1. Full flow production dispatch system based on artificial intelligence, characterized in that, the system includes: the data acquisition unit is configured for acquiring the operation data of the production line; the production line resource estimation unit is configured to calculate a resource value of each production line by using a preset resource estimation model based on the operation data; and the scheduling unit is configured for randomly screening 9 production lines from the production lines, setting a quantity value N based on the calculated resource value of each production line, finding N production lines with the highest resource values, summing all the resource values to obtain a total resource value, connecting the N production lines if the total resource value is above a set threshold value, finishing the scheduling, and if the total resource value is lower than the set threshold value, randomly screening 9 production lines from the production lines again, and performing in a circulating manner until the total resource value is above the set threshold value, thereby finishing the scheduling.

2. The system of claim 1, wherein the operational data includes at least: production line operating status, production line location, and production line throughput; the production line running state comprises three types: normal, failed, and idle.

3. The system of claim 2, wherein the line resource estimation unit comprises: the system comprises an operation data preprocessing unit, a model establishing unit and a calculating unit; the running data preprocessing unit is configured to perform data preprocessing on the running data to obtain preprocessed data; the model establishing unit is configured to establish a calculation model; and the computing unit is configured to substitute the preprocessing data into the computing model based on the established computing model, and compute to obtain the resource value of the production line.

4. The system of claim 3, wherein the computational model created by the model creation unit is represented using the following formula:wherein, C₀Is the resource value; s₀For the value corresponding to the operation state of the production line, in the normal state, S₀1, in fault state, S₀0.5, in idle state, S₀2; t is the throughput of the production line, and sigma is the standard deviation of the throughput of the production line; x is a value corresponding to the production line position, and the calculation process of X is as follows: numbering each production line according to the position sequence, wherein after the numbering is finished, X is a numbered numerical value/10; d₁Is a first intermediate value, d₂Is the second intermediate value.

5. The system of claim 4, wherein the scheduling unit comprises: the random screening unit is configured for randomly screening 9 production lines from the production lines and arranging the screened 9 production lines according to 9 grids; the total resource calculation unit is configured to set a quantity value N based on the calculated resource value of each production line, find N production lines with the highest resource values, and sum all the resource values to obtain a total resource value; and the judging unit is configured for judging whether the calculated total resource is above a set threshold, if the total resource value is above the set threshold, connecting the N production lines to finish the scheduling, and if the total resource value is lower than the set threshold, randomly screening out 9 production lines from the production lines again, and performing circular execution until the total resource value is above the set threshold to finish the scheduling.

6. The system of claim 5, wherein the total resource calculating unit, after calculating the total resource value, further adjusts the total resource value based on the set quantity value N to obtain an adjusted total resource value, and includes: the adjusted total resource value is equal to the total resource value/N; the judgment unit judges based on the obtained adjusted total resource value.

7. An artificial intelligence based full flow production scheduling method based on the system of one of claims 1 to 6, characterized in that the method performs the following steps:

step 1: acquiring operation data of a production line;

step 2, calculating the resource value of each production line by using a preset resource estimation model based on the operation data;

and 3, randomly screening 9 production lines from the production lines, setting a quantity value N based on the resource value of each production line obtained through calculation, finding N production lines with the highest resource values, summing all the resource values to obtain a total resource value, connecting the N production lines if the total resource value is above a set threshold value, finishing the scheduling, and if the total resource value is lower than the set threshold value, randomly screening 9 production lines from the production lines again, and executing in a circulating mode until the total resource value is above the set threshold value, so that the scheduling is finished.

8. The method of claim 7, wherein the operational data includes at least: production line operating status, production line location, and production line throughput; the production line running state comprises three types: normal, failed, and idle.

9. The method of claim 8, wherein the resource estimation model is represented using the following formula:wherein, C₀Is the resource value; s₀For the value corresponding to the operation state of the production line, in the normal state, S₀1 at faultIn the state of S₀0.5, in idle state, S₀2; t is the throughput of the production line, and sigma is the standard deviation of the throughput of the production line; x is a value corresponding to the production line position, and the calculation process of X is as follows: numbering each production line according to the position sequence, wherein after the numbering is finished, X is a numbered numerical value/10; d₁Is a first intermediate value, d₂Is a second intermediate value; n (d)₁) Represents to the first intermediate value d₁And carrying out rounding operation.

10. The method of claim 9, wherein the resource estimation model is represented using the following formula:wherein, C₀Is the resource value; s₀For the value corresponding to the operation state of the production line, in the normal state, S₀1, in fault state, S₀0.5, in idle state, S₀2; t is the throughput of the production line, and sigma is the standard deviation of the throughput of the production line; x is a value corresponding to the production line position, and the calculation process of X is as follows: numbering each production line according to the position sequence, wherein after the numbering is finished, X is a numbered numerical value/10; d₁Is a first intermediate value, d₂Is a second intermediate value; n (d)₂) Represents to the first intermediate value d₂And carrying out rounding operation.

Background

The complex product assembly is a typical discrete assembly and has the characteristics of complex and variable required resources, long assembly and adjustment period and the like, for example, a satellite, an airplane, a ship and the like, the assembly process is carried out around the product, different products have different process routes, the processes of the different process routes are mutually independent, and meanwhile, a lot of uncertain production disturbances exist in the complex product assembly process, which often causes the phenomena of easy deviation between the actual execution process and the production plan, delayed workshop tasks, uneven busy and idle workshop personnel, resource waste and the like.

In recent years, the fourth industrial revolution advocated as leading "smart manufacturing" is changing the way of human learning, working and living, and also continuously pushing the industrial production to merge into industrialization and informatization. The intelligent manufacturing is promoted, the product development period can be effectively shortened, the production efficiency and the product quality are improved, the operation cost and the resource energy consumption are reduced, the intelligent manufacturing is accelerated, and the intelligent manufacturing method has very important significance for improving the adaptability and the flexibility of a supply structure of the manufacturing industry and cultivating economic growth new kinetic energy.

Artificial Intelligence (AI) is also known as intelligent mechanical and machine intelligence, and refers to the intelligence developed by machines manufactured by humans. Artificial intelligence generally refers to techniques for presenting human intelligence through ordinary computer programs. The word also indicates the study of whether and how such an intelligent system can be implemented. Meanwhile, through the progress of medicine, neuroscience, robotics, statistics and the like, the normality prediction considers that many occupations of human beings are gradually replaced by the same.

Be applied to artificial intelligence and make the field with intelligence, not only can promote manufacturing efficiency by a wide margin, can also reduce the error that manual operation brought simultaneously, promote product quality.

Patent No. CN201510432581.0A discloses a method and system for scheduling production line production, which includes scheduling, tracking, feedback, and adjusting stages; the scheduling stage is to acquire the station configuration of the product production line and the information of the product production process to form an initial distribution suggestion; the tracking stage acquires production data of the production line in real time and analyzes and measures the processing capacity of each station on the production line based on the dependency relationship; the feedback stage is to predict that the specified delivery amount can be completed within the specified delivery expectation according to the current production allocation scheme based on the analysis of the processing capacity of the station, coordinate the station processing task by adopting a feedback control method, generate a production adjustment suggestion, and feedback control of assembly line resources and station configuration; the adjusting stage is to adjust the material flow of the production line and the distribution of the process stations according to the production adjusting suggestion obtained in the feedback stage. The invention can scientifically plan production plan, and schedule production resource allocation to ensure the balance inside and among procedures.

The production scheduling is controlled by tracking and acquiring production data of the production line to acquire the processing capacity of each production line. Although the balance inside and among the processes can be ensured, the scheduling cannot be applied to the whole production flow, the intelligent scheduling is not realized fundamentally, and the improvement efficiency is limited.

Disclosure of Invention

In view of the above, the main objective of the present invention is to provide a full-flow production scheduling system and method based on artificial intelligence, which calculate a resource value of each production line by obtaining operation data on the production lines to determine whether the production line can be called for use, so that on one hand, the production line with more idle resources can be screened out in the process to ensure that each production line can maximally utilize resources, and on the other hand, the idle resources can be integrated to improve the work efficiency of the production line.

In order to achieve the purpose, the technical scheme of the invention is realized as follows:

an artificial intelligence based full flow production scheduling system, the system comprising: the data acquisition unit is configured for acquiring the operation data of the production line; the production line resource estimation unit is configured to calculate a resource value of each production line by using a preset resource estimation model based on the operation data; and the scheduling unit is configured for randomly screening 9 production lines from the production lines, setting a quantity value N based on the calculated resource value of each production line, finding N production lines with the highest resource values, summing all the resource values to obtain a total resource value, connecting the N production lines if the total resource value is above a set threshold value, finishing the scheduling, and if the total resource value is lower than the set threshold value, randomly screening 9 production lines from the production lines again, and performing in a circulating manner until the total resource value is above the set threshold value, thereby finishing the scheduling.

Further, the operation data at least comprises: production line operating status, production line location, and production line throughput; the production line running state comprises three types: normal, failed, and idle.

Further, the line resource estimation unit includes: the system comprises an operation data preprocessing unit, a model establishing unit and a calculating unit; the running data preprocessing unit is configured to perform data preprocessing on the running data to obtain preprocessed data; the model establishing unit is configured to establish a calculation model; and the computing unit is configured to substitute the preprocessing data into the computing model based on the established computing model, and compute to obtain the resource value of the production line.

Further, the calculation model established by the model establishing unit is expressed by the following formula:wherein, C₀Is the resource value; s₀For the value corresponding to the operation state of the production line, in the normal state, S₀1, in fault state, S₀0.5, in idle state, S₀2; t is the throughput of the production line, and sigma is the standard deviation of the throughput of the production line; x is a value corresponding to the production line position, and the calculation process of X is as follows: numbering each production line according to the position sequence, wherein after the numbering is finished, X is a numbered numerical value/10; d₁Is a first intermediate value, d₂Is the second intermediate value.

Further, the scheduling unit includes: the random screening unit is configured for randomly screening 9 production lines from the production lines and arranging the screened 9 production lines according to 9 grids; the total resource calculation unit is configured to set a quantity value N based on the calculated resource value of each production line, find N production lines with the highest resource values, and sum all the resource values to obtain a total resource value; and the judging unit is configured for judging whether the calculated total resource is above a set threshold, if the total resource value is above the set threshold, connecting the N production lines to finish the scheduling, and if the total resource value is lower than the set threshold, randomly screening out 9 production lines from the production lines again, and performing circular execution until the total resource value is above the set threshold to finish the scheduling.

Further, after the total resource value is obtained through calculation, the total resource calculation unit further adjusts the total resource value based on the set quantity value N to obtain an adjusted total resource value, including: the adjusted total resource value is equal to the total resource value/N; the judgment unit judges based on the obtained adjusted total resource value.

A full-flow production scheduling method based on artificial intelligence, which executes the following steps:

step 1: acquiring operation data of a production line;

step 2, calculating the resource value of each production line by using a preset resource estimation model based on the operation data;

and 3, randomly screening 9 production lines from the production lines, setting a quantity value N based on the resource value of each production line obtained through calculation, finding N production lines with the highest resource values, summing all the resource values to obtain a total resource value, connecting the N production lines if the total resource value is above a set threshold value, finishing the scheduling, and if the total resource value is lower than the set threshold value, randomly screening 9 production lines from the production lines again, and executing in a circulating mode until the total resource value is above the set threshold value, so that the scheduling is finished.

Further, the operation data at least comprises: production line operating status, production line location, and production line throughput; the production line running state comprises three types: normal, failed, and idle.

Further, the resource estimation model is expressed by the following formula:

wherein, C₀Is the resource value; s₀For the value corresponding to the operation state of the production line, in the normal state, S₀1, in fault state, S₀0.5, in idle state, S₀2; t is the throughput of the production line, and sigma is the standard deviation of the throughput of the production line; x is a value corresponding to the production line position, and the calculation process of X is as follows: numbering each production line according to the position sequence, wherein after the numbering is finished, X is a numbered numerical value/10; d₁Is a first intermediate value, d₂Is a second intermediate value; n (d)₁) Represents to the first intermediate value d₁And carrying out rounding operation.

Further, the resource estimation model is expressed by the following formula:

wherein, C₀Is the resource value; s₀For the value corresponding to the operation state of the production line, in the normal state, S₀1, in fault state, S₀0.5, in idle state, S₀2; t is the throughput of the production line, and sigma is the standard deviation of the throughput of the production line; x is a value corresponding to the production line position, and the calculation process of X is as follows: numbering each production line according to the position sequence, wherein after the numbering is finished, X is a numbered numerical value/10; d₁Is a first intermediate value, d₂Is a second intermediate value; n (d)₂) Represents to the first intermediate value d₂And carrying out rounding operation.

According to the full-flow production scheduling system and method based on artificial intelligence, the running data on the production lines are obtained, and the resource value of each production line is calculated to judge whether the production line can be called for use, so that on one hand, the production lines with more idle resources can be screened out in the process to ensure that each production line can maximally utilize the resources, on the other hand, the idle resources can be integrated, and the working efficiency of the production lines is improved; the method is mainly realized by the following steps: 1. and (3) scheduling production line resources through resource values: the invention realizes the production line scheduling by calculating the resource value of each production line so as to ensure that the resource utilization rate of each production line can reach the maximum, and meanwhile, the invention performs uniform scheduling instead of single scheduling aiming at the flow of the production line or the running state of the production line in the scheduling process, and all factors are included in the resource value calculation, thereby realizing the comprehension of the scheduling; 2. calculation of resource value: the resource calculation of the production line is realized by constructing a resource calculation model of the production line, and the difference from the prior art is that the resources of the production line are estimated mostly by a single factor in the prior art; 3. the diversification of the scheduling is realized by setting values: the invention realizes the dispatching of a plurality of production lines by setting different values, and is not limited to dispatching one or more production lines, thereby improving the applicability of the dispatching.

Drawings

Fig. 1 is a schematic structural diagram of a system of an artificial intelligence-based full-process production scheduling system of the internet of things according to an embodiment of the present invention;

FIG. 2 is a schematic diagram illustrating a scheduling principle of the artificial intelligence-based full-process production scheduling system and method according to an embodiment of the present invention when the value N is 3;

fig. 3 is a schematic diagram illustrating a scheduling principle of the artificial intelligence based full-process production scheduling system and method when the N value is 4 according to the embodiment of the present invention.

Detailed Description

The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.

Example 1

As shown in fig. 1, the system for full-flow production scheduling based on artificial intelligence comprises: the data acquisition unit is configured for acquiring the operation data of the production line; the production line resource estimation unit is configured to calculate a resource value of each production line by using a preset resource estimation model based on the operation data; and the scheduling unit is configured for randomly screening 9 production lines from the production lines, setting a quantity value N based on the calculated resource value of each production line, finding N production lines with the highest resource values, summing all the resource values to obtain a total resource value, connecting the N production lines if the total resource value is above a set threshold value, finishing the scheduling, and if the total resource value is lower than the set threshold value, randomly screening 9 production lines from the production lines again, and performing in a circulating manner until the total resource value is above the set threshold value, thereby finishing the scheduling.

Specifically, the method and the device can be used for judging whether the production line can be called for use or not by acquiring the operation data on the production lines and calculating the resource value of each production line, so that on one hand, the production lines with more idle resources can be screened out in the process to ensure that each production line can maximally utilize the resources, on the other hand, the idle resources can be integrated, and the working efficiency of the production lines is improved; the method is mainly realized by the following steps: 1. and (3) scheduling production line resources through resource values: the invention realizes the production line scheduling by calculating the resource value of each production line so as to ensure that the resource utilization rate of each production line can reach the maximum, and meanwhile, the invention performs uniform scheduling instead of single scheduling aiming at the flow of the production line or the running state of the production line in the scheduling process, and all factors are included in the resource value calculation, thereby realizing the comprehension of the scheduling; 2. calculation of resource value: the resource calculation of the production line is realized by constructing a resource calculation model of the production line, and the difference from the prior art is that the resources of the production line are estimated mostly by a single factor in the prior art; 3. the diversification of the scheduling is realized by setting values: the invention realizes the dispatching of a plurality of production lines by setting different values, and is not limited to dispatching one or more production lines, thereby improving the applicability of the dispatching.

Example 2

On the basis of the above embodiment, the operation data at least includes: production line operating status, production line location, and production line throughput; the production line running state comprises three types: normal, failed, and idle.

Example 3

On the basis of the above embodiment, the line resource estimation unit includes: the system comprises an operation data preprocessing unit, a model establishing unit and a calculating unit; the running data preprocessing unit is configured to perform data preprocessing on the running data to obtain preprocessed data; the model establishing unit is configured to establish a calculation model; and the computing unit is configured to substitute the preprocessing data into the computing model based on the established computing model, and compute to obtain the resource value of the production line.

In particular, for some complex parts, multiple processes are generally required. In particular, key parts of a car power assembly, such as an engine cylinder cover, a valve body and the like, involve a plurality of working procedures, a plurality of devices and a complex process. In the production line process planning scheme, the arrangement of the equipment and the planning of the process scheme are particularly important. Because each line occupies a large area and the area of a common factory building is limited, the pressure on a factory is great when a single line processes a single product, especially for a verification line for verifying processes and equipment, the total processing time of the single product is long, and the single line is designed independently, so that the cost is increased, and the resource is wasted.

Example 4

On the basis of the above embodiment, the calculation model established by the model establishing unit is represented by the following formula:wherein, C₀Is the resource value; s₀For the value corresponding to the operation state of the production line, in the normal state, S₀1, in fault state, S₀0.5, in idle state, S₀2; t is the throughput of the production line, and sigma is the standard deviation of the throughput of the production line; x is a value corresponding to the production line position, and the calculation process of X is as follows: numbering each production line according to the position sequence, wherein after the numbering is finished, X is a numbered numerical value/10; d₁Is a first intermediate value, d₂Is the second intermediate value.

Specifically, the production line becomes a production line, which is a production mode in industry, and each production unit only focuses on the work of processing a certain segment, so as to improve the work efficiency and the yield.

The conveying mode according to the assembly line can be roughly divided into: seven types of assembly lines, namely a belt assembly line, a plate chain line, a speed doubling chain, a plug-in line, a mesh belt line, a suspension line and a roller assembly line. Generally comprises a traction piece, a bearing member, a driving device, a tensioning device, a direction-changing device, a supporting piece and the like.

The production line has high expandability, and can design conveying capacity, conveying speed, assembly stations and auxiliary components (comprising quick connectors, fans, lamps, sockets, process boards, object placing tables, 24V power supplies, air batches and the like according to requirements, so that the production line is popular in enterprises.

Example 5

On the basis of the previous embodiment, the scheduling unit includes: the random screening unit is configured for randomly screening 9 production lines from the production lines and arranging the screened 9 production lines according to 9 grids; the total resource calculation unit is configured to set a quantity value N based on the calculated resource value of each production line, find N production lines with the highest resource values, and sum all the resource values to obtain a total resource value; and the judging unit is configured for judging whether the calculated total resource is above a set threshold, if the total resource value is above the set threshold, connecting the N production lines to finish the scheduling, and if the total resource value is lower than the set threshold, randomly screening out 9 production lines from the production lines again, and performing circular execution until the total resource value is above the set threshold to finish the scheduling.

Specifically, referring to fig. 2 and 3, each of the nine-squares in fig. 2 and 3 represents one production line, which is illustrated in fig. 2 when the set N value is 3. When the set value is 4, the graph is shown in fig. 3. After the total resource value is calculated, the scheduling unit connects all the production lines.

Example 6

On the basis of the previous embodiment, after the total resource calculation unit calculates the total resource value, the total resource calculation unit further adjusts the total resource value based on the set quantity value N to obtain an adjusted total resource value, including: the adjusted total resource value is equal to the total resource value/N; the judgment unit judges based on the obtained adjusted total resource value.

Example 7

A full-flow production scheduling method based on artificial intelligence, which executes the following steps:

step 1: acquiring operation data of a production line;

step 2, calculating the resource value of each production line by using a preset resource estimation model based on the operation data;

and 3, randomly screening 9 production lines from the production lines, setting a quantity value N based on the resource value of each production line obtained through calculation, finding N production lines with the highest resource values, summing all the resource values to obtain a total resource value, connecting the N production lines if the total resource value is above a set threshold value, finishing the scheduling, and if the total resource value is lower than the set threshold value, randomly screening 9 production lines from the production lines again, and executing in a circulating mode until the total resource value is above the set threshold value, so that the scheduling is finished.

Example 8

On the basis of the above embodiment, the operation data at least includes: production line operating status, production line location, and production line throughput; the production line running state comprises three types: normal, failed, and idle.

Example 9

On the basis of the above embodiment, the resource estimation model is expressed by using the following formula:wherein, C₀Is the resource value; s₀For the value corresponding to the operation state of the production line, in the normal state, S₀1 in fault state，S₀0.5, in idle state, S₀2; t is the throughput of the production line, and sigma is the standard deviation of the throughput of the production line; x is a value corresponding to the production line position, and the calculation process of X is as follows: numbering each production line according to the position sequence, wherein after the numbering is finished, X is a numbered numerical value/10; d₁Is a first intermediate value, d₂Is a second intermediate value; n (d)₁) Represents to the first intermediate value d₁And carrying out rounding operation.

Example 10

On the basis of the above embodiment, the resource estimation model is expressed by using the following formula:wherein, C₀Is the resource value; s₀For the value corresponding to the operation state of the production line, in the normal state, S₀1, in fault state, S₀0.5, in idle state, S₀2; t is the throughput of the production line, and sigma is the standard deviation of the throughput of the production line; x is a value corresponding to the production line position, and the calculation process of X is as follows: numbering each production line according to the position sequence, wherein after the numbering is finished, X is a numbered numerical value/10; d₁Is a first intermediate value, d₂Is a second intermediate value; n (d)₂) Represents to the first intermediate value d₂And carrying out rounding operation.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.

It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional units, and in practical applications, the functions may be distributed by different functional units according to needs, that is, the units or steps in the embodiments of the present invention are further decomposed or combined, for example, the units in the foregoing embodiment may be combined into one unit, or may be further decomposed into multiple sub-units, so as to complete all or the functions of the units described above. The names of the units and steps involved in the embodiments of the present invention are only for distinguishing the units or steps, and are not to be construed as unduly limiting the present invention.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

Those of skill in the art would appreciate that the various illustrative elements, method steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the elements, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or unit/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or unit/apparatus.

So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent modifications or substitutions of the related art marks may be made by those skilled in the art without departing from the principle of the present invention, and the technical solutions after such modifications or substitutions will fall within the protective scope of the present invention.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.