Intelligent control method and system for television assembly line

Abstract

The present invention relates to the field of television assembly technology, and provides a method and system for intelligent control of a television assembly line, the method comprising: constructing an assembly line network; allocating assembly area key information to each assembly area; obtaining a real-time attention degree and a real-time attention trend value for each assembly area; obtaining a target television assembly line, obtaining a multi-dimensional first characteristic parameter, and obtaining a monitoring index for each assembly area based on the multi-dimensional first characteristic parameter, the real-time attention degree, and the real-time attention trend value; adjusting a monitoring strategy and an assembly quality judgment standard for the assembly area corresponding to the monitoring index based on the monitoring index, and implementing monitoring and assembly of the target television assembly line based on the adjusted monitoring strategy and assembly quality judgment standard. The present invention can effectively improve the accuracy and flexibility of decision-making control parameters of the television assembly line, and improve the degree of intelligent automation of the television assembly line.

Description

Technical Field

[0001] The present invention relates to the technical field of television assembly, and in particular to an intelligent control method and system for a television assembly line. Background Art

[0002] Television assembly lines can be divided into single-product assembly lines, multi-product batch assembly lines, and multi-product assembly lines based on product batch categories. Single-product assembly lines are mainly used to assemble parts for televisions of a single specification; multi-product batch assembly lines assemble multiple televisions in batches; and multi-product assembly lines assemble televisions of multiple specifications simultaneously. These assembly lines generally include processes such as conveying, assembly, and testing. For the assembly process, most are equipped with equipment such as automatic soldering machines and automatic screw machines to improve their degree of automation and production efficiency. Furthermore, assembly lines are often equipped with corresponding scanning programs that can distinguish batches or specifications of televisions and allocate them for production, thereby greatly reducing the possibility of misordering.

[0003] For example, Chinese invention patents with application number "CN201510207133.0," "A Television Automated Assembly System and Automated Screw Assembly Equipment Thereof," "CN201410665858.X," "A Method for Optimizing Scheduling of the Production and Assembly Process of LCD Televisions," and "CN201910940893.0," "Method for Controlling a Robotic Arm for a Television Assembly Line," all involve television assembly lines and can improve television assembly production efficiency to a certain extent. However, none of these television assembly lines utilize multi-dimensional data to calculate monitoring indices for different areas. Consequently, they are unable to dynamically adjust monitoring strategies and assembly quality judgment criteria for different areas based on the monitoring indices. Consequently, they lack flexibility and leave room for further improvement in automation and intelligence. Summary of the Invention

[0004] Based on this, in order to solve the problem that the existing television assembly line does not combine multi-dimensional data to calculate the monitoring index of different areas, and cannot dynamically adjust the monitoring strategy and assembly quality judgment standard of different areas based on the monitoring index, the present invention provides a television assembly line intelligent control method, which constructs an assembly line network including servers of multiple television assembly lines, combines the real-time attention degree, real-time attention trend value and multi-dimensional first characteristic parameters of each assembly area in the target television assembly line, and can flexibly and dynamically adjust the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index based on the monitoring index, thereby improving the intelligence and automation level of the television assembly line. The specific technical solution is as follows:

[0005] A method for intelligently controlling a television assembly line comprises the following steps:

[0006] Based on preset rules, constructing an assembly line network including a plurality of servers, wherein each of the servers corresponds to a television assembly line;

[0007] For each television assembly line in the assembly line network, the television assembly line is divided into a plurality of different assembly areas, and assembly area key information is allocated to each of the assembly areas;

[0008] According to the key information of the assembly area, obtaining the real-time attention degree and real-time attention trend value of each assembly area;

[0009] Obtain a target television assembly line, and for other television assembly lines except the target television assembly line, obtain multi-dimensional first feature parameters through servers corresponding to the other television assembly lines, and obtain a monitoring index for each assembly area in the target television assembly line based on the multi-dimensional first feature parameters, the real-time attention degree, and the real-time attention trend value;

[0010] According to the monitoring index, the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index are adjusted, and the monitoring and assembly of the target TV assembly line are achieved according to the adjusted monitoring strategy and assembly quality judgment standard.

[0011] The television assembly line intelligent control system constructs an assembly line network including multiple servers, divides the television assembly line into multiple different assembly areas, and assigns assembly area key information to each assembly area. Then, based on the assembly area key information, the real-time attention and real-time attention trend value of each assembly area are obtained, and the monitoring index of each assembly area in the target television assembly line is calculated in combination with the real-time attention, real-time attention trend value of each assembly area and the multi-dimensional first characteristic parameters of other television assembly lines. When obtaining the monitoring index, it takes different data of multiple dimensions into consideration, and can flexibly and dynamically adjust the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index based on the monitoring index, which can effectively improve the accuracy and flexibility of the decision-making control parameters of the television assembly line, and improve the degree of intelligence and automation of the television assembly line.

[0012] Preferably, the specific method for obtaining the real-time attention degree and the real-time attention trend value of each assembly area according to the key information of the assembly area includes the following steps:

[0013] Selecting multiple search engines, and obtaining real-time popularity data and search volume data of key information of the assembly area through the multiple search engines;

[0014] The real-time attention degree is obtained according to the real-time heat data, and the real-time attention trend value is obtained according to the search volume data.

[0015] Preferably, the specific method of obtaining the multi-dimensional first characteristic parameter through the server corresponding to the other television assembly line includes the following steps:

[0016] Constructing a feature parameter sharing rule, wherein the feature parameter sharing rule includes the type of feature parameters involved in sharing and the update frequency;

[0017] According to the characteristic parameter sharing rule, the multi-dimensional first characteristic parameters are obtained from the servers corresponding to other television assembly lines.

[0018] Preferably, the specific method for obtaining the monitoring index of each assembly area in the target television assembly line according to the multi-dimensional first characteristic parameter, the real-time attention degree, and the real-time attention trend value includes the following steps:

[0019] respectively obtaining the correlation between the target TV assembly line and other TV assembly lines;

[0020] Obtaining a multi-dimensional second characteristic parameter of each search engine, and obtaining a search engine weight coefficient according to the multi-dimensional second characteristic parameter;

[0021] A monitoring index of each assembly area in the target television assembly line is obtained according to the correlation degree, the search engine weight coefficient, the multi-dimensional first characteristic parameter, the real-time attention degree and the real-time attention trend value.

[0022] Preferably, the search engine weight coefficient

[0023] Among them, m represents the total number of dimensions of the multi-dimensional second characteristic parameter, p' 2i represents the standard value of the second characteristic parameter of the i-th dimension, p 2i represents the second characteristic parameter value of the i-th dimension, λ i Represents the adjustment coefficient of the second characteristic parameter value of the i-th dimension, u i represents an intermediate variable, and e represents a natural constant.

[0024] An intelligent control system for a television assembly line, used to implement the intelligent control method for a television assembly line, comprising:

[0025] A network construction module, configured to construct an assembly line network comprising a plurality of servers based on preset rules, wherein each server corresponds to a television assembly line;

[0026] a key information allocation module, configured to divide each television assembly line in the assembly line network into a plurality of different assembly areas, and allocate assembly area key information to each of the assembly areas;

[0027] A real-time attention information acquisition module is used to obtain the real-time attention degree and real-time attention trend value of each assembly area according to the key information of the assembly area;

[0028] A first characteristic parameter acquisition module is configured to acquire a target television assembly line and, for other television assembly lines except the target television assembly line, acquire multi-dimensional first characteristic parameters through servers corresponding to the other television assembly lines;

[0029] a monitoring index acquisition module, configured to acquire a monitoring index for each assembly area in the target television assembly line according to the multi-dimensional first characteristic parameter, the real-time attention degree, and the real-time attention trend value;

[0030] A monitoring and assembly module is used to adjust the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index according to the monitoring index, and to realize the monitoring and assembly of the target TV assembly line according to the adjusted monitoring strategy and assembly quality judgment standard.

[0031] Preferably, the real-time attention information acquisition module includes:

[0032] A search engine selection unit, configured to select a plurality of search engines and obtain real-time popularity data and search volume data of the key information of the assembly area through the plurality of search engines;

[0033] A real-time attention information acquisition unit is used to obtain the real-time attention degree according to the real-time heat data, and to obtain the real-time attention trend value according to the search volume data.

[0034] Preferably, the first characteristic parameter acquisition module includes:

[0035] A rule building unit, configured to build a feature parameter sharing rule, wherein the feature parameter sharing rule includes the type of feature parameters involved in sharing and the update frequency;

[0036] The first characteristic parameter acquisition unit is used to acquire multi-dimensional first characteristic parameters from servers corresponding to other television assembly lines according to the characteristic parameter sharing rule.

[0037] Preferably, the monitoring index acquisition module includes:

[0038] a correlation degree obtaining unit, configured to respectively obtain the correlation degrees between the target television assembly line and other television assembly lines;

[0039] A search engine weight coefficient acquisition unit, configured to acquire a multi-dimensional second characteristic parameter of each search engine, and acquire a search engine weight coefficient according to the multi-dimensional second characteristic parameter;

[0040] A monitoring index acquisition unit is used to obtain the monitoring index of each assembly area in the target TV assembly line according to the correlation, the search engine weight coefficient, the multi-dimensional first feature parameter, the real-time attention degree and the real-time attention trend value.

[0041] Preferably, the multi-dimensional first characteristic parameters include appearance defect parameters, size and shape defect parameters, electrical connection defect parameters and functional defect parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but rather the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0043] Figure 1 This is a schematic diagram of the overall process of an intelligent control method for a television assembly line according to one embodiment of the present invention;

[0044] Figure 2 It is a flowchart of a specific method for obtaining the real-time attention degree and real-time attention trend value of each assembly area in one embodiment of the present invention;

[0045] Figure 3 1 is a flow chart of a specific method for obtaining a multi-dimensional first characteristic parameter in one embodiment of the present invention;

[0046] Figure 4 1 is a flow chart of a specific method for obtaining a monitoring index of each assembly area in a target television assembly line according to an embodiment of the present invention;

[0047] Figure 5 It is a schematic diagram of the overall structure of an intelligent control of a television assembly line in one embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with its embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0049] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly attached to the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only implementation methods.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0051] The "first" and "second" in the present invention do not represent specific quantities and orders, but are only used to distinguish names.

[0052] Before describing the embodiments of the present invention, a brief introduction to the prior art is given first.

[0053] For TV assembly lines, such as those produced by OEMs, there are often more than one. For multiple OEM TV assembly lines, TVs from different models in the same series, or from different batches of the same model, often share most of the same parts or components, with only minor differences, and similar production conditions and quality standards. Therefore, once a target TV assembly line is selected, production data from other related lines becomes valuable as a reference, allowing adjustments to control decisions (such as monitoring strategies or assembly quality assessments) for that target line.

[0054] Furthermore, due to differences in monitoring resources such as visual inspection cameras, monitors, and computers across different TV assembly lines, the monitoring strategy and level of monitoring for different assembly areas are generally adjusted based on the actual situation of the assembly line. Quality control and assembly quality assessment for different areas of the TV assembly line, and even for the entire production line, can also be adjusted based on actual conditions. For example, the direction and intensity of quality inspection and control can be adjusted based on consumer interest in certain features of the TV.

[0055] After extensive searching, no TV assembly line control method has been found that combines multi-dimensional data related to the TV assembly line and consumer attention to dynamically adjust the monitoring strategies of different areas and the assembly quality judgment standards.

[0056] To this end, an embodiment of the present invention provides an intelligent control method for a television assembly line, one of the purposes of which is to solve the problems existing in the television assembly line in the prior art mentioned above, such as Figure 1 As shown, the intelligent control method for the television assembly line includes the following steps:

[0057] S1. Based on preset rules, construct an assembly line network including multiple servers, wherein each server corresponds to a television assembly line.

[0058] The preset rules, set by technicians or administrators, can be used to construct an assembly line network consisting of multiple servers based on the degree of connectivity between TV assembly lines. For example, an assembly line network can be constructed based on the series, model, and batch of TVs assembled by the TV assembly lines. Servers for TV assembly lines that produce different batches of the same model in the same series, or different models and batches of the same series within a preset time period, such as one year or six months, can be assigned to the assembly line network.

[0059] The preset rules can also be generated based on the similarity between the panel areas of the televisions assembled by the television assembly line. For example, a general television includes panel areas such as a power board, a main board, a screen, a casing, speakers, screen cables, a backlight module, and an adapter board. For televisions of the same model or different models in the same series, the structures of some panel areas are mostly the same or similar, such as speakers, adapter boards, etc. The preset rules are generated based on the number of panel areas with the same or similar structures of the televisions assembled by the television assembly line, that is, the number of panel areas with the same or similar structures reaching a preset value is used as a preset rule, and the servers of the corresponding television assembly line are divided into a set, and an assembly line network including multiple servers is constructed based on the set.

[0060] During assembly, production data from different TV assembly lines can be shared for reference. A simple example is timely adjustments to TV assembly control decision parameters and / or quality inspection criteria based on the specific yield rate or defects found in a specific area across multiple production lines. For example, when yield rates decrease or defects increase, quality inspection criteria can be increased, while process parameters can be optimized to improve production efficiency and quality across the TV assembly lines.

[0061] In this way, by building an assembly line network including multiple servers and sharing the television production data in multiple television assembly line servers based on big data technology, the production quality of the television assembly line can be improved and its degree of automation and intelligence can be improved.

[0062] S2, for each television assembly line in the assembly line network, dividing the television assembly line into a plurality of different assembly areas, and allocating assembly area key information to each of the assembly areas.

[0063] Specifically, a TV assembly line can be divided into multiple assembly areas based on the TV's functional modules. Each assembly area serves as a process station for assembling the corresponding functional module structure. For example, a TV assembly line can be divided into assembly areas including the power board, motherboard, screen, chassis, speakers, screen cables, backlight module, and adapter board.

[0064] In addition, the functional block areas can be further refined according to specific functions. For example, the power board mostly includes circuit structures such as rectification, current stabilization, filtering, voltage transformation, and heat dissipation, and the casing includes structures such as the front frame, middle frame and back cover. Then, according to these refined functional block areas, the TV assembly line is divided into multiple different assembly areas, that is, the TV assembly line is divided into assembly areas including power board rectification, current stabilization, filtering, voltage transformation, heat dissipation and casing front frame, middle frame and back cover.

[0065] By dividing the assembly area into multiple distinct zones, it's easier to assign key assembly area information to each zone, thereby obtaining more accurate real-time attention and trend values ​​for each zone. Generally speaking, the more detailed the assembly area division, the more accurate the real-time attention and trend values ​​obtained for each zone. This allows for more timely adjustments to TV assembly control decision parameters and / or quality inspection criteria based on production data shared by the assembly line network server, optimizing control process parameters and improving production efficiency and quality of the TV assembly line.

[0066] The key assembly area information assigned to each assembly area includes at least one keyword that can be used to describe the structural or functional characteristics of the assembly area. Preferably, the key assembly area information assigned to each assembly area includes multiple keywords to obtain more accurate real-time attention and real-time attention trend values, thereby timely and accurately adjusting the television assembly control decision parameters and / or quality inspection quality judgment criteria, and optimizing the control process parameters.

[0067] S3, obtaining the real-time attention degree and the real-time attention trend value of each assembly area according to the key information of the assembly area.

[0068] In step S3, if Figure 2 As shown, as a preferred technical solution, the specific method for obtaining the real-time attention degree and real-time attention trend value of each assembly area includes the following steps:

[0069] S31, selecting multiple search engines, and obtaining real-time popularity data and search volume data of the key information of the assembly area through the multiple search engines.

[0070] Here, multiple search engines include but are not limited to Baidu, Weibo, Google, Yahoo, Bing, and 360. The real-time popularity data includes but is not limited to search popularity, discussion popularity, dissemination popularity, interaction rate, and search index. The search volume data includes but is not limited to PC search volume and mobile search volume.

[0071] S32 obtains the real-time attention degree according to the real-time heat data, and obtains the real-time attention trend value according to the search volume data.

[0072] The real-time attention degree of a certain key information in each search engine is calculated as (search popularity + discussion popularity + dissemination popularity) × interaction rate; or the real-time attention degree of a certain key information in each search engine is equal to the search index of a certain key information in the search engine, such as Baidu search index, Weibo search index, etc.

[0073] The real-time attention trend value of a key information in each search engine is equal to the average value of the change rate of the search volume SV in multiple consecutive preset time periods ΔT, that is, the real-time attention trend value of a key information in each search engine Where K is the number of preset time periods ΔT, SV i Represents the search volume within the i-th preset time period ΔT.

[0074] Real-time trend value of each assembly area Among them, RATV' j represents the real-time attention trend average of the key information of the assembly area obtained by the j-th search engine, SEWC j represents the weight coefficient of the j-th search engine, M represents the number of selected search engines, and the average value of the real-time attention trend of the key information of the assembly area obtained by the j-th search engine is N represents the number of key information in the assembly area, RATV i Indicates the real-time attention trend value of the i-th key information in the search engine.

[0075] Real-time attention to each of the assembly areas Among them, RAL' j represents the average real-time attention of key information of the assembly area obtained by the j-th search engine, SEWC j represents the weight coefficient of the j-th search engine, M represents the number of selected search engines, and the average real-time attention of the key information of the assembly area obtained by the j-th search engine is N represents the number of key information in the assembly area, RAL i Indicates the real-time attention of the i-th key information in the search engine.

[0076] Preferably, the search engine weight coefficient

[0077] Among them, m represents the total number of dimensions of the multi-dimensional second characteristic parameter, p' 2i represents the standard value of the second characteristic parameter of the i-th dimension, p 2i represents the second characteristic parameter value of the i-th dimension, λ i Represents the adjustment coefficient of the second characteristic parameter value of the i-th dimension, u i represents an intermediate variable, and e represents a natural constant.

[0078] p' 2i and λ i The multi-dimensional second feature parameters are set by technical personnel and include, but are not limited to, the number of daily active users of the search engine, the number of registered users, the total user rating, the number of PC and / or mobile downloads, and the overall satisfaction score. It is understood that the multi-dimensional second feature parameters are used to evaluate the overall ranking or popularity of the search engine.

[0079] Combine the multi-dimensional second characteristic parameters and according to the formula Obtaining the search engine weight coefficient is conducive to combining the multi-dimensional data of the related TV assembly line and the real-time attention and trend values ​​of consumers, and dynamically adjusting the monitoring strategies and assembly quality judgment standards of different areas.

[0080] S4, obtain the target TV assembly line, and for other TV assembly lines except the target TV assembly line, obtain multi-dimensional first feature parameters through the servers corresponding to other TV assembly lines, and obtain the monitoring index of each assembly area in the target TV assembly line according to the multi-dimensional first feature parameters, the real-time attention degree and the real-time attention trend value.

[0081] As a preferred technical solution, Figure 3 As shown, the specific method for obtaining the multi-dimensional first characteristic parameter through the server corresponding to other TV assembly lines includes the following steps:

[0082] S41, constructing a characteristic parameter sharing rule, wherein the characteristic parameter sharing rule includes the type of characteristic parameters involved in sharing and the update frequency.

[0083] S42: Acquire multi-dimensional first characteristic parameters from servers corresponding to other TV assembly lines according to the characteristic parameter sharing rule.

[0084] Generally speaking, it should be of reference significance to obtain multi-dimensional first characteristic parameters from servers corresponding to other TV assembly lines. The purpose of constructing the types of characteristic parameters involved in sharing and the update frequency is to limit the types and age of the first characteristic parameters of multiple dimensions obtained. The types of multi-dimensional first characteristic parameters include but are not limited to appearance defect parameters, size and shape defect parameters, electrical connection defect parameters and functional defect parameters. Specifically, characteristic parameter sharing rules can be constructed based on the set range of the types of characteristic parameters involved in sharing and the range of update frequencies, and the characteristic parameters that meet the set range of types and the range of update frequencies are shared as multi-dimensional first characteristic parameters.

[0085] Appearance defects in the assembly area may result in poor visual effects of the product, thereby affecting the product's market competitiveness, generally including scratches or nicks on the product surface, dust or dirt, accumulated bubbles or gas residues, color differences, labeling or identification errors, etc.; size and shape defects in the assembly area may cause the functional performance of the product to be impaired or malfunction, generally including product size that is too large or too small, bent or deformed, and mismatched mating parts; electrical connection defects in the assembly area may cause circuit instability or failure to transmit correct signals, generally including poor welding or cracked solder joints, incorrect connection lines, short circuits or open circuits, and poor contact of plugs or sockets, etc.; functional defects in the assembly area may cause the product to malfunction or fail to meet the expected performance requirements, generally including damage or failure of circuit components, frequency or voltage fluctuations, data transmission errors or losses, signal interference or noise, etc.

[0086] As a preferred technical solution, Figure 4 As shown, the specific method for obtaining the monitoring index of each assembly area in the target TV assembly line according to the multi-dimensional first characteristic parameter, the real-time attention degree and the real-time attention trend value includes the following steps:

[0087] S43, respectively obtaining the correlation between the target television assembly line and other television assembly lines.

[0088] The correlation degree can be set by technical personnel or management personnel based on experience. One method of obtaining the correlation degree is: through the corresponding server database, obtain the series, model and batch of televisions produced by each television assembly line (including the target television assembly line and other television assembly lines) within a preset assembly time period, and calculate the correlation degree through the number of series, models and batches of the same televisions produced by the target television assembly line and other television assembly lines and the corresponding correlation weight coefficients, wherein the series, model and batch of the televisions are respectively assigned corresponding correlation weight coefficients; specifically, the correlation degree between the target television assembly line and a certain other television assembly line Among them, m1 and m2 represent the number of televisions of the same series and the number of the same models under the same series produced by the target television assembly line and another television assembly line within the preset assembly time period, respectively. α1, α2, and α3 represent the correlation weight coefficients assigned to the television series, model, and batch, respectively. m1, Type i 、Batch i They respectively represent the number of televisions of the same series produced by the target television assembly line and another television assembly line within the preset assembly time period, the number of the same model in the i-th same series, and the total number of batches of the same model under the i-th same series.

[0089] For example, the TV series generated by a TV assembly line are divided into A1, A2, A3, ..., A10. During a preset assembly time period, the TV series produced by the target TV assembly line include A1, A2, A4, A6, and A7. The TV series produced by another TV assembly line include A1, A2, A3, A6, and A9. Then, the same series includes A1, A2, and A6, and m1 is 3. If, within the same series A2, the TV models produced by the target TV assembly line during the preset assembly time period include B1, B2, B4, B5, B7, B9, and B10, and the TV models produced by another TV assembly line include B1, B2, B4, B5, and B15, then the same models within the same series A2 include B1, B2, B4, and B5. The number of same models within the same series A2 is 4, and Type2 = 4. If the number of identical models in the same series A1 and A6 is 3 and 7 respectively, then the number of identical models m2 = 3 + 4 + 7 = 14.

[0090] If the target TV assembly line and another TV assembly line produce TVs in batches of 2 and 3 respectively under the same model, then Batch i =2+3=5.

[0091] Since for multiple OEM TV assembly lines, most of the parts areas of TVs of different models in the same series or different batches of the same model are often the same or slightly different, and the corresponding assembly areas have similar production conditions and quality standards, the correlation can be calculated by the number of series, models and batches of the same TVs produced by the target TV assembly line and other TV assembly lines, as well as the corresponding correlation weight coefficients. Based on this correlation, it is possible to better refer to the production data of other TV assembly lines to obtain more accurate monitoring indexes, and adjust the monitoring strategies and assembly quality judgment standards of the assembly areas corresponding to the monitoring indexes. Through this correlation acquisition method, the sharing and utilization efficiency of production data of each TV assembly line in the assembly line network can be improved, and it also provides a new way of thinking for calculating the correlation between TV assembly lines.

[0092] S44 obtains a multi-dimensional second characteristic parameter of each search engine, and obtains a search engine weight coefficient according to the multi-dimensional second characteristic parameter.

[0093] Among them, the search engine weight coefficient m represents the total number of dimensions of the multi-dimensional second characteristic parameter, p' 2i represents the standard value of the second characteristic parameter of the i-th dimension, p 2i represents the second characteristic parameter value of the i-th dimension, λ i represents the adjustment coefficient of the second characteristic parameter value of the i-th dimension, ui represents the intermediate variable, and e represents the natural constant.

[0094] S45 obtains a monitoring index for each assembly area in the target TV assembly line according to the correlation degree, the search engine weight coefficient, the multi-dimensional first characteristic parameter, the real-time attention degree, and the real-time attention trend value.

[0095] Preferably, the monitoring index

[0096] in, β1 and β2 represent the server comprehensive weight adjustment coefficient and the search engine comprehensive weight adjustment coefficient, respectively. l and k represent the number of other servers and the total number of dimensions of the first characteristic parameter, respectively. R' j , δ i are intermediate variables, RAL' and RATV' represent the real-time attention and the real-time attention trend value respectively, ε1 and ε2 represent the real-time attention weight adjustment coefficient and the real-time attention trend value adjustment coefficient respectively, R j, R' respectively represent the correlation between the i-th other server and the server corresponding to the target TV assembly line and the preset server correlation threshold, p' 1i represents the standard value of the first characteristic parameter of the i-th dimension, p 1i represents the first characteristic parameter value of the i-th dimension, α i represents the adjustment coefficient of the first characteristic parameter value of the i-th dimension, and e represents a natural constant.

[0097] The first characteristic parameter value may be the number, area, frequency, or severity of the first characteristic parameter. For example, multiple first characteristic parameter values ​​may include, but are not limited to, the number and area of ​​exterior scratches, the area of ​​paint peeling, the number of screen jitters, the severity of signal instability or noise interference, and the area or severity of component bending and deformation. More specifically, to facilitate calculation, technicians may quantify and assign values ​​to the first characteristic parameter values ​​in multiple dimensions.

[0098] The monitoring index comprehensively considers multiple factors, including the server comprehensive weight, search engine comprehensive weight, first characteristic parameters of multiple dimensions, real-time attention and real-time trend value of the assembly area, and the correlation between the target TV assembly line and other TV assembly lines. It is based on big data technology and combines data parameters from multiple different angles and dimensions. It can serve as an effective reference for adjusting the monitoring strategy of the assembly area and the assembly quality judgment standard. In addition, the monitoring index obtained based on this embodiment also provides a new idea for adjusting the monitoring strategy of the assembly area and the assembly quality judgment standard, which can improve the automation and intelligence level of the TV assembly line.

[0099] S5, adjusting the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index according to the monitoring index, and implementing the monitoring and assembly of the target TV assembly line according to the adjusted monitoring strategy and assembly quality judgment standard.

[0100] Adjusting the monitoring strategy of the assembly area corresponding to the monitoring index according to the monitoring index may be understood as adjusting the monitoring frequency and the amount of monitoring resources of the assembly area according to the monitoring index.

[0101] The monitoring resource amount includes but is not limited to the number of monitoring cameras and the computer memory used to analyze and process the images collected by the monitoring cameras. Preferably, the monitoring frequency and the monitoring resource amount are proportional to the monitoring index, that is, the monitoring frequency and the monitoring resource amount are adjusted accordingly according to the size of the monitoring index. Specifically, the monitoring frequency MF = MF' × e MonitorIndex , the monitoring resource amount MC=MC'×e MonitorIndex; Wherein, MF' and MC' represent the preset monitoring frequency standard value and the preset monitoring resource quantity standard value, respectively, and both can be set by technical personnel. In this way, the higher the monitoring index, to a certain extent, it indicates that the assembly area is more concerned by consumers / users, and the more likely the assembly area of ​​the TV is to have related defects. Through the monitoring index, the monitoring and control strategy of the TV assembly line can be adjusted dynamically and targeted in a timely manner to improve the production quality of TVs and reduce the defective product rate.

[0102] Adjusting the assembly quality judgment standard of the assembly area corresponding to the monitoring index according to the monitoring index can be understood as adjusting the assembly quality inspection judgment condition of the assembly area or adjusting the assembly quality inspection judgment threshold according to the monitoring index. For example, the threshold for good product judgment is adjusted according to the monitoring index. One example is that when judging the appearance defects of the assembly area based on visual image technology, the defect similarity threshold is adjusted according to the monitoring index, so that the higher the monitoring index, the lower the defect similarity threshold or the higher the good product judgment threshold. In general, the higher the monitoring index, the higher the corresponding assembly quality judgment standard, and the smaller the allowable or acceptable defect value.

[0103] Thus, a higher monitoring index indicates, to a certain extent, that the assembly area is of greater concern to consumers / users, and that the TV assembly area is more likely to have related defects. Using this monitoring index, the assembly quality judgment criteria of the TV assembly line can be adjusted dynamically and targeted, improving TV production quality and reducing defective product rates. In other words, by obtaining this monitoring index, the direction and intensity of quality inspection and control can be adjusted based on consumer attention to certain parts and functions of the TV, increasing its automation and intelligence, and its applicability is wide.

[0104] For the assembly process, robots such as automated soldering and screwing robots, and automated painting robots can be controlled to assemble components in various assembly areas of the target television assembly line. During this process, the monitoring index can be dynamically updated at a certain frequency, and the monitoring strategy and assembly quality judgment criteria for the assembly area corresponding to the monitoring index can be dynamically adjusted.

[0105] To sum up, the intelligent control system of the television assembly line divides the television assembly line into multiple different assembly areas by constructing an assembly line network including multiple servers, and assigns assembly area key information to each assembly area. Then, based on the assembly area key information, the real-time attention and real-time attention trend value of each assembly area are obtained, and the monitoring index of each assembly area in the target television assembly line is calculated by combining the real-time attention, real-time attention trend value and multi-dimensional first characteristic parameters of each assembly area and other television assembly lines. When obtaining the monitoring index, it takes different data of multiple dimensions into account, and can flexibly and dynamically adjust the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index based on the monitoring index, which can effectively improve the accuracy and flexibility of the decision-making control parameters of the television assembly line, and improve the degree of intelligence and automation of the television assembly line.

[0106] In addition, the intelligent control method for the television assembly line also provides a new idea for adjusting the assembly area monitoring strategy and the assembly quality judgment standard, which can improve the automation and intelligence level of the television assembly line.

[0107] The present invention also provides a television assembly line intelligent control system for implementing the television assembly line intelligent control method, such as Figure 5 As shown, it includes a network construction module, a key information allocation module, a real-time attention information acquisition module, a first characteristic parameter acquisition module, a monitoring index acquisition module and a monitoring and assembly module.

[0108] The network construction module is used to construct an assembly line network including multiple servers based on preset rules, wherein each server corresponds to a television assembly line; the key information allocation module is used to divide each television assembly line in the assembly line network into multiple different assembly areas, and allocate assembly area key information to each assembly area.

[0109] The real-time attention information acquisition module is used to obtain the real-time attention degree and real-time attention trend value of each assembly area based on the key information of the assembly area; the first characteristic parameter acquisition module is used to obtain the target TV assembly line, and for other TV assembly lines except the target TV assembly line, obtain multi-dimensional first characteristic parameters through the servers corresponding to other TV assembly lines.

[0110] The key assembly area information assigned to each assembly area includes multiple keywords to obtain more accurate real-time attention and real-time attention trend values, thereby timely and accurately adjusting the TV assembly control decision parameters and / or quality inspection quality judgment standards to optimize the control process parameters.

[0111] The keywords include but are not limited to back cover, scratches, screen distortion, screen flickering when starting up, screen flickering, indicator light off, unable to start up, loose screws, motherboard failure, poor sound effects, scratches on the case surface, cracks and paint peeling, etc.

[0112] The first characteristic parameter acquisition module includes a rule construction unit and a first characteristic parameter acquisition unit. The rule construction unit is used to construct characteristic parameter sharing rules, wherein the characteristic parameter sharing rules include the type of characteristic parameters to be shared and the update frequency. The first characteristic parameter acquisition unit is used to obtain multi-dimensional first characteristic parameters from servers corresponding to other TV assembly lines according to the characteristic parameter sharing rules.

[0113] Specifically, the multi-dimensional first characteristic parameters include appearance defect parameters, size and shape defect parameters, electrical connection defect parameters, and functional defect parameters.

[0114] The monitoring index acquisition module is used to obtain the monitoring index of each assembly area in the target TV assembly line based on the multi-dimensional first characteristic parameter, the real-time attention degree and the real-time attention trend value; the monitoring and assembly module is used to adjust the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index according to the monitoring index, and realize the monitoring and assembly of the target TV assembly line according to the adjusted monitoring strategy and assembly quality judgment standard.

[0115] As a preferred technical solution, the real-time attention information acquisition module includes a search engine selection unit and a real-time attention information acquisition unit.

[0116] The search engine selection unit is used to select multiple search engines, and obtain the real-time popularity data and search volume data of the key information of the assembly area through the multiple search engines; the real-time attention information acquisition unit is used to obtain the real-time attention according to the real-time popularity data, and obtain the real-time attention trend value according to the search volume data.

[0117] The real-time attention degree of a certain key information in each search engine is calculated as (search popularity + discussion popularity + dissemination popularity) × interaction rate; or the real-time attention degree of a certain key information in each search engine is equal to the search index of a certain key information in the search engine, such as Baidu search index, Weibo search index, etc.

[0118] The real-time attention trend value of a key information in each search engine is equal to the average value of the change rate of the search volume SV in multiple consecutive preset time periods ΔT, that is, the real-time attention trend value of a key information in each search engine Where K is the number of preset time periods ΔT, SV iRepresents the search volume within the i-th preset time period ΔT.

[0119] Real-time trend value of each assembly area Among them, RATV' j represents the real-time attention trend average of the key information of the assembly area obtained by the j-th search engine, SEWC j represents the weight coefficient of the j-th search engine, M represents the number of selected search engines, and the average value of the real-time attention trend of the key information of the assembly area obtained by the j-th search engine is N represents the number of key information in the assembly area, RATV i Indicates the real-time attention trend value of the i-th key information in the search engine.

[0120] Real-time attention to each of the assembly areas Among them, RAL' j represents the average real-time attention of key information of the assembly area obtained by the j-th search engine, SEWC j represents the weight coefficient of the j-th search engine, M represents the number of selected search engines, and the average real-time attention of the key information of the assembly area obtained by the j-th search engine is N represents the number of key information in the assembly area, RAL i Indicates the real-time attention of the i-th key information in the search engine.

[0121] Preferably, the search engine weight coefficient

[0122] Among them, m represents the total number of dimensions of the multi-dimensional second characteristic parameter, p' 2i represents the standard value of the second characteristic parameter of the i-th dimension, p 2i represents the second characteristic parameter value of the i-th dimension, λ i Represents the adjustment coefficient of the second characteristic parameter value of the i-th dimension, u i represents an intermediate variable, and e represents a natural constant.

[0123] p' 2i and λ i The multi-dimensional second feature parameters are set by technical personnel and include, but are not limited to, the number of daily active users of the search engine, the number of registered users, the total user rating, the number of PC and / or mobile downloads, and the overall satisfaction score. It is understood that the multi-dimensional second feature parameters are used to evaluate the overall ranking or popularity of the search engine.

[0124] Combine the multi-dimensional second characteristic parameters and according to the formula Obtaining the search engine weight coefficient is conducive to combining the multi-dimensional data of the related TV assembly line and the real-time attention and trend values ​​of consumers, and dynamically adjusting the monitoring strategies and assembly quality judgment standards of different areas.

[0125] The monitoring index acquisition module includes a relevance acquisition unit, a search engine weight coefficient acquisition unit and a monitoring index acquisition unit.

[0126] The correlation acquisition unit is used to respectively obtain the correlation between the target TV assembly line and other TV assembly lines; the search engine weight coefficient acquisition unit is used to obtain the multi-dimensional second characteristic parameters of each search engine, and obtain the search engine weight coefficient based on the multi-dimensional second characteristic parameters; the monitoring index acquisition unit is used to obtain the monitoring index of each assembly area in the target TV assembly line based on the correlation, the search engine weight coefficient, the multi-dimensional first characteristic parameter, the real-time attention degree and the real-time attention trend value.

[0127] The correlation degree can be set by technical personnel or management personnel based on experience. One method of obtaining the correlation degree is: through the corresponding server database, obtain the series, model and batch of televisions produced by each television assembly line (including the target television assembly line and other television assembly lines) within a preset assembly time period, and calculate the correlation degree through the number of series, models and batches of the same televisions produced by the target television assembly line and other television assembly lines and the corresponding correlation weight coefficients, wherein the series, model and batch of the televisions are respectively assigned corresponding correlation weight coefficients; specifically, the correlation degree between the target television assembly line and a certain other television assembly line Among them, m1 and m2 represent the number of televisions of the same series and the number of the same models under the same series produced by the target television assembly line and another television assembly line within the preset assembly time period, respectively. α1, α2, and α3 represent the correlation weight coefficients assigned to the television series, model, and batch, respectively. m1, Type i 、Batch i They respectively represent the number of televisions of the same series produced by the target television assembly line and another television assembly line within the preset assembly time period, the number of the same model in the i-th same series, and the total number of batches of the same model under the i-th same series.

[0128] Preferably, the monitoring index

[0129]

[0130] in, β1 and β2 represent the server comprehensive weight adjustment coefficient and the search engine comprehensive weight adjustment coefficient, respectively. l and k represent the number of other servers and the total number of dimensions of the first characteristic parameter, respectively. R' j , δ i are intermediate variables, RAL' and RATV' represent the real-time attention and the real-time attention trend value respectively, ε1 and ε2 represent the real-time attention weight adjustment coefficient and the real-time attention trend value adjustment coefficient respectively, R j , R' respectively represent the correlation between the i-th other server and the server corresponding to the target TV assembly line and the preset server correlation threshold, p' 1i represents the standard value of the first characteristic parameter of the i-th dimension, p 1i represents the first characteristic parameter value of the i-th dimension, α i represents the adjustment coefficient of the first characteristic parameter value of the i-th dimension, and e represents a natural constant.

[0131] The monitoring index comprehensively considers multiple factors, including the server comprehensive weight, search engine comprehensive weight, first characteristic parameters of multiple dimensions, real-time attention and real-time trend value of the assembly area, and the correlation between the target TV assembly line and other TV assembly lines. It is based on big data technology and combines data parameters from multiple different angles and dimensions. It can serve as an effective reference for adjusting the monitoring strategy of the assembly area and the assembly quality judgment standard. In addition, the monitoring index obtained based on this embodiment also provides a new idea for adjusting the monitoring strategy of the assembly area and the assembly quality judgment standard, which can improve the automation and intelligence level of the TV assembly line.

[0132] It should be noted here that, for the convenience of calculation, the variables involved in the formulas in the embodiments of the present invention are uniformly dimensionless.

[0133] To sum up, the intelligent control system of the television assembly line divides the television assembly line into multiple different assembly areas by constructing an assembly line network including multiple servers, and assigns assembly area key information to each assembly area. Then, based on the assembly area key information, the real-time attention and real-time attention trend value of each assembly area are obtained, and the monitoring index of each assembly area in the target television assembly line is calculated by combining the real-time attention, real-time attention trend value and multi-dimensional first characteristic parameters of each assembly area and other television assembly lines. When obtaining the monitoring index, it takes different data of multiple dimensions into account, and can flexibly and dynamically adjust the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index based on the monitoring index, which can effectively improve the accuracy and flexibility of the decision-making control parameters of the television assembly line, and improve the degree of intelligence and automation of the television assembly line.

[0134] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for intelligent control of a television assembly line, characterized in that: The intelligent control method for a television assembly line comprises the following steps: Based on preset rules, constructing an assembly line network including a plurality of servers, wherein each of the servers corresponds to a television assembly line; For each television assembly line in the assembly line network, the television assembly line is divided into a plurality of different assembly areas, and assembly area key information is allocated to each of the assembly areas; According to the key information of the assembly area, obtaining the real-time attention degree and real-time attention trend value of each assembly area; Obtain a target television assembly line, and for other television assembly lines except the target television assembly line, obtain multi-dimensional first feature parameters through servers corresponding to the other television assembly lines, and obtain a monitoring index for each assembly area in the target television assembly line based on the multi-dimensional first feature parameters, the real-time attention degree, and the real-time attention trend value; According to the monitoring index, adjusting the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index, and implementing monitoring and assembly of the target television assembly line according to the adjusted monitoring strategy and assembly quality judgment standard; The specific method for obtaining the multi-dimensional first characteristic parameter includes the following steps: Constructing a feature parameter sharing rule, wherein the feature parameter sharing rule includes the type of feature parameters involved in sharing and the update frequency; According to the characteristic parameter sharing rule, obtaining the multi-dimensional first characteristic parameter from the server corresponding to the other television assembly line; The specific method for obtaining the monitoring index of each assembly area in the target television assembly line includes the following steps: respectively obtaining the correlation between the target TV assembly line and other TV assembly lines; Obtaining a multi-dimensional second characteristic parameter of each search engine, and obtaining a search engine weight coefficient based on the multi-dimensional second characteristic parameter; A monitoring index of each assembly area in the target television assembly line is obtained according to the correlation degree, the search engine weight coefficient, the multi-dimensional first characteristic parameter, the real-time attention degree and the real-time attention trend value.

2. The intelligent control method for a television assembly line according to claim 1, characterized in that: The specific method for obtaining the real-time attention degree and real-time attention trend value of each assembly area according to the key information of the assembly area includes the following steps: Selecting multiple search engines, and obtaining real-time popularity data and search volume data of key information of the assembly area through the multiple search engines; The real-time attention degree is obtained according to the real-time heat data, and the real-time attention trend value is obtained according to the search volume data.

3. A television assembly line intelligent control method as claimed in claim 2, characterized in that: Search engine weight coefficient ; in, Indicates the total number of dimensions of the multi-dimensional second characteristic parameter, Indicates the The standard value of the second characteristic parameter of the dimension, Indicates the The second characteristic parameter value of the dimension, Indicates the The adjustment coefficient of the second characteristic parameter value of the dimension, represents the intermediate variable, Represents a natural constant.

4. An intelligent control system for a television assembly line, used to implement the intelligent control method for a television assembly line according to any one of claims 1 to 3, characterized in that: The intelligent control system of the television assembly line includes: A network construction module, configured to construct an assembly line network comprising a plurality of servers based on preset rules, wherein each server corresponds to a television assembly line; a key information allocation module, configured to divide each television assembly line in the assembly line network into a plurality of different assembly areas, and allocate assembly area key information to each of the assembly areas; A real-time attention information acquisition module is used to obtain the real-time attention degree and real-time attention trend value of each assembly area according to the key information of the assembly area; A first characteristic parameter acquisition module is configured to acquire a target television assembly line and, for other television assembly lines except the target television assembly line, acquire multi-dimensional first characteristic parameters through servers corresponding to the other television assembly lines; a monitoring index acquisition module, configured to acquire a monitoring index for each assembly area in the target television assembly line according to the multi-dimensional first characteristic parameter, the real-time attention degree, and the real-time attention trend value; a monitoring and assembly module, configured to adjust, based on the monitoring index, a monitoring strategy and an assembly quality judgment standard for the assembly area corresponding to the monitoring index, and implement monitoring and assembly of the target television assembly line based on the adjusted monitoring strategy and the assembly quality judgment standard; The first characteristic parameter acquisition module includes: A rule building unit, configured to build a feature parameter sharing rule, wherein the feature parameter sharing rule includes the type of feature parameters involved in sharing and the update frequency; A first characteristic parameter acquisition unit, configured to acquire multi-dimensional first characteristic parameters from servers corresponding to other television assembly lines according to the characteristic parameter sharing rule; The monitoring index acquisition module includes: a correlation degree obtaining unit, configured to respectively obtain the correlation degrees between the target television assembly line and other television assembly lines; A search engine weight coefficient acquisition unit, configured to acquire a multi-dimensional second characteristic parameter of each search engine, and acquire a search engine weight coefficient according to the multi-dimensional second characteristic parameter; A monitoring index acquisition unit is used to obtain the monitoring index of each assembly area in the target TV assembly line according to the correlation, the search engine weight coefficient, the multi-dimensional first feature parameter, the real-time attention degree and the real-time attention trend value.

5. The intelligent control system for a television assembly line according to claim 4, characterized in that: The real-time attention information acquisition module includes: A search engine selection unit, configured to select a plurality of search engines and obtain real-time popularity data and search volume data of the key information of the assembly area through the plurality of search engines; A real-time attention information acquisition unit is used to obtain the real-time attention degree according to the real-time heat data, and to obtain the real-time attention trend value according to the search volume data.

6. The intelligent control system for a television assembly line according to claim 5, characterized in that: The multi-dimensional first characteristic parameters include appearance defect parameters, size and shape defect parameters, electrical connection defect parameters, and functional defect parameters.

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