Halogen pot scheduling method, device and equipment based on genetic algorithm
By optimizing the scheduling of braising pots using a genetic algorithm, the problem of long scheduling time for braising pots was solved, enabling rapid scheduling and efficient production of braised products.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-03-03
- Publication Date
- 2026-04-07
AI Technical Summary
In existing technologies, the scheduling of braising pots is time-consuming and cannot be scheduled in a timely manner, resulting in low production efficiency of braised products. Furthermore, manual scheduling is difficult to fully consider the changing constraints.
A genetic algorithm-based scheduling method for braising pots is adopted. By determining the population of braising pots for scheduling, an optimization function is calculated to find the optimal scheduling result, and scheduling instructions are sent to the control equipment to achieve rapid scheduling of braising pots.
It enables rapid scheduling and timely dispatching of braising pots, improves the production efficiency of braised products, and fully considers the constraints of food braising.
Smart Images
Figure CN114626706B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of food braising, and in particular to a method, apparatus and equipment for scheduling braising pots based on genetic algorithms. Background Technology
[0002] In the food braising industry, a large number of braising pots are needed for the production of braised products. To improve the production efficiency of braised products, it is necessary to rationally plan and schedule the process of allocating the production tasks of braised products to the braising pots, that is, to schedule the braising pots. There are many constraints that the scheduling of braising pots must meet, and these constraints change with the actual production needs.
[0003] In existing technology, based on human experience, basic office software is used to generate a table and a rough schedule for the braising pot is obtained. Based on this schedule, the braising pot is scheduled to produce braised products.
[0004] However, in existing technologies, it is difficult for humans to take all the constraints into account, and repeated adjustments are needed to achieve a production-ready schedule, which is time-consuming; furthermore, it makes it impossible to schedule the braising pots in a timely manner to complete the production process. Summary of the Invention
[0005] This application provides a method, apparatus, and equipment for scheduling braising pots based on a genetic algorithm, which solves the problem that the scheduling of braising pots takes a long time and cannot be scheduled in a timely manner to complete the production process.
[0006] In a first aspect, this application provides a method for scheduling brine pots based on a genetic algorithm, the method being applied to electronic devices, the method comprising:
[0007] Based on preset scheduling parameters and adjustment parameters, a first group of braising pot scheduling is determined, and an optimization function is determined based on preset optimization indicators and preset weight information of the optimization indicators. The first group includes M scheduling results, where M is a positive integer greater than 1. The scheduling results are used by each braising pot to produce braised products based on the scheduling results, and the optimization indicators are used to adjust the rationality of the scheduling results.
[0008] Based on the optimization function, and using the first population as a basis, perform iterative optimization calculations to determine the optimal population.
[0009] Based on the optimization function, determine the optimal scheduling result in the optimal population;
[0010] Based on the optimal scheduling result, a retrieval command is sent to the control device, wherein the retrieval command is used to schedule each braising pot for braising processing.
[0011] In one optional implementation, a first group of braising pot scheduling is determined based on preset scheduling parameters and adjustment parameters, including:
[0012] The initial population is determined based on the scheduling parameters, wherein the scheduling parameters characterize the target information and basic information of braised product production, and the scheduling parameters include braised product production task information, braising pot information and material information;
[0013] The initial population is adjusted according to the adjustment parameters to determine the first population; wherein, the adjustment parameters are used to optimize the braising pot scheduling results, and the adjustment parameters include task timeout information and / or task priority information.
[0014] In one optional implementation, the optimization indicators include one or more of the following: unscheduled task ratio, task overtime ratio, completion time ratio, brine pot number ratio, and completion time balance rate of each brine pot; each optimization indicator corresponds to a preset weight information.
[0015] In one optional implementation, based on the optimization function and the first population, an iterative optimization calculation is performed to determine the optimal population, including:
[0016] Repeat the following steps until a preset condition is met, wherein the preset condition is a preset maximum number of iterations; the first population obtained when the preset condition is met is the optimal population:
[0017] Based on the optimization function, the optimization value of each scheduling result in the M scheduling results of the first population is determined, wherein the optimization value is used to characterize the rationality of the scheduling result, and the optimization value and the rationality are negatively correlated.
[0018] Based on the optimization values and preset parameters, the first population is subjected to evolution, mutation and replication processes in sequence to obtain the second population. The second population includes M plus Q scheduling results, where M is a positive integer greater than 1 and Q is a positive integer greater than or equal to 1. The preset parameters include a preset crossover rate threshold and a preset mutation rate threshold.
[0019] The second group is designated as the new first group.
[0020] In one optional implementation, determining the optimal scheduling result of the optimal population based on the optimization function includes:
[0021] Based on the optimization function, determine the optimized value of each scheduling result in the optimal population;
[0022] The scheduling result with the smallest optimization value is determined as the optimal scheduling result.
[0023] In one optional implementation, the step of sequentially performing evolution, mutation, and replication processes on the first population based on each of the optimization values and the preset parameters to obtain a second population includes:
[0024] Based on the optimized values of each scheduling result, the selection probability of each scheduling result is determined. Based on the selection probability and the preset crossover rate threshold, the first population is subjected to evolutionary processing using a roulette wheel method to obtain a first evolved population. The optimized values of the scheduling results and the selection probabilities are negatively correlated. The first evolved population includes M scheduling results, where M is a positive integer greater than 1.
[0025] The first evolutionary population is subjected to mutation processing according to the preset mutation rate threshold to obtain a first mutated population, which includes M scheduling results, where M is a positive integer greater than 1.
[0026] The Q scheduling results with the smallest optimization values in the first population are determined, and the Q scheduling results are copied to the first mutant population to obtain the second population.
[0027] Secondly, this application provides a brine pot scheduling device based on a genetic algorithm, the device being applied to an electronic device, the device comprising:
[0028] The first processing unit is used to determine the first group of braising pot scheduling based on preset scheduling parameters and adjustment parameters, and to determine the optimization function based on preset optimization indicators and preset weight information of the optimization indicators. The first group includes M scheduling results, where M is a positive integer greater than 1. The scheduling results are used by each braising pot to produce braised products based on the scheduling results. The optimization indicators are used to adjust the rationality of the scheduling results.
[0029] The first determining unit is used to perform optimization iterative calculations on the first population based on the optimization function to determine the optimal population.
[0030] The second determining unit is used to determine the optimal scheduling result in the optimal population based on the optimization function.
[0031] The second processing unit is used to send a retrieval instruction to the control device based on the optimal scheduling result, wherein the retrieval instruction is used to schedule each braising pot for braising processing.
[0032] Thirdly, this application provides an electronic device, the electronic device comprising: a memory and a processor;
[0033] Memory; memory for storing executable instructions of the processor;
[0034] The processor is configured to perform the method as described in the first aspect.
[0035] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described in the first aspect.
[0036] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.
[0037] The genetic algorithm-based braising pot scheduling method provided in this application comprises the following steps: determining a first population for braising pot scheduling based on preset scheduling and adjustment parameters, and determining an optimization function based on preset optimization indices and their preset weights; performing iterative optimization calculations on the first population based on the optimization function to determine the optimal population; determining the optimal scheduling result within the optimal population based on the optimization function; and sending a retrieval command to the control equipment based on the optimal scheduling result, wherein the retrieval command is used to schedule each braising pot for braising processing. This method comprehensively considers the constraints of food braising, achieving rapid scheduling of braising pots, and thus enabling timely scheduling of braising pots to complete the production process. Attached Figure Description
[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0039] Figure 1 A flowchart illustrating a brine pot scheduling method based on a genetic algorithm, provided for an embodiment of this application;
[0040] Figure 2 A flowchart of another brine pot scheduling method based on a genetic algorithm provided in an embodiment of this application;
[0041] Figure 3 A schematic diagram of a brine pot scheduling device based on a genetic algorithm is provided in an embodiment of this application;
[0042] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;
[0043] Figure 5 This is a block diagram of a terminal device provided in an embodiment of this application.
[0044] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation
[0045] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0046] In the food braising industry, a large number of braising pots are needed for the production of braised products. To improve the production efficiency of braised products, it is necessary to rationally plan and schedule the process of allocating the production tasks to the braising pots, that is, to schedule the braising pots. The scheduling of braising pots must meet many constraints, such as the limit on the number of braising pots, the limit on the number of times each pot can be used, and the diverse and specific requirements of the production tasks: requirements for the types of braised items, the braising time requirements for each item, the braising sequence requirements, the braising time requirements for different items, and ensuring that the completion time of each braising pot is basically balanced. Furthermore, these constraints change with actual production needs.
[0047] In existing technologies, a rough schedule for a braising pot is obtained by using basic office software to generate a schedule based on human experience. The production of braised products is then scheduled based on this schedule. However, it is difficult for humans to take all the constraints into account, and repeated adjustments are needed to achieve a production-ready schedule. The scheduling process is time-consuming and relies on human experience, so the scheduling scheme is not universal. Furthermore, it leads to the inability to schedule the braising pot in a timely manner to complete the production process.
[0048] The method for scheduling brine pots based on genetic algorithms provided in this application aims to solve the above-mentioned technical problems in the prior art.
[0049] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.
[0050] Figure 1 A flowchart illustrating a genetic algorithm-based brine pot scheduling method provided in this application embodiment is shown. This method is applied to electronic devices, such as... Figure 1As shown, the method includes:
[0051] 101. Based on the preset scheduling parameters and adjustment parameters, determine the first group of braising pot scheduling, and determine the optimization function based on the preset optimization index and the preset weight information of the optimization index. The first group includes M scheduling results, where M is a positive integer greater than 1. The scheduling results are used by each braising pot to produce braised products based on the scheduling results, and the optimization index is used to adjust the rationality of the scheduling results.
[0052] For example, when scheduling is required for the braising pots, preset scheduling parameters and adjustment parameters are obtained from other devices, such as electronic storage devices. Based on these preset scheduling parameters and adjustment parameters, M scheduling results for the braising pots are determined. These scheduling results form a first type group. The scheduling results are used by each braising pot to produce braised products based on the scheduling results. At the same time, in order to generate a reasonable braising pot schedule, the rationality of the scheduling results is adjusted. Different optimization indicators can be set according to actual needs, and a weight coefficient is set for each optimization indicator. The optimization indicators and their corresponding weights are weighted and summed to determine the comprehensive optimization function. The weight coefficient of each optimization indicator can be changed according to actual needs.
[0053] 102. Based on the optimization function and the first population, perform iterative optimization calculations to determine the optimal population.
[0054] Based on the obtained optimization function, and using the heritage algorithm, the optimal population is determined by iterative optimization calculation of the scheduling population for the first population, which includes multiple scheduling results.
[0055] 103. Based on the optimization function, determine the optimal scheduling result in the optimal population.
[0056] For example, each scheduling result in the optimal population is evaluated according to the optimization function to determine the optimal scheduling result in the optimal population.
[0057] 104. Based on the optimal scheduling result, send a retrieval command to the control equipment. The retrieval command is used to schedule each braising pot for braising processing.
[0058] For example, based on the optimal scheduling result, a retrieval command is sent to the control device, and the control device can then schedule each braising pot to perform braising processing based on the optimal scheduling result.
[0059] In this embodiment, the following steps are performed: First, a first population for scheduling the braising pots is determined based on preset scheduling and adjustment parameters; second, an optimization function is determined based on preset optimization indices and their preset weights; third, based on the optimization function and the first population, iterative optimization calculations are performed to determine the optimal population; fourth, the optimal scheduling result within the optimal population is determined based on the optimization function; and fifth, a retrieval command is sent to the control device based on the optimal scheduling result, whereby the retrieval command is used to schedule each braising pot for braising processing. This process comprehensively considers the constraints of food braising, enabling rapid scheduling of the braising pots and allowing for timely scheduling to complete the production process.
[0060] Figure 2 A flowchart illustrating another brine pot scheduling method based on a genetic algorithm provided in this application embodiment. This method is applied to electronic devices, such as... Figure 2 As shown, the method includes:
[0061] 201. Determine the initial population based on the scheduling parameters. The scheduling parameters represent the target information and basic information of the production of braised products. The scheduling parameters include the production task information of braised products, the information of the braising pot, and the material information.
[0062] For example, when scheduling the production of braised products, preset scheduling parameters are retrieved from other devices, such as electronic storage devices, and preliminary calculations are performed based on these parameters to obtain initial scheduling results. Multiple scheduling results constitute an initial population, i.e., the initial population is determined based on the scheduling parameters. The scheduling parameters represent the target and basic information of braised product production, such as the quantity and type of braised products to be produced, and the number of braising pots, based on the production task information, braising pot information, and material information.
[0063] 202. Based on the adjustment parameters, the initial population is adjusted to determine the first population, which includes M scheduling results, where M is a positive integer greater than 1. The scheduling results are used by each braising pot to produce braised products based on the scheduling results. The adjustment parameters are used to optimize the braising pot scheduling results. The adjustment parameters include task timeout information and / or task priority information.
[0064] For example, the initial population is adjusted and optimized based on task timeout information and task priority information, or either task timeout information or task priority information, to obtain the first population. That is, the initial population is adjusted according to the adjustment parameters to determine the first population. The first population includes M scheduling results, where M is a positive integer greater than 1. The scheduling results are used by each braising pot to produce braised products based on the scheduling results.
[0065] In one example, for each scheduling result in the initial population, among the scheduled tasks, try to adjust the earlier tasks to be completed on time; and put the tasks with higher priority before the tasks with lower priority, etc., to form the first population.
[0066] 203. Based on the preset optimization indicators and their preset weights, determine the optimization function, whereby the optimization indicators are used to adjust the rationality of the scheduling results.
[0067] In one example, the optimization metrics include one or more of the following: unscheduled task ratio, task timeout ratio, completion time ratio, number of brine pots ratio, and equalization rate of completion time for each brine pot; each optimization metric corresponds to a preset weight information.
[0068] For example, in order to generate a reasonable schedule for braising pots, the reasonableness of the scheduling results can be adjusted. Different optimization indicators can be set according to actual needs. For example, optimization indicators include one or more of the following: unscheduled task ratio, task overtime ratio, completion time ratio, braising pot number ratio, and completion time balance rate of each braising pot. Each optimization indicator has its fixed value range, such as 0 to 1. A weight coefficient is set for each optimization indicator. By weighted summing of each optimization indicator and its corresponding weight, a comprehensive optimization function can be determined. The weight coefficient of each optimization indicator can be changed according to actual needs.
[0069] In one example, a total task may include multiple tasks. The unscheduled task ratio represents the proportion of tasks that were not scheduled and therefore not produced by the braising pots out of all tasks. The smaller the value, the better the scheduling. Each braising product production task has a completion time requirement. The task overtime ratio represents the proportion of overtime for each task. The smaller the value, the better the scheduling. The completion time ratio represents the relative proportion of the completion times of all tasks. The smaller the value, the better the scheduling. The braising pot number ratio measures the relative proportion of the number of braising pots required to complete the task for each scheduling result. The smaller the value, the better the scheduling. The braising pot completion time balance rate measures the balance of completion times of each braising pot in each scheduling result. The smaller the value, the better the scheduling.
[0070] 204. Based on the optimization function and the first population, perform iterative optimization calculations to determine the optimal population.
[0071] In one example, step 204 includes the following steps:
[0072] Repeat the following steps until a preset condition is met, where the preset condition is a preset maximum number of iterations; the first population obtained when the preset condition is met is the optimal population:
[0073] Based on the optimization function, the optimization value of each scheduling result in the M scheduling results of the first group is determined. The optimization value is used to characterize the rationality of the scheduling result, and there is a negative correlation between the optimization value and the rationality.
[0074] Based on the optimized values of each scheduling result, the selection probability of each scheduling result is determined. Based on the selection probability and the preset crossover rate threshold, the first population is subjected to evolutionary processing using a roulette wheel method to obtain the first evolutionary population. The optimized values of the scheduling results and the selection probabilities are negatively correlated. The first evolutionary population includes M scheduling results, where M is a positive integer greater than 1.
[0075] The first evolutionary population is subjected to mutation processing according to the preset mutation rate threshold to obtain the first mutated population. The first mutated population includes M scheduling results, where M is a positive integer greater than 1.
[0076] Identify the Q scheduling results with the smallest optimal values in the first population, and copy these Q scheduling results to the first mutation population to obtain the second population; wherein the second population includes M plus Q scheduling results, where M is a positive integer greater than 1, and Q is a positive integer greater than or equal to 1, and the preset parameters include a preset crossover rate threshold and a preset mutation rate threshold; the second population is determined as the new first population; wherein the preset condition is a preset maximum number of iterations; the first population obtained when the preset condition is met is the optimal population.
[0077] For example, based on the optimization function, the optimization value of each of the M scheduling results in the first population is determined, where the optimization value is used to characterize the rationality of the scheduling result. There is a negative correlation between the optimization value and the rationality, that is, the smaller the optimization value, the more rational the scheduling result. Based on the optimization value of each scheduling result, the selection probability of each scheduling result is determined. The lower the optimization value, the higher the probability of being selected. Based on the selection probability and a preset crossover rate threshold, the first population is subjected to evolutionary processing using a roulette wheel method to obtain a first evolutionary population, which includes M scheduling results. Based on a preset mutation rate threshold, the first evolutionary population is subjected to mutation processing to obtain a first mutant population. The number of scheduling results in the first mutant population does not increase compared to the first evolutionary population. The Q scheduling results with the smallest optimization values in the first population are determined and copied to the first mutant population to obtain a second population. The second population contains M plus Q scheduling results, where M is a positive integer greater than 1 and Q is a positive integer greater than or equal to 1. The second population is then designated as the new first population. The above steps are repeated until the preset maximum number of iterations is reached. The first population obtained when the preset conditions are met is the optimal population.
[0078] In one example, the optimized value for each scheduling result can be calculated using the following formula:
[0079]
[0080] Among them, F i f is the optimal value of the i-th scheduling result in the population. ij Let r be the j-th optimization index value of the i-th scheduling result in the population. ij Let P be the weight coefficient of the j-th optimization index in the i-th solution, and let P be the number of optimization indexes.
[0081] In one example, the probability of each scheduling outcome being selected can be determined by the following formula:
[0082]
[0083] Where, p i F represents the probability that schedule result i will be selected. i Let M be the optimized value of the i-th scheduling result in the population, and M be the number of scheduling results included in the population.
[0084] 205. Based on the optimization function, determine the optimal value of each scheduling result in the optimal population, and determine the scheduling result with the smallest optimal value as the optimal scheduling result.
[0085] For example, based on the optimization function, each scheduling result in the optimal population is evaluated, the optimization value of each scheduling result in the optimal population is determined, and the scheduling result with the smallest optimization value is determined as the optimal scheduling result in the optimal population.
[0086] 206. Based on the optimal scheduling result, send a retrieval command to the control equipment, wherein the retrieval command is used to schedule each braising pot for braising processing.
[0087] For example, this step is the same as step 104, and will not be repeated here.
[0088] In this embodiment, the following steps are performed: First, a first population for scheduling braising pots is determined based on preset scheduling parameters and adjustment parameters. The scheduling parameters include production task information for braised products, braising pot information, and material information. The adjustment parameters include task timeout information and / or task priority information. An optimization function is obtained based on preset optimization indicators and their preset weights, for example, by weighted summation. The optimization indicators include one or more of the following: unscheduled task ratio, task timeout ratio, completion time ratio, braising pot number ratio, and equalization rate of completion time for each braising pot. Each optimization indicator corresponds to a preset weight. Based on the optimization function and the first population, the population undergoes evolution, mutation, and replication to determine the optimal population. The optimal scheduling result within the optimal population is determined based on the optimization function. Based on the optimal scheduling result, a retrieval command is sent to the control device, allowing the control device to schedule each braising pot for braising processing based on this optimal scheduling result. This comprehensively considers the constraints of food braising, enabling rapid scheduling of braising pots and timely scheduling to complete the production process.
[0089] Figure 3 This application provides a schematic diagram of a brine pot scheduling device based on a genetic algorithm, which is applied to electronic devices, such as... Figure 3 As shown, the device includes:
[0090] The first processing unit 31 is used to determine the first group of braising pot scheduling based on preset scheduling parameters and adjustment parameters, and to determine the optimization function based on preset optimization indicators and preset weight information of the optimization indicators. The first group includes M scheduling results, where M is a positive integer greater than 1. The scheduling results are used by each braising pot to produce braised products based on the scheduling results, and the optimization indicators are used to adjust the rationality of the scheduling results.
[0091] The first determining unit 32 is used to perform optimization iterative calculations of the population based on the first population according to the optimization function, and to determine the optimal population.
[0092] The second determining unit 33 is used to determine the optimal scheduling result in the optimal population based on the optimization function.
[0093] The second processing unit 34 is used to send a retrieval command to the control device based on the optimal scheduling result, wherein the retrieval command is used to schedule each braising pot for braising processing.
[0094] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 4 As shown, the electronic device includes: a memory 51 and a processor 52.
[0095] Memory 51; a memory used to store instructions executable by processor 52.
[0096] The processor 52 is configured to perform the methods provided in the above embodiments.
[0097] Figure 5 This is a block diagram of a terminal device provided in an embodiment of this application. The device may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.
[0098] The device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.
[0099] Processing component 802 typically controls the overall operation of device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.
[0100] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0101] Power supply component 806 provides power to various components of device 800. Power supply component 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to device 800.
[0102] Multimedia component 808 includes a screen that provides an output interface between device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
[0103] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
[0104] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.
[0105] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of device 800, changes in the position of device 800 or a component of device 800, the presence or absence of user contact with device 800, the orientation or acceleration / deceleration of device 800, and temperature changes of device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.
[0106] Communication component 816 is configured to facilitate wired or wireless communication between device 800 and other devices. Device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
[0107] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.
[0108] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of the device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.
[0109] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the methods provided in the above embodiments.
[0110] This application also provides a computer program product, which includes: a computer program stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the solution provided in any of the above embodiments.
[0111] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.
[0112] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.
Claims
1. A method for scheduling brine pots based on genetic algorithms, characterized in that, The method is applied to an electronic device, and the method includes: Based on preset scheduling parameters and adjustment parameters, a first population for braising pot scheduling is determined; specifically, this includes: determining an initial population based on the scheduling parameters, wherein the scheduling parameters characterize the target information and basic information of braised product production, and the scheduling parameters include braised product production task information, braising pot information, and material information; and adjusting the initial population based on the adjustment parameters to determine the first population, wherein the adjustment parameters are used to optimize the braising pot scheduling results, and the adjustment parameters include task timeout information and / or task priority. An optimization function is determined based on preset optimization indicators and preset weight information of the optimization indicators. The first group includes M scheduling results, where M is a positive integer greater than 1. The scheduling results are used by each braising pot to produce braised products based on the scheduling results. The optimization indicators are used to adjust the rationality of the scheduling results. The optimization indicators include one or more of the following: unscheduled task ratio, task overtime ratio, completion time ratio, braising pot number ratio, and completion time balance rate of each braising pot. Each optimization indicator corresponds to a preset weight information. Among them, the unscheduled task ratio represents the ratio of tasks that have not been scheduled and therefore not executed by the braising pots to all tasks; the task overtime ratio represents the overtime ratio of each task; the completion time ratio represents the relative ratio of the completion time of all tasks; the braising pot number ratio is used to measure the relative ratio of the number of braising pots required to complete the task for each scheduling result; and the completion time balance rate of each braising pot is used to measure the balance of the completion time of each braising pot for each scheduling result. Based on the optimization function, and using the first population as a basis, perform iterative optimization calculations to determine the optimal population. Based on the optimization function, determine the optimal scheduling result in the optimal population; Based on the optimal scheduling result, a retrieval command is sent to the control device, wherein the retrieval command is used to schedule each braising pot for braising processing.
2. The method according to claim 1, characterized in that, Based on the optimization function, and using the first population as a basis, iterative optimization calculations are performed to determine the optimal population, including: Repeat the following steps until a preset condition is met, wherein the preset condition is a preset maximum number of iterations; the first population obtained when the preset condition is met is the optimal population: Based on the optimization function, the optimization value of each scheduling result in the M scheduling results of the first population is determined, wherein the optimization value is used to characterize the rationality of the scheduling result, and the optimization value and the rationality are negatively correlated. Based on the optimization values and preset parameters, the first population is subjected to evolution, mutation and replication processes in sequence to obtain the second population. The second population includes M plus Q scheduling results, where M is a positive integer greater than 1 and Q is a positive integer greater than or equal to 1. The preset parameters include a preset crossover rate threshold and a preset mutation rate threshold. The second group is designated as the new first group.
3. The method according to claim 2, characterized in that, Based on the optimization function, the optimal scheduling result of the optimal population is determined, including: Based on the optimization function, determine the optimized value of each scheduling result in the optimal population; The scheduling result with the smallest optimization value is determined as the optimal scheduling result.
4. The method according to claim 2, characterized in that, The step of sequentially performing evolution, mutation, and replication processes on the first population based on each of the optimized values and the preset parameters to obtain the second population includes: Based on the optimized values of each scheduling result, the selection probability of each scheduling result is determined. Based on the selection probability and the preset crossover rate threshold, the first population is subjected to evolutionary processing using a roulette wheel method to obtain a first evolved population. The optimized values of the scheduling results and the selection probabilities are negatively correlated. The first evolved population includes M scheduling results, where M is a positive integer greater than 1. The first evolutionary population is subjected to mutation processing according to the preset mutation rate threshold to obtain a first mutated population, which includes M scheduling results, where M is a positive integer greater than 1. The Q scheduling results with the smallest optimization values in the first population are determined, and the Q scheduling results are copied to the first mutant population to obtain the second population.
5. A brine pot scheduling device based on a genetic algorithm, characterized in that, The device is used in an electronic device, and the device includes: The first processing unit is used to determine a first population for scheduling braising pots based on preset scheduling parameters and adjustment parameters. Specifically, this includes: determining an initial population based on the scheduling parameters, where the scheduling parameters characterize the target and basic information of braising product production, including braising product production task information, braising pot information, and material information; adjusting the initial population based on the adjustment parameters to determine the first population, where the adjustment parameters are used to optimize the braising pot scheduling results, including task timeout information and / or task priority; and determining an optimization function based on preset optimization indicators and preset weight information of the optimization indicators. The first population includes M scheduling results, where M is a positive integer greater than 1, and the scheduling results are used for each braising pot. The production of braised products is carried out based on the scheduling results. The optimization indicators are used to adjust the rationality of the scheduling results. The optimization indicators include one or more of the following: unscheduled task ratio, task overtime ratio, completion time ratio, brine pot number ratio, and completion time balance rate of each brine pot. Each optimization indicator corresponds to a preset weight information. Among them, the unscheduled task ratio represents the ratio of tasks that have not been scheduled and therefore have not been produced by the brine pots to the total number of tasks. The task overtime ratio represents the overtime ratio of each task. The completion time ratio represents the relative ratio of the completion time of all tasks. The brine pot number ratio is used to measure the relative ratio of the number of brine pots required to complete the task in each scheduling result. The completion time balance rate of each brine pot is used to measure the balance of the completion time of each brine pot in each scheduling result. The first determining unit is used to perform optimization iterative calculations on the first population based on the optimization function to determine the optimal population. The second determining unit is used to determine the optimal scheduling result in the optimal population based on the optimization function. The second processing unit is used to send a retrieval instruction to the control device based on the optimal scheduling result, wherein the retrieval instruction is used to schedule each braising pot for braising processing.
6. An electronic device, characterized in that, The electronic device includes: a memory and a processor; Memory; memory for storing instructions executable by the processor; The processor is configured to perform the method as described in any one of claims 1-4.
7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-4.
8. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-4.