A method and system for intelligent instrument and meter production task-oriented operation prediction

Abstract

The application relates to a kind of operation prediction method and system for intelligent instrument production task, which comprises: optimizing the part inventory state of enterprise based on genetic algorithm;According to the part inventory state of enterprise, the total processing time of inventory parts, the part assembly time of parts capable of being assembled into components after processing and the debugging inspection time factor of components capable of being assembled into system, the longest time that existing inventory can meet production is calculated to determine the allowed purchase time of the second batch of parts, and operation prediction is completed.The application can calculate the longest time that existing inventory can meet production, and quantitatively determine the allowed purchase time of the second batch of parts.The application can be applied in the field of intelligent production.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent production and manufacturing, and particularly to a method and system for operation prediction for intelligent instrument and meter production tasks. BACKGROUND

[0002] The intelligent instrument and meter industry is developing towards intelligent production, networked collaboration and service extension. It has become a trend for instrument and meter enterprises to use digital technology for production scheduling and intelligent operation. At present, the production of the intelligent instrument and meter industry generally has the characteristics of small batch and multiple types, which has a certain impact on the procurement and inventory management of parts by enterprises. At the same time, the data of enterprises are not well associated with design, manufacturing, service and business management, and the arrangement of production tasks is habitually dependent on the experience of managers, resulting in poor data management and insufficient mining of data implicit value. How to make full use of digital, artificial intelligence and other technologies to carry out predictive operation of intelligent instrument and meter enterprises is conducive to realizing the development mode of exploring intelligent upgrading and digital production of the intelligent instrument and meter industry. SUMMARY

[0003] In view of the above problems, the present application aims to provide a method and system for operation prediction for intelligent instrument and meter production tasks, which can calculate the longest time that the existing inventory can meet production and quantitatively determine the allowed procurement time of the second batch of parts.

[0004] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a method for operation prediction for intelligent instrument and meter production tasks, comprising: optimizing the part inventory state of an enterprise based on a genetic algorithm; calculating the longest time that the existing inventory can meet production according to factors such as the part inventory state of the enterprise, the total processing time of the inventory parts, the part assembly time after processing, and the debugging and testing time of the components assembled from the components, to determine the allowed procurement time of the second batch of parts and complete operation prediction.

[0005] Further, the longest production time is used as the fitness function, and the genetic operation is used on the types and quantities of inventory parts to find the optimal inventory state of the parts.

[0006] Further, the optimization of the part inventory state of the enterprise based on the genetic algorithm comprises:

[0007] Selecting binary coding, randomly generating N binary coding combinations to form an initial population;

[0008] Using the total processing time of the parts as the fitness, the fitness value is calculated;

[0009] Judging whether the preset convergence criterion f j (t)≤f j-1If yes, output the result, otherwise, select a individual P in the population according to the fitness value i (m) according to the selection probability, and randomly select part of the genetic from the current population to the next generation according to the selection probability, thus generating a new population;

[0010] Cross and mutation operations are sequentially performed on the new population, the new population is updated, and the fitness value is calculated until the optimal solution is obtained.

[0011] Further, the fitness value is calculated as follows:

[0012]

[0013] Wherein, f j (t) is the fitness value, POP j (m) is the initial population, m represents the part type, t m is the processing time of a single part.

[0014] Further, a constraint condition is set, if the fitness value satisfies the constraint condition, it is judged whether the convergence criterion is satisfied, otherwise, an individual is randomly generated within the constraint range;

[0015] The constraint condition is that the inventory cost is lower than K.

[0016]

[0017] Wherein, K m is the price of a single part, and K is the inventory cost allowed by the enterprise.

[0018] Further, the allowed purchase time of the second batch of parts is determined as follows: the allowed purchase time of the second batch of parts should be less than or equal to the sum of the total processing time of the inventory parts, the component assembly time and the system debugging and testing time.

[0019] Further, the component assembly time is:

[0020]

[0021] The system debugging and testing time is:

[0022]

[0023] Wherein, f Z (t) is the component assembly time, t Z is the assembly time of a single component, Q is the number of parts that can be assembled into a component after processing, f T (t) is the system debugging and testing time, R is the number of systems that can be composed of Q components, t T is the debugging and testing time of a single system.

[0024] A running prediction system for intelligent instrument production task, comprising: a processing module, which optimizes the part inventory state of an enterprise based on a genetic algorithm; and a prediction module, which calculates the longest time that the existing inventory can satisfy production according to factors such as the part inventory state of the enterprise, the total processing time of the inventory parts, the part assembly time after processing, and the debugging inspection time of the components assembled from the components, to determine the allowed purchase time of the second batch of parts, and complete the running prediction.

[0025] A computer-readable storage medium storing one or more programs, wherein the one or more programs comprise instructions that, when executed by a computing device, cause the computing device to perform any of the above methods.

[0026] A computing device comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise instructions for performing any of the above methods.

[0027] The present application has the following advantages due to the above technical solutions:

[0028] 1. The present application optimizes the part inventory state of an enterprise based on a genetic algorithm, reasonably determines the types and quantities of the inventory parts, ensures reasonable inventory for use when new production tasks are added, and reduces the occupied funds of the inventory.

[0029] 2. The present application calculates the longest time that the existing inventory can satisfy production according to factors such as the part inventory state of the enterprise, the processing, assembly, and debugging time of the parts, quantitatively determines the allowed purchase time of the second batch of parts, and meets the predictive running requirements of the enterprise. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is a running prediction method flowchart for intelligent instrument production tasks in the embodiments of the present application. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0032] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0033] The present application provides a method and system for operation prediction of intelligent instrument production task, which comprises: optimizing the part inventory state of an enterprise based on a genetic algorithm; calculating the longest time that the existing inventory can satisfy production according to the part inventory state of the enterprise, the total processing time of the inventory parts, the part assembly time of parts capable of being assembled into components after processing, and the debugging and testing time of components capable of being assembled into systems, to determine the allowed purchase time of the second batch of parts, and complete operation prediction. The present application can calculate the longest time that the existing inventory can satisfy production, and quantitatively determine the allowed purchase time of the second batch of parts.

[0034] In one embodiment of the present application, a method for operation prediction of intelligent instrument production task is provided. In this embodiment, as shown in Figure 1 , the method comprises the following steps:

[0035] 1) Optimizing the part inventory state of an enterprise based on a genetic algorithm;

[0036] 2) Calculating the longest time that the existing inventory can satisfy production according to the part inventory state of the enterprise, the total processing time of the inventory parts, the part assembly time of parts capable of being assembled into components after processing, and the debugging and testing time of components capable of being assembled into systems, to determine the allowed purchase time of the second batch of parts, and complete operation prediction.

[0037] In step 1) above, the genetic algorithm (GA) is an optimization calculation model simulating Darwin's biological evolution theory, which provides a general framework for solving optimization problems of complex systems. In this embodiment, the longest production time is used as the fitness function, and genetic operations are performed on the types and quantities of inventory parts to find the optimal inventory state of the parts.

[0038] Optimizing the part inventory state of an enterprise based on a genetic algorithm comprises the following steps:

[0039] 1.1) Selecting binary encoding and randomly generating N binary encoding combinations to form an initial population POP j (m);

[0040] Specifically, m = 1, 2, … N. m represents the part type, the number of part 1 corresponds to the first code, the number of part 2 corresponds to the second code, …, and the number of part N corresponds to the Nth code.

[0041] 1.2) Take the total processing time of the parts as the fitness, and calculate the fitness value;

[0042] The fitness value is calculated as:

[0043]

[0044] In the formula, f j (t) is the fitness value, POP j (m) is the initial population, m represents the part type, t m is the processing time of a single part, m = 1, 2, … N. The longest processing time of a single part is t max = max(t m ), and the corresponding part quantity is POP j (max).

[0045] Set a constraint condition, if the fitness value meets the constraint condition, it is judged whether the convergence criterion is met, otherwise a new individual is randomly generated within the constraint range;

[0046] The constraint condition is: the inventory cost is lower than K;

[0047]

[0048] In the formula, K m is the price of a single part, K is the inventory cost allowed by the enterprise, which is a fixed value.

[0049] 1.3) Judge whether the preset convergence criterion f j (t) ≤ f j-1 (t) is met, if yes, output the result, otherwise calculate the selection probability of a certain individual P i (m) in the population (where i represents the part quantity), and randomly select part of the genetic from the current population to the next generation according to the selection probability, thereby generating a new population POP j+1 (m);

[0050] Wherein, the convergence criterion is: f j (t) ≤ f j-1 (t);

[0051] A certain individual P i (m) in the population is:

[0052]

[0053] 1.4) the new population is updated and the fitness value is calculated until the optimal solution is obtained.

[0054] In the embodiment, the crossover operation is: pairing two by two with a probability of Pc, and performing single-point crossover genetic at an optional point in each pair of chromosomes to obtain a population with m chromosomes, and updating the new population POP j+1 (m). j+1

[0055] The mutation operation is: selecting a small probability Pm to mutate the genes of the chromosomes, updating the new population POP j+1 (m); and calculating the fitness value again to repeat the operation until the optimal solution is obtained.

[0056] In the above step 2), the allowed procurement time of the second batch of parts is determined as: the allowed procurement time of the second batch of parts should be less than or equal to the sum of the total processing time of the parts in stock, the assembly time of the components, and the system debugging and testing time, so as to ensure that the production is not interrupted:

[0057] f C (t)≤f j (t)+f Z (t)+f T .

[0058] In the above step 2), the parts after processing can be assembled into Q components, and the assembly time of a single component is t Z , then the assembly time of the components is:

[0059]

[0060] Q components can form R systems, and the debugging and testing time of a single system is t T , then the debugging and testing time of the system is:

[0061]

[0062] In the formula, f Z (t) is the assembly time of the components, t Z is the assembly time of a single component, Q is the number of components that can be assembled from the parts after processing, f T (t) is the debugging and testing time of the system, R is the number of systems that can be formed by the Q components, and t T is the debugging and testing time of a single system.

[0063] In one embodiment of the application, a running prediction system for intelligent instrument production tasks is provided, which comprises:

[0064] A processing module for optimizing the part inventory state of an enterprise based on a genetic algorithm.​

[0065] The forecasting module calculates the maximum time that the existing inventory can meet production needs based on factors such as the company's parts inventory status, the total processing time of the inventory parts, the assembly time of the processed parts into components, and the debugging and inspection time of the components into a system. This determines the allowable procurement time for the second batch of parts and completes the operational forecast.

[0066] In the above processing module, the fitness function is the longest production time. Genetic operations are applied to the types and quantities of inventory parts to find the optimal inventory state of the parts.

[0067] In the above embodiments, optimizing the enterprise's parts inventory status based on genetic algorithms includes:

[0068] Choose binary encoding, and randomly generate N combinations of binary encodings to form the initial population;

[0069] The fitness value is calculated using the total machining time of the part as the fitness level.

[0070] Determine whether the preset convergence criterion f is met. j (t)≤f j-1 If (t) is satisfied, the result is output; otherwise, the fitness value of an individual P in the population is calculated. i The selection probability (m) is used to randomly select a portion of the current population to be inherited by the next generation, thereby generating a new population;

[0071] Perform crossover and mutation operations on the new population in sequence, update the new population, and calculate the fitness value until the optimal solution is obtained.

[0072] The fitness value is calculated as follows:

[0073]

[0074] In the formula, f j (t) represents the fitness value, POP j (m) represents the initial group, m represents the type of part, and t m The processing time for a single part.

[0075] Set constraints; if the fitness value meets the constraints, determine whether the convergence criterion is met; otherwise, randomly generate individuals within the constraints.

[0076] The constraint is: inventory cost is lower than K;

[0077]

[0078] In the formula, K m K represents the price of a single part, and K represents the company's allowed inventory cost.

[0079] In the above embodiment, the allowed purchase time of the second batch of parts is determined as: the allowed purchase time of the second batch of parts should be less than or equal to the sum of the total processing time of the inventory parts, the assembly time of the components, and the system debugging and testing time.

[0080] In the above embodiment, the assembly time of the components is:

[0081]

[0082] The system debugging and testing time is:

[0083]

[0084] In the formula, f Z (t) is the assembly time of the components, t Z is the assembly time of a single component, Q is the number of parts that can be assembled into a component after processing, f T (t) is the system debugging and testing time, R is the number of systems that can be composed of Q components, t T is the debugging and testing time of a single system.

[0085] The system provided in the embodiment is used to execute the above method embodiments, and specific processes and detailed contents are referred to the above embodiments, which will not be described herein.

[0086] In an embodiment of the present application, a computing device is provided, which can be a terminal and can include a processor, a communications interface, a memory, a display screen and an input device. The processor, the communications interface and the memory can communicate with each other through a communication bus. The processor is configured to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program, which is executed by the processor to implement a method for running prediction for intelligent instrument production tasks. The internal memory provides an environment for running of the operating system and the computer program in the non-volatile storage medium. The communications interface is configured to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, a management network, NFC (Near Field Communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computing device, or an external keyboard, touchpad or mouse, etc. The processor can call logical instructions in the memory.

[0087] Moreover, the logic instructions in the storage medium of the above-mentioned embodiments can be implemented in the form of software function units and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or partially contribute to the prior art, or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0088] In an embodiment of the present application, a computer program product is provided, which includes a computer program stored on a non-transitory computer readable storage medium, and the computer program includes program instructions, which, when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments.

[0089] In an embodiment of the present application, a non-transitory computer readable storage medium is provided, which stores server instructions, and the computer instructions enable a computer to perform the method provided by the above-mentioned embodiments.

[0090] The computer readable storage medium provided by the above-mentioned embodiments has similar implementation principles and technical effects to the above-mentioned method embodiments, and will not be described here.

[0091] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks

[0092] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 of the flow or flows and / or blocks Figure 1 of the block or blocks specified in the flow.

[0093] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 of the flow or flows and / or blocks Figure 1 of the block or blocks specified in the flow.

[0094] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the present application; even though the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some of the technical features can be replaced by equivalent technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting the operation of intelligent instruments and meters production tasks, characterized in that, include: Optimize the enterprise's parts inventory status based on genetic algorithms; Based on the company's parts inventory status, the total processing time of the inventory parts, the assembly time of the processed parts into components, and the debugging and inspection time of the components into a system, calculate the maximum time that the existing inventory can meet production needs, in order to determine the allowable procurement time for the second batch of parts and complete the operation forecast. Using the longest production time as the fitness function, the optimal inventory state of the parts is found by applying genetic operations to the types and quantities of inventory parts. The optimization of enterprise parts inventory status based on genetic algorithms includes: Choose binary encoding, and randomly generate N combinations of binary encodings to form the initial population; The fitness value is calculated using the total machining time of the part as the fitness value: ； In the formula, For fitness value, For the initial group, m Indicates the type of part. The processing time for a single part; Determine whether the preset convergence criteria are met. If the fitness condition is met, the result is output; otherwise, the fitness values ​​of a specific individual in the population are calculated. The selection probability is used to randomly select a portion of the current population to be passed on to the next generation, thus generating a new population; in this new population, an individual in the population... for: ； Perform crossover and mutation operations on the new population in sequence, update the new population, and calculate the fitness value until the optimal solution is obtained; Set constraints; if the fitness value satisfies the constraints, determine whether the convergence criterion is met; otherwise, randomly generate individuals within the constraints. The constraint is: inventory cost is lower than K; ； In the formula, represents the price of a single part, and represents the company's allowed inventory cost.

2. The operation prediction method for intelligent instrument production tasks as described in claim 1, characterized in that, The allowable procurement time for the second batch of parts is determined as follows: the allowable procurement time for the second batch of parts is less than or equal to the sum of the total processing time of the inventory parts, the component assembly time, and the system debugging and inspection time.

3. The operation prediction method for intelligent instrument production tasks as described in claim 1, characterized in that, The assembly time for the component is: ； The system debugging and testing time is: ； In the formula, For component assembly time, Let Q be the assembly time for a single component, and Q be the number of components that can be assembled from the processed parts. R represents the system debugging and testing time, and R is the number of systems that can be formed from Q components. This refers to the debugging and testing time for a single system.

4. A system for predicting the operation of intelligent instruments and meters production tasks, used to implement the method for predicting the operation of intelligent instruments and meters production tasks as described in any one of claims 1 to 3, characterized in that, include: The processing module optimizes the enterprise's parts inventory status based on a genetic algorithm; The forecasting module calculates the maximum time that the existing inventory can meet production needs based on factors such as the company's parts inventory status, the total processing time of the inventory parts, the assembly time of the processed parts into components, and the debugging and inspection time of the components into a system. This determines the allowable procurement time for the second batch of parts and completes the operational forecast.

5. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 3.

6. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 3.

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