CN113888655A - Color difference early warning method, system, electronic device and medium - Google Patents
Color difference early warning method, system, electronic device and medium Download PDFInfo
- Publication number
- CN113888655A CN113888655A CN202111137357.0A CN202111137357A CN113888655A CN 113888655 A CN113888655 A CN 113888655A CN 202111137357 A CN202111137357 A CN 202111137357A CN 113888655 A CN113888655 A CN 113888655A
- Authority
- CN
- China
- Prior art keywords
- color difference
- data
- target product
- early warning
- comparison result
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 127
- 230000008569 process Effects 0.000 claims abstract description 85
- 238000013507 mapping Methods 0.000 claims abstract description 40
- 238000001514 detection method Methods 0.000 claims abstract description 37
- 230000004075 alteration Effects 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims description 31
- 238000012545 processing Methods 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 description 11
- 238000004519 manufacturing process Methods 0.000 description 9
- 238000013527 convolutional neural network Methods 0.000 description 7
- 239000011248 coating agent Substances 0.000 description 5
- 238000000576 coating method Methods 0.000 description 5
- 238000002372 labelling Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 125000004122 cyclic group Chemical group 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
Abstract
The invention is suitable for the field of color difference detection, and provides a color difference early warning method, a system, electronic equipment and a medium, wherein the method comprises the following steps: acquiring process data and color difference standard data, and establishing a mapping relation between the process data and the color difference standard data; acquiring current process data of a target product, and configuring current color difference standard data according to the mapping relation and the current process data; acquiring color difference detection data of a target product, and comparing the color difference detection data with the current color difference standard data to acquire a comparison result; if the comparison result does not meet the preset condition, generating early warning information according to the comparison result; by adopting the method, the problem that the chromatic aberration correction period is long due to the fact that the chromatic aberration problem is not found in time by the automobile chromatic aberration data and early warning in the prior art is solved.
Description
Technical Field
The invention relates to the field of color difference detection, in particular to a color difference early warning method, a color difference early warning system, electronic equipment and a medium.
Background
In the production process of the automobile, in order to ensure the qualified appearance of the automobile, the color difference detection of the automobile is required, and early warning information is generated according to the color difference detection result. Most of the color difference early warning methods are that when vehicles in each workshop are produced, a handheld color difference measuring instrument is used for measuring the color difference of the vehicles, and then the measured result is compared with a standard numerical value for judgment; if the difference value between the measurement result and the standard value exceeds the warning range, determining the color difference as color difference early warning; and if the difference value between the measurement result and the standard value exceeds the limit value range, determining that the chromatic aberration exceeds the limit.
Although each automobile is subjected to color difference detection, most of the color difference detection data of the automobiles are stored in a mode of manually recording files such as excel/word and the like, so that the color difference detection data of the automobiles cannot be transmitted, shared and early-warned in time. In addition, most of historical color difference data tables of the automobile are sent periodically, so that the workload of relevant integration data is increased, the time delay of finding problems is delayed, and the cycle of modifying the automobile is longer.
Disclosure of Invention
The invention provides a color difference early warning method, a color difference early warning system, electronic equipment and a medium, and aims to solve the problems that in the prior art, the color difference problem is not found in time by automobile color difference data and the color difference correction period is long due to early warning.
The color difference early warning method provided by the invention comprises the following steps:
acquiring process data and color difference standard data, and establishing a mapping relation between the process data and the color difference standard data;
acquiring current process data of a target product, and configuring current color difference standard data according to the mapping relation and the current process data;
acquiring color difference detection data of a target product, and comparing the color difference detection data with the current color difference standard data to acquire a comparison result;
and if the comparison result does not accord with the preset condition, generating early warning information according to the comparison result.
Optionally, the obtaining process data and color difference standard data, and establishing a mapping relationship between the process data and the color difference standard data includes:
and constructing an initial model, inputting the process data into the initial model, outputting corresponding color difference standard data, training the initial model, and establishing a target model with a mapping relation.
Optionally, the early warning information includes first early warning information and second early warning information, and if the comparison result does not meet a preset condition, the early warning information is generated according to the comparison result, including;
if the comparison result is greater than the early warning value, judging whether the comparison result is greater than a preset threshold value;
if so, generating first early warning information, acquiring first analysis data of which the comparison result does not meet a preset condition, and adjusting process data of the target product according to the first analysis data;
if not, second early warning information is generated, and the color difference state of the target product is obtained according to the comparison result, wherein the color difference state comprises normal and abnormal.
Optionally, the obtaining of the first analysis data that the comparison result does not meet the preset condition, and adjusting the process data of the target product according to the first analysis data includes:
and if the first analysis data is related to the process data of the target product, adjusting the process data of the target product, and configuring color difference standard data according to the adjusted process data and the mapping relation.
Optionally, the obtaining the color difference state of the target product according to the comparison result includes:
if the color difference state of the target product is normal, the process data of the target product is kept unchanged;
and if the color difference state of the target product is abnormal, acquiring second analysis data of which the comparison result does not meet a preset condition, and adjusting the process data of the target product according to the second analysis data.
Optionally, the acquiring color difference detection data of the target product includes:
acquiring color difference data of the target product based on a color difference measuring instrument;
and identifying text data of the color difference measuring instrument and acquiring color difference detection data of the target product.
Optionally, the color difference warning method further includes:
obtaining the model of a target product, and configuring a target object according to the model;
acquiring a color difference processing state of a target product, and transmitting the color difference processing state to a target object;
judging whether the color difference processing state of the target product changes or not;
if yes, obtaining the change content, and transmitting the transformation content to the target object.
The invention also provides a color difference early warning system, which comprises:
the mapping relation establishing module is used for acquiring process data and color difference standard data and establishing a mapping relation between the process data and the color difference standard data;
the standard data configuration module is used for acquiring current process data of a target product and configuring current color difference standard data according to the mapping relation and the current process data;
the comparison result acquisition module is used for acquiring color difference detection data of a target product, comparing the color difference detection data with the current color difference standard data and acquiring a comparison result;
and the early warning information generation module is used for generating early warning information according to the comparison result if the comparison result does not meet the preset condition, and the mapping relation establishment module, the standard data configuration module, the comparison result acquisition module and the early warning information generation module are connected.
The present invention also provides an electronic device comprising: a processor and a memory;
the memory is used for storing a computer program, and the processor is used for executing the computer program stored by the memory so as to enable the electronic equipment to execute the color difference early warning method.
The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the color difference warning method as described above.
As described above, the present invention provides a color difference warning method, system, electronic device, and medium, which have the following advantages: firstly, establishing a mapping relation between process data and color difference standard data by acquiring the process data and the color difference standard data; then obtaining the current process data of the target product, and configuring the current color difference standard data according to the mapping relation and the current process data, thereby realizing reasonable configuration of the current color difference standard data; acquiring color difference detection data of a target product, and comparing the color difference detection data with the current color difference standard data to acquire a comparison result; if the comparison result does not accord with the preset condition, generating early warning information according to the comparison result, thereby realizing timely finding of the color difference problem, facilitating timely solving of the color difference problem, reducing the cycle of correcting the color difference of the automobile, and further improving the production efficiency of the automobile. In addition, the invention also configures the corresponding target object according to the model of the target product and transmits the color difference processing state of the target product to the target object in time, and the target object can acquire the color difference data in time, thereby being convenient for integrating and processing the related data and further being capable of finding and solving the color difference problem more quickly.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart of a color difference warning method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an early warning information generation method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a color difference warning system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The appearance of the automobile is directly attractive to consumers and directly related to whether the belt is purchased or not, so that the quality of the appearance is very important for the automobile. The automobile coating color difference is one of important contents of the appearance performance of an automobile, the automobile color difference is the difference between the color attribute of the same automobile and the color attribute of a standard color plate, and the color difference is closely related to factors such as coating, equipment, construction environment and the like. In order to improve the appearance performance of the automobile, it is necessary to reasonably set the color difference standard data, judge the color difference abnormal data in time, and early warn the data in time.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart of a color difference warning method according to an embodiment of the present invention.
As shown in fig. 1, the color difference warning method includes steps S110 to S140:
s110, acquiring process data and color difference standard data, and establishing a mapping relation between the process data and the color difference standard data;
s120, acquiring current process data of a target product, and configuring current color difference standard data according to the mapping relation and the current process data;
s130, obtaining color difference detection data of a target product, comparing the color difference detection data with the current color difference standard data, and obtaining a comparison result;
and S140, if the comparison result does not meet the preset condition, generating early warning information according to the comparison result.
In step S110 of this embodiment, the process data is process data in the production process of the product, and the color difference standard data may be LAB data. Process data is a factor affecting color difference standard data, including but not limited to environmental data, raw material data, and equipment production data. Optionally, the establishing of the relationship between the process data and the color difference standard data includes: and constructing an initial model, inputting the process data into the initial model, outputting corresponding standard data, training the initial model, and establishing a target model with a mapping relation between the process data and the color difference standard data. The initial model includes, but is not limited to, convolutional neural networks, cyclic neural networks, deep belief neural networks. Specifically, the initial model is a convolutional neural network, and a first sample data set is formed according to the acquired process data and the color difference standard data; dividing the first sample data set into a first training set and a first testing set; inputting process data into a convolutional neural network, outputting corresponding color difference standard data, and training the convolutional neural network by adopting a first test data set; inputting the first test data set into the trained convolutional neural network to obtain a first test result and a true value corresponding to the first test result; and acquiring a first error according to the first test result and the corresponding real value, and updating the trained convolutional neural network by adopting the first error back propagation to acquire a target model with a mapping relation between the process data and the color difference standard data. A cross entropy loss function may be employed to obtain a first error based on the first test result and the corresponding true value.
In step S120 of this embodiment, current color difference standard data is configured reasonably by obtaining current process data of a target product and according to the mapping relationship and the current process data; the set color difference standard data are more accurate, and the appearance performance of the automobile can be improved.
In step S130 of this embodiment, the method for obtaining the color difference detection data of the target product may include: acquiring color difference data of the target product based on a color difference measuring instrument; and identifying text data of the color difference measuring instrument and acquiring color difference detection data of the target product. Specifically, the color difference data can be LAB data, and the measurement method is that workers operate a color difference measuring instrument to measure the specified part of the vehicle body to obtain color difference data under 25 degrees, 45 degrees and 75 degrees; the mode of recognizing the text data of the color difference measuring instrument can be directly recognized by adopting an OCR recognition technology, and can also be recognized by adopting a text recognition model.
In one embodiment, the step of recognizing the text data of the color difference measuring instrument by using the text recognition model comprises the following steps: constructing an initial identification model, acquiring a color difference measuring instrument picture of acquired color difference data, and labeling text data of the picture to acquire a labeling result; forming a sample data set according to the acquired picture and the labeling result; and training an initial recognition model by adopting the sample data set to obtain a text recognition model. In order to improve the identification accuracy of the text identification model to the text data of the color difference measuring instrument, the sample data set can be divided into a second training set and a second test set, the initial identification model is trained by the second training set, the second test set is input into the trained initial identification model, and a second test result is obtained; and acquiring a second error according to the second test result and the labeling result by adopting a loss function, and updating the trained initial recognition model by adopting the second error back propagation to acquire the text recognition model. The initial recognition model includes, but is not limited to, convolutional neural networks, cyclic neural networks.
In step S140 of this embodiment, if the comparison result does not meet the preset condition, please refer to fig. 2 for a specific implementation method of generating the warning information according to the comparison result, and fig. 2 is a schematic flow chart of the warning information generating method according to an embodiment of the present invention.
As shown in fig. 2, the warning information generating method may include the following steps S210 to S230:
s210, if the comparison result is greater than the early warning value, judging whether the comparison result is greater than a preset threshold value;
s220, if yes, generating first early warning information, acquiring first analysis data of which the comparison result does not meet preset conditions, and adjusting process data of the target product according to the first analysis data;
and S220, if not, generating second early warning information, and acquiring the color difference state of the target product according to the comparison result.
In an embodiment, the implementation method for obtaining first analysis data that the comparison result does not meet a preset condition and adjusting the process data of the target product according to the first analysis data includes: judging whether the first analysis data is related to the process data of the target product; if so, adjusting the process data of the target product, and configuring color difference standard data according to the adjusted process data and the mapping relation; if not, adjusting the production process of automobile coating according to the first analysis data. In particular, the manual operation and the plant operation can be adjusted by adjusting the production process of the automobile coating according to the first analysis data.
In one embodiment, the color difference status includes normal and abnormal. The step of obtaining the color difference state of the target product according to the comparison result comprises the following steps: if the color difference state of the target product is normal, the process data of the target product is kept unchanged; and if the color difference state of the target product is abnormal, acquiring second analysis data of which the comparison result does not meet a preset condition, and adjusting the process data of the target product according to the second analysis data. Adjusting the process data of the target product according to the second analysis data comprises: judging whether the second analysis data is related to the process data of the target product; if so, adjusting the process data of the target product, and configuring color difference standard data according to the adjusted process data and the mapping relation; if not, adjusting the production process of automobile coating according to the second analysis data.
In an embodiment, in order to transmit and share color difference data in time, the color difference warning method further includes: obtaining the model of a target product, and configuring a target object according to the model; acquiring a color difference processing state of a target product, and transmitting the color difference processing state to a target object; judging whether the color difference processing state of the target product changes or not; if yes, obtaining the change content, and transmitting the transformation content to the target object. Specifically, the color difference processing state includes, but is not limited to, a color difference acquisition state, a color difference identification state, a color difference early warning state, and a color difference exception handling state. By acquiring the model of the target product, configuring the target object according to the model of the target product and timely transmitting the chromatic aberration processing state of the target product to the target object, the target object can acquire chromatic aberration data in time, and the integration processing of related data is facilitated, so that the chromatic aberration problem can be found and solved more quickly, the cycle of correcting and modifying automobile chromatic aberration is reduced, and the production efficiency of automobiles is improved.
The embodiment of the invention provides a color difference early warning method, which comprises the steps of firstly, acquiring process data and color difference standard data, and establishing a mapping relation between the process data and the color difference standard data; then obtaining the current process data of the target product, and configuring the current color difference standard data according to the mapping relation and the current process data, thereby realizing reasonable configuration of the current color difference standard data; acquiring color difference detection data of a target product, and comparing the color difference detection data with the current color difference standard data to acquire a comparison result; if the comparison result does not accord with the preset condition, generating early warning information according to the comparison result, thereby realizing timely finding of the color difference problem, facilitating timely solving of the color difference problem, reducing the cycle of correcting the color difference of the automobile, and further improving the production efficiency of the automobile. In addition, the invention also configures the corresponding target object according to the model of the target product and transmits the color difference processing state of the target product to the target object in time, and the target object can acquire the color difference data in time, thereby being convenient for integrating and processing the related data and further being capable of finding and solving the color difference problem more quickly.
Based on the same inventive concept as the color difference early warning method, correspondingly, the embodiment also provides a color difference early warning system. In this embodiment, the color difference warning system executes the color difference warning method according to any of the embodiments, and specific functions and technical effects are as described in the embodiments above, which is not described herein again.
Fig. 3 is a schematic structural diagram of a color difference warning system provided in the present invention.
As shown in fig. 3, the color difference warning system includes: the system comprises a 31 mapping relation establishing module, a 32 standard data configuration module, a 33 comparison result obtaining module and a 34 early warning information generating module.
The mapping relation establishing module is used for acquiring process data and color difference standard data and establishing a mapping relation between the process data and the color difference standard data;
the standard data configuration module is used for acquiring current process data of a target product and configuring current color difference standard data according to the mapping relation and the current process data;
the comparison result acquisition module is used for acquiring color difference detection data of a target product, comparing the color difference detection data with the current color difference standard data and acquiring a comparison result;
and the early warning information generation module is used for generating early warning information according to the comparison result if the comparison result does not meet the preset condition, and the mapping relation establishment module, the standard data configuration module, the comparison result acquisition module and the early warning information generation module are connected.
In some exemplary embodiments, the mapping relationship establishing module includes:
and the mapping relation establishing unit is used for establishing an initial model, inputting the process data into the initial model, outputting corresponding color difference standard data, training the initial model and establishing a target model with a mapping relation.
In some exemplary embodiments, the warning information generating module includes:
the judging unit is used for judging whether the comparison result is greater than a preset threshold value or not if the comparison result is greater than the early warning value;
the first early warning information generating unit is used for generating first early warning information if the comparison result does not meet the preset condition, acquiring first analysis data of which the comparison result does not meet the preset condition, and adjusting process data of the target product according to the first analysis data;
and the second early warning information generating unit is used for generating second early warning information if the target product is not in the normal state, and acquiring the color difference state of the target product according to the comparison result, wherein the color difference state comprises a normal state and an abnormal state.
In some exemplary embodiments, the first warning information generating unit includes:
and the first adjusting subunit is configured to adjust the process data of the target product if the first analysis data is related to the process data of the target product, and configure color difference standard data according to the adjusted process data and the mapping relationship.
In some exemplary embodiments, the second warning information generating unit includes:
the data holding subunit is used for keeping the process data of the target product unchanged if the color difference state of the target product is normal;
and the second adjusting subunit is used for acquiring second analysis data of which the comparison result does not meet a preset condition if the color difference state of the target product is abnormal, and adjusting the process data of the target product according to the second analysis data.
In some exemplary embodiments, the alignment result obtaining module includes:
the detection data acquisition unit is used for acquiring color difference detection data of a target product;
a comparison result obtaining unit for comparing the color difference detection data with the current color difference standard data to obtain a comparison result
In some exemplary embodiments, the detection data acquiring unit includes:
the data acquisition subunit is used for acquiring color difference data of the target product based on a color difference measuring instrument;
and the detection data acquisition subunit is used for identifying the text data of the color difference measuring instrument and acquiring the color difference detection data of the target product.
In some exemplary embodiments, the color difference warning system further includes:
the object configuration module is used for obtaining the model of a target product and configuring a target object according to the model;
the first transmission module is used for acquiring the color difference processing state of a target product and transmitting the color difference processing state to a target object;
the state judgment module is used for judging whether the color difference processing state of the target product changes or not;
and the second transmission module is used for acquiring the changed content and transmitting the changed content to the target object if the changed content is the target object.
The present embodiment also provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements any of the methods in the present embodiments.
In an embodiment, referring to fig. 4, the embodiment further provides an electronic device 400, which includes a memory 401, a processor 402, and a computer program stored in the memory and executable on the processor, and when the processor 402 executes the computer program, the steps of the method according to any one of the above embodiments are implemented.
The computer-readable storage medium in the present embodiment can be understood by those skilled in the art as follows: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with a computer program. The aforementioned computer program may be stored in a computer readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The electronic device provided by the embodiment comprises a processor, a memory, a transceiver and a communication interface, wherein the memory and the communication interface are connected with the processor and the transceiver and are used for realizing mutual communication, the memory is used for storing a computer program, the communication interface is used for carrying out communication, and the processor and the transceiver are used for operating the computer program to enable the electronic device to execute the steps of the method.
In this embodiment, the Memory may include a Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In the above-described embodiments, references in the specification to "the present embodiment," "an embodiment," "another embodiment," "in some exemplary embodiments," or "other embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least some embodiments, but not necessarily all embodiments. The various appearances of the phrase "the present embodiment," "one embodiment," or "another embodiment" are not necessarily all referring to the same embodiment.
In the embodiments described above, although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory structures (e.g., dynamic ram (dram)) may use the discussed embodiments. The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A color difference early warning method is characterized by comprising the following steps:
acquiring process data and color difference standard data, and establishing a mapping relation between the process data and the color difference standard data;
acquiring current process data of a target product, and configuring current color difference standard data according to the mapping relation and the current process data;
acquiring color difference detection data of a target product, and comparing the color difference detection data with the current color difference standard data to acquire a comparison result;
and if the comparison result does not accord with the preset condition, generating early warning information according to the comparison result.
2. The color difference early warning method according to claim 1, wherein the obtaining of the process data and the color difference standard data and the establishing of the mapping relationship between the process data and the color difference standard data comprise:
and constructing an initial model, inputting the process data into the initial model, outputting corresponding color difference standard data, training the initial model, and establishing a target model with a mapping relation.
3. The color difference early warning method according to claim 1, wherein the early warning information comprises first early warning information and second early warning information, and if the comparison result does not meet a preset condition, the early warning information is generated according to the comparison result, including;
if the comparison result is greater than the early warning value, judging whether the comparison result is greater than a preset threshold value;
if so, generating first early warning information, acquiring first analysis data of which the comparison result does not meet a preset condition, and adjusting process data of the target product according to the first analysis data;
if not, second early warning information is generated, and the color difference state of the target product is obtained according to the comparison result, wherein the color difference state comprises normal and abnormal.
4. The color difference early warning method according to claim 3, wherein the obtaining of the first analysis data that the comparison result does not meet the preset condition and the adjusting of the process data of the target product according to the first analysis data comprises:
and if the first analysis data is related to the process data of the target product, adjusting the process data of the target product, and configuring color difference standard data according to the adjusted process data and the mapping relation.
5. The color difference early warning method according to claim 3, wherein the obtaining of the color difference state of the target product according to the comparison result comprises:
if the color difference state of the target product is normal, the process data of the target product is kept unchanged;
and if the color difference state of the target product is abnormal, acquiring second analysis data of which the comparison result does not meet a preset condition, and adjusting the process data of the target product according to the second analysis data.
6. The color difference early warning method according to claim 1, wherein the acquiring of the color difference detection data of the target product comprises:
acquiring color difference data of the target product based on a color difference measuring instrument;
and identifying text data of the color difference measuring instrument and acquiring color difference detection data of the target product.
7. The color difference warning method according to claim 1, further comprising:
obtaining the model of a target product, and configuring a target object according to the model;
acquiring a color difference processing state of a target product, and transmitting the color difference processing state to a target object;
judging whether the color difference processing state of the target product changes or not;
if yes, obtaining the change content, and transmitting the transformation content to the target object.
8. A chromatic aberration early warning system, comprising:
the mapping relation establishing module is used for acquiring process data and color difference standard data and establishing a mapping relation between the process data and the color difference standard data;
the standard data configuration module is used for acquiring current process data of a target product and configuring current color difference standard data according to the mapping relation and the current process data;
the comparison result acquisition module is used for acquiring color difference detection data of a target product, comparing the color difference detection data with the current color difference standard data and acquiring a comparison result;
and the early warning information generation module is used for generating early warning information according to the comparison result if the comparison result does not meet the preset condition, and the mapping relation establishment module, the standard data configuration module, the comparison result acquisition module and the early warning information generation module are connected.
9. An electronic device comprising a processor, a memory, and a communication bus;
the communication bus is used for connecting the processor and the memory;
the processor is configured to execute a computer program stored in the memory to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program for causing a computer to perform the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111137357.0A CN113888655A (en) | 2021-09-27 | 2021-09-27 | Color difference early warning method, system, electronic device and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111137357.0A CN113888655A (en) | 2021-09-27 | 2021-09-27 | Color difference early warning method, system, electronic device and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113888655A true CN113888655A (en) | 2022-01-04 |
Family
ID=79007194
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111137357.0A Pending CN113888655A (en) | 2021-09-27 | 2021-09-27 | Color difference early warning method, system, electronic device and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113888655A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1459204A (en) * | 2001-03-16 | 2003-11-26 | 精工爱普生株式会社 | Environment adaptive image display system, information storage medium, and image processing method |
| CN106841568A (en) * | 2017-01-21 | 2017-06-13 | 成都冠禹科技有限公司 | Good farmland soil detection system and its implementation based on technology of Internet of things |
| CN108280494A (en) * | 2018-01-15 | 2018-07-13 | 四川小猕猴科技有限公司 | It being capable of pinpoint assembly line code printing mechanism |
| CN108309302A (en) * | 2018-03-20 | 2018-07-24 | 中南大学湘雅医院 | A kind of surgical operation microcirculation monitoring method and device |
| CN108398187A (en) * | 2018-01-17 | 2018-08-14 | 广汽丰田汽车有限公司 | Chromatism data management system and method, the storage medium of vehicle |
| CN109590237A (en) * | 2018-12-20 | 2019-04-09 | 赣州市南康区万家源家具有限公司 | A kind of device detecting finishing coat heterochromia |
| CN109903256A (en) * | 2019-03-07 | 2019-06-18 | 京东方科技集团股份有限公司 | Model training method, chromatic aberration calibrating method, device, medium and electronic equipment |
| CN111754470A (en) * | 2020-06-11 | 2020-10-09 | 厦门雨程户外运动用品有限公司 | Automatic cloth inspecting method and device, automatic cloth inspecting machine and storage medium |
| CN111783673A (en) * | 2020-07-02 | 2020-10-16 | 南京邮电大学 | Video segmentation improvement method based on OSVOS |
-
2021
- 2021-09-27 CN CN202111137357.0A patent/CN113888655A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1459204A (en) * | 2001-03-16 | 2003-11-26 | 精工爱普生株式会社 | Environment adaptive image display system, information storage medium, and image processing method |
| CN106841568A (en) * | 2017-01-21 | 2017-06-13 | 成都冠禹科技有限公司 | Good farmland soil detection system and its implementation based on technology of Internet of things |
| CN108280494A (en) * | 2018-01-15 | 2018-07-13 | 四川小猕猴科技有限公司 | It being capable of pinpoint assembly line code printing mechanism |
| CN108398187A (en) * | 2018-01-17 | 2018-08-14 | 广汽丰田汽车有限公司 | Chromatism data management system and method, the storage medium of vehicle |
| CN108309302A (en) * | 2018-03-20 | 2018-07-24 | 中南大学湘雅医院 | A kind of surgical operation microcirculation monitoring method and device |
| CN109590237A (en) * | 2018-12-20 | 2019-04-09 | 赣州市南康区万家源家具有限公司 | A kind of device detecting finishing coat heterochromia |
| CN109903256A (en) * | 2019-03-07 | 2019-06-18 | 京东方科技集团股份有限公司 | Model training method, chromatic aberration calibrating method, device, medium and electronic equipment |
| CN111754470A (en) * | 2020-06-11 | 2020-10-09 | 厦门雨程户外运动用品有限公司 | Automatic cloth inspecting method and device, automatic cloth inspecting machine and storage medium |
| CN111783673A (en) * | 2020-07-02 | 2020-10-16 | 南京邮电大学 | Video segmentation improvement method based on OSVOS |
Non-Patent Citations (2)
| Title |
|---|
| 刘波等: "《汽车涂装美容技术问答》", pages: 125 - 127 * |
| 黄虎等: "基于机器视觉的工业品色差检测系统", 《兵工自动化》, vol. 29, no. 07, 31 July 2010 (2010-07-31), pages 78 - 80 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2019095782A1 (en) | Data sample label processing method and apparatus | |
| CN108255897B (en) | Visualized chart data conversion processing method and device | |
| CN110334013B (en) | Decision engine testing method and device and electronic equipment | |
| US20190139334A1 (en) | Reconciling outlier telematics across monitored populations | |
| CN111552600B (en) | Signal testing method, system, device and readable storage medium | |
| WO2020168842A1 (en) | Vehicle damage assessment method and device and electronic device | |
| US20210390802A1 (en) | Method, Computer Program And Device For Processing Signals | |
| CN113888655A (en) | Color difference early warning method, system, electronic device and medium | |
| CN112654999B (en) | Method and device for determining labeling information | |
| CN113607817A (en) | Pipeline girth weld detection method and system, electronic equipment and medium | |
| CN112700817A (en) | Memory device quality evaluation method and device and computer readable storage medium | |
| CN112816959B (en) | Clustering method, device, equipment and storage medium for vehicles | |
| CN113781013A (en) | Coating color point monitoring method, system, medium and terminal | |
| CN113495907A (en) | Product detection method, product detection device, computer device and storage medium | |
| CN113780881A (en) | Coating color difference tracing method, system, medium and terminal | |
| CN113848862A (en) | Diagnostic software acquisition method and device, communication equipment and storage medium | |
| CN112858725A (en) | Vehicle speed consistency detection method, terminal equipment and storage medium | |
| CN113989632A (en) | Bridge detection method and device for remote sensing image, electronic equipment and storage medium | |
| CN113688351A (en) | Method, device, electronic equipment and readable medium for detecting weight of article | |
| CN113139673A (en) | Method, device, terminal and storage medium for predicting air quality | |
| CN113673916B (en) | Risk data identification method, terminal device and computer-readable storage medium | |
| CN112541514A (en) | Event distribution method, server, terminal and storage medium | |
| CN110909067A (en) | Visual analysis system and method for ocean multidimensional data | |
| Young et al. | A flow based architecture for efficient distribution of vehicular information in smart cities | |
| CN113781014B (en) | Coating color difference management method, system, medium and terminal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |