or computer-readable media include recordable media such as volatile and non-volatile memory devices 710, floppy disks and other removable disks, hard disk drives, optical disks (e.g., optical disk read-only memories (CD-ROMs), digital multifunction optical disks (DVDs)), and transmission media such as digital and analog communication links.

[0081] Network adapter 712 enables computer system 700 to transmit data with entities outside computer system 700 within network 714 via any communication protocol supported by computer system 700 and external entities. In embodiments, network adapter 712 includes a network adapter card, wireless network interface card, router, access point, wireless router, switch, multilayer switch, protocol converter, gateway, bridge, bridge router, hub, digital media receiver, and / or repeater.

[0082] In one embodiment, network adapter 712 includes a firewall that governs and / or manages permissions to access proxy data within a computer network and tracks different trust levels between different machines and / or applications. In another embodiment, the firewall is any number of modules having any combination of hardware and / or software components capable of enforcing a predetermined set of access permissions (e.g., to regulate traffic flow and resource sharing between these entities) between machines and applications, or between specific sets of machines and / or applications. The firewall also manages and / or has access to an access control list that details permissions, including access and operational permissions for individuals, machines, and / or applications to objects, and the environment in which those permissions take effect.

[0083] In the embodiments, the functions performed in the processes and methods are implemented in different orders. Furthermore, the outlined steps and operations are provided by way of example only. For instance, some steps and operations may be optional, combined into fewer steps and operations, or extended into additional steps and operations without departing from the essence of the disclosed embodiments.

[0084] In the embodiments, the techniques described herein are implemented by programmable circuit systems (e.g., one or more microprocessors), software and / or firmware, dedicated hardwired (i.e., non-programmable) circuit systems, or combinations thereof. In the embodiments, the dedicated circuit system takes the form of one or more application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), etc.

[0085] Industrial applicability

[0086] The disclosed systems and methods can be implemented in machines that use electric motors to drive certain operations. For example, the disclosed techniques can be used with industrial machines that use one or more electric motors to drive hydraulic and / or mechanical operations (e.g., raising and / or lowering, extending and / or retracting, rotating machine parts, etc.). Industrial machines typically require frequent start-ups and stops (e.g., to coordinate with other machine operations, move to different locations, reposition, load materials, unload materials, etc.). When machine operation stops (or pauses), the electric motors can be configured to automatically shut off after a certain period to save energy and / or reduce motor wear. However, shutting off the electric motors can lead to undesirable operational delays and operator dissatisfaction because the motors must be restarted for subsequent operations.

[0087] The disclosed technology allows machines with electric motors to optimize the automatic shutdown timing of the electric motors on a dynamic (e.g., continuously updated or changed) basis, such that the shutdown delay is long enough to mitigate and / or prevent operational delays and frustrations associated with the need to restart the electric motor, but short enough to mitigate and / or prevent unnecessary energy waste and / or motor wear. For example, for a first set of jobs and / or task assignments, the disclosed technology can anticipate frequent machine starts / stops and increase the delay time for shutting down the electric motor to mitigate the delays associated with the corresponding frequent restarts of the motor. For a second set of jobs and / or task assignments, the disclosed technology can anticipate relatively long periods of machine start / stop and reduce the delay time to mitigate motor wear and energy waste associated with running the motor when no motor action is imminent. The disclosed technology can continuously adjust and / or update the delay time during a given set of jobs and / or task assignments based on one or more parameters of the machine (i.e., condition, status, input), one or more parameters of the electric motor, and / or one or more parameters of a user profile associated with the machine operator.

[0088] By dynamically adjusting the shutdown delay time of the electric motor, a balance can be struck between saving energy and / or reducing unnecessary motor wear and mitigating operational delays and / or operator dissatisfaction caused by the need to restart the motor.

[0089] Remark

[0090] The terms “example,” “embodiment,” and “implementation” are used interchangeably. For example, a reference to “an example” or “example” in this disclosure may be, but is not necessarily, a reference to the same implementation; and such a reference means at least one implementation. The appearance of the phrase “in an example” does not necessarily refer to all of the same example, nor is it a single or alternative example that excludes other examples. Features, structures, or characteristics described in connection with an example may include in another example of this disclosure. Furthermore, various features that may be demonstrated by some examples but not by others are described. Similarly, various requirements that may be required for some examples but not for others are described.

[0091] The terms used herein should be interpreted in their broadest and most reasonable manner, even when used in conjunction with certain specific examples of the invention. The terms used in this disclosure generally have their ordinary meaning in the relevant art, in the context of this disclosure, and in the specific context in which each term is used. The use of alternative language or synonyms does not preclude the use of other synonyms. No special meaning should be assigned to terms as they are presented or discussed herein. Highlighted use has no effect on the scope and meaning of the terms. Furthermore, it should be understood that the same content can be expressed in more than one way.

[0092] Unless the context explicitly requires otherwise, throughout the specification and claims, the terms “comprising,” “including,” etc., shall be interpreted in an inclusive sense, not an exclusive or exhaustive sense; that is, in the sense of “including but not limited to.” As used herein, the terms “connection,” “link,” or any variation thereof mean any direct or indirect connection or link between two or more elements; such connection or link between elements may be physical, logical, or a combination thereof. Furthermore, the terms “this article,” “above,” “below,” and terms with similar meanings may refer to the entirety of this application, rather than any particular part of it. Where the context permits, the terms used in the above specific embodiments, whether singular or plural, may also include both singular and plural forms, respectively. The term “or,” referring to a list of two or more items, covers all of the following interpretations: any item in the list, all items in the list, and any combination of items in the list. The term “module” broadly refers to software components, firmware components, and / or hardware components.

[0093] While specific examples of the technology have been described above for illustrative purposes, various equivalent modifications are possible within the scope of this invention, as will be recognized by those skilled in the art. For example, although processes or blocks are presented in a given order, alternative implementations may execute routines with steps in a different order, or employ systems with blocks, and some processes or blocks may be deleted, moved, added, subdivided, combined, and / or modified to provide alternatives or sub-combinations. Each of these processes or blocks may be implemented in various different ways. Moreover, although processes or blocks are sometimes shown as being executed sequentially, these processes or blocks may alternatively be executed or implemented in parallel, or may be executed at different times. Furthermore, any specific figures mentioned herein are merely examples, allowing alternative implementations to employ different values ​​or ranges.

[0094] The details of the disclosed embodiments may vary significantly in particular embodiments, while still being covered by the teachings disclosed. As noted above, specific terms used in describing features or aspects of the invention should not be construed as implying that the terms are redefined herein as limited to any specific characteristic, feature, or aspect of the invention associated with that term. Generally, the terms used in the following claims should not be construed as limiting the invention to the specific examples disclosed herein, unless such terms are expressly defined by the foregoing specific embodiments. Therefore, the actual scope of the invention covers not only the disclosed examples but also all equivalent ways of practicing or implementing the invention according to the claims. Some alternative embodiments may include additional elements of those embodiments described above, or may include fewer elements.

[0095] Any patents and applications and other references mentioned above, as well as any patents and applications and other references that may be listed in the appended filings, are incorporated herein by reference in their entirety, except where there is any subject matter waiver or denial, and except where the incorporated material is inconsistent with the express disclosure herein, in which case the language of this disclosure shall prevail. Various aspects of the invention may be modified to incorporate the systems, functions, and concepts of the foregoing various references to provide further embodiments of the invention.

[0096] To reduce the number of claims, certain embodiments are presented below in certain claim forms, but the applicant contemplates other forms of aspects of the invention. For example, aspects of the claims may be described in a means-plus-function form or otherwise, such as embodied in a computer-readable medium. Claims intended to be interpreted as means-plus-function claims will use the phrase “means for…”. However, the use of the term “for” in any other context is not intended to invoke a similar interpretation. The applicant reserves the right to seek such additional claim forms in this application or subsequent applications.

Claims

1. A system for optimizing the timing of shutting off an electric motor, the system comprising: The machine includes an electric motor configured to automatically shut off after a first time period; At least one hardware processor; as well as At least one non-transitory memory, the at least one non-transitory memory storing instructions, the instructions causing the system to: when executed by the at least one hardware processor A value pattern is determined based on one or more parameters of the machine, one or more parameters of the electric motor, and / or one or more parameters of a user profile associated with the operator of the machine, wherein the value pattern indicates the action of the electric motor and / or the machine. A second time period is generated based on the value pattern; as well as The electric motor is automatically shut off based on the second time period.

2. The system according to claim 1, wherein, One or more parameters of the user profile include at least one of the following: time since the operator previously turned off the electric motor, time since the operator previously turned on the electric motor, the operator's experience, the operator's certification and / or qualifications, the frequency with which the operator starts and / or turns off the electric motor, and / or the tasks assigned to the operator. One or more parameters of the machine include: the machine's operation, the machine's state, input from the operator, the machine's movement, the machine's position, the machine's location, the machine's startup, the machine's shutdown, the machine's runtime and / or the tasks assigned to the machine, and One or more parameters of the electric motor include at least one of the following: the operating time of the electric motor, the action of the electric motor, the state of the electric motor, and / or input from the operator.

3. The system of claim 1, wherein the value pattern includes at least one of the following: time since the electric motor was previously turned off, time since the electric motor was previously started, previous operating time of the electric motor, frequency of starting and / or turning off the electric motor, type of operation of the electric motor, type of operation of the machine, time since the machine was previously turned off, time since the machine was previously started, previous operating time of the machine, frequency of starting and / or turning off the machine, probability of operation of the machine and / or probability of operation of the electric motor.

4. The system of claim 1, wherein the value pattern is determined via at least one machine learning algorithm, wherein the at least one machine learning algorithm is trained based on at least one dataset, the at least one dataset being associated with a value pattern previously determined based on one or more parameters of the machine, one or more parameters of the electric motor, and / or one or more parameters of the user profile associated with the operator of the machine.

5. The system of claim 1, wherein the at least one non-transitory memory further stores instructions that, when executed by the at least one hardware processor, cause the system to determine the probability of an action of the electric motor and / or the machine based on the value pattern, and wherein the generation of the second time period is also based on the determined probability of the action.

6. The system of claim 1, wherein the one or more parameters are a first set of parameters and indicate a first action of the electric motor and / or the machine, and wherein the at least one non-transitory memory also stores instructions that, when executed by the at least one hardware processor, cause the system to: The first set of parameters is associated with the first action of the electric motor and / or the machine; The second set of parameters is associated with the second action of the electric motor and / or the machine; and The value pattern is determined based on a first set of parameters and a second set of parameters.

7. A method for optimizing the shut-off timing of an electric motor, the method comprising: Identify the time periods associated with the automatic shutdown of the machine's electric motor; Determine one or more parameters of the machine, the electric motor, and / or the user profile associated with the operator of the machine; A value pattern is determined based on the one or more parameters, wherein the value pattern indicates the action of the electric motor and / or the machine; Modify the time period based on the value pattern; as well as The electric motor is automatically shut off based on the modified time period.

8. The method of claim 7, further comprising the possibility of analyzing the value pattern via at least one machine learning algorithm to generate the action of the electric motor, wherein modifying the time period is also based on the possibility of the action of the electric motor.

9. The method of claim 7, wherein the one or more parameters are a first set of parameters indicating a first action of the electric motor and / or the machine, and wherein the method further comprises: The first set of parameters is associated with the first action of the electric motor and / or the machine; as well as The second set of parameters is associated with the second action of the electric motor and / or the machine; The value pattern is determined based on a first set of parameters and a second set of parameters.

10. The method according to claim 7, wherein, One or more parameters of the machine include: the machine's operation, the machine's state, input from the operator, the machine's movement, the machine's position, the machine's location, the machine's startup, the machine's shutdown, the machine's running time, and / or the tasks assigned to the machine. One or more parameters of the electric motor include at least one of the following: the operating time of the electric motor, the action of the electric motor, the state of the electric motor, and / or input from the operator. One or more parameters of the user profile include at least one of the following: time since the operator previously turned off the electric motor, time since the operator previously turned on the electric motor, the operator's experience, the operator's certification and / or qualifications, the frequency with which the operator turns the electric motor on and / or the tasks assigned to the operator.

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