Television assembly line intelligent control method and system

Abstract

The invention relates to the technical field of television assembly, and provides a television assembly line intelligent control method and system, and the method comprises the steps: constructing an assembly line network; assembling area key information is distributed to each assembling area; the real-time attention degree and the real-time attention trend value of each assembly area are obtained; obtaining a target television assembly line, obtaining a multi-dimensional first characteristic parameter, and obtaining a monitoring index of each assembly area according to the multi-dimensional first characteristic parameter, the real-time attention and the real-time attention trend value; and according to the monitoring index, adjusting a monitoring strategy and an assembly quality judgment standard of the assembly area corresponding to the monitoring index, and according to the adjusted monitoring strategy and the assembly quality judgment standard, realizing monitoring and assembly of the target television assembly line. According to the invention, the accuracy and flexibility of decision control parameters of the television assembly line can be effectively improved, and the intelligent and automatic degree of the television assembly line is improved.

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 according to product batch categories. The single-product assembly line is mainly used for assembling television parts of a single specification model; the multi-product batch assembly line assembles multiple televisions by batch; the multi-product assembly line assembles multiple specifications of televisions simultaneously on the assembly line. These assembly lines generally include processes such as conveying, assembling, and detecting. For the assembling process, most are equipped with devices such as automatic soldering machines and automatic screw machines to improve their automation degree and production efficiency. Moreover, the assembly line is often equipped with a corresponding scanning program, which can distinguish batches or specification televisions and allocate production, thus greatly reducing the situation of incorrect procedures.

[0003] Chinese invention patents such as "An Automatic Assembly System for Television and Its Automatic Screw Assembly Equipment" with the application number "CN201510207133.0", "An Optimization Scheduling Method for the Production Assembly Process of a Liquid Crystal Television" with the application number "CN201410665858.X", and "A Manipulator Control Method for a Television Assembly Line" with the application number "CN201910940893.0" all relate to television assembly lines, and they can all improve the television assembly production efficiency to a certain extent. However, the above several television assembly lines do not calculate the monitoring indexes of different regions by combining multi-dimensional data, and cannot dynamically adjust the monitoring strategies and assembly quality judgment criteria of different regions based on the monitoring indexes, lacking flexibility, and there is still room for further improvement in the degree of 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 calculate the monitoring indexes of different regions by combining multi-dimensional data and cannot dynamically adjust the monitoring strategies and assembly quality judgment criteria of different regions based on the monitoring indexes, the present invention provides an intelligent control method for a television assembly line. By constructing an assembly line network including servers of multiple television assembly lines, and combining the real-time attention degree, real-time attention trend value of each assembly area and multi-dimensional first characteristic parameters of other television assembly lines to calculate the monitoring index of each assembly area in the target television assembly line, it can flexibly and dynamically adjust the monitoring strategy and assembly quality judgment criteria of the assembly area corresponding to the monitoring index based on the monitoring index, improving the degree of intelligence and automation of the television assembly line. The specific technical solutions are as follows:

[0005] An intelligent control method for a TV assembly line, which comprises the following steps:

[0006] Based on preset rules, construct an assembly line network including multiple servers, where each of the servers corresponds to a TV assembly line;

[0007] For each TV assembly line in the assembly line network, divide the TV assembly line into multiple different assembly areas, and allocate key information of the assembly area to each of the assembly areas;

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

[0009] Obtain a target TV assembly line. For the other TV assembly lines except the target TV assembly line, obtain multi-dimensional first characteristic parameters through the servers corresponding to the 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 characteristic parameters, the real-time attention degree and the real-time attention trend value;

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

[0011] The intelligent control system of the TV assembly line constructs an assembly line network including multiple servers, divides the TV assembly line into multiple different assembly areas, and allocates key information of the assembly area to each of the assembly areas. Then, according to the key information of the assembly area, obtain the real-time attention degree and the real-time attention trend value of each of the assembly areas. By combining the real-time attention degree, the real-time attention trend value of each assembly area and the multi-dimensional first characteristic parameters of other TV assembly lines, calculate the monitoring index of each assembly area in the target TV assembly line. When obtaining the monitoring index, it takes into account different data in multiple dimensions, and can flexibly and dynamically adjust the monitoring strategy and the assembly quality judgment standard corresponding to the monitoring index based on the monitoring index, which can effectively improve the accuracy and flexibility of the decision control parameters of the TV assembly line and improve the intelligent and automated level of the TV assembly line.

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

[0013] Select multiple search engines, and obtain the real-time heat data and search volume data of the key information of the assembly area through the multiple search engines;

[0014] Obtain the real-time attention degree according to the real-time popularity data, and obtain the real-time attention trend value according to the search volume data.

[0015] Preferably, the specific method for obtaining the multi-dimensional first feature parameters through the server corresponding to other TV assembly lines includes the following steps:

[0016] Construct a feature parameter sharing rule, where the feature parameter sharing rule includes the types of feature parameters participating in the sharing and the update frequency;

[0017] According to the feature parameter sharing rule, obtain the multi-dimensional first feature parameters from the server corresponding to other TV assembly lines.

[0018] Preferably, the specific method for obtaining 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 includes the following steps:

[0019] Respectively obtain the correlation degrees between the target TV assembly line and other TV assembly lines;

[0020] Obtain the multi-dimensional second feature parameters of each search engine, and obtain the search engine weight coefficient according to the multi-dimensional second feature parameters;

[0021] Obtain the monitoring index of each assembly area in the target TV assembly line according to the correlation degree, the search engine weight coefficient, the multi-dimensional first feature parameters, 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 feature parameters, p' 2i represents the standard value of the second feature parameter of the i-th dimension, p 2i represents the second feature parameter value of the i-th dimension, λ i represents the adjustment coefficient of the second feature parameter value of the i-th dimension, u i represents an intermediate variable, and e represents the natural constant.

[0024] A TV assembly line intelligent control system for implementing the TV assembly line intelligent control method as described above, which includes:

[0025] A network construction module, configured to construct an assembly line network including multiple servers based on a preset rule, where each server corresponds to a TV assembly line;

[0026] A key information distribution module, which is used to divide each TV assembly line in the assembly line network into multiple different assembly areas and allocate key information of the assembly area to each of the assembly areas;

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

[0028] A first feature parameter acquisition module, which is used to acquire a target TV assembly line. For other TV assembly lines except the target TV assembly line, multi-dimensional first feature parameters are acquired through the servers corresponding to the other TV assembly lines;

[0029] A monitoring index acquisition module, which is used to acquire 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;

[0030] A monitoring and assembly module, which is used to adjust the monitoring strategy and the 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 the assembly quality judgment standard.

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

[0032] A search engine selection unit, which is used to select multiple search engines and acquire the real-time heat data and the search volume data of the key information of the assembly area through the multiple search engines;

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

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

[0035] A rule construction unit, which is used to construct a feature parameter sharing rule, and the feature parameter sharing rule includes the type and the update frequency of the feature parameters participating in the sharing;

[0036] A first feature parameter acquisition unit, which is used to acquire multi-dimensional first feature parameters from the servers corresponding to the other TV assembly lines according to the feature parameter sharing rule.

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

[0038] A correlation degree acquisition unit, which is used to acquire the correlation degree between the target TV assembly line and the other TV assembly lines respectively;

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

[0040] A monitoring index acquisition unit, configured to acquire a monitoring index of each assembly area in the target TV assembly line according to the relevance, the search engine weight coefficient, the multi-dimensional first feature parameters, the real-time attention degree, and the real-time attention trend value.

[0041] Preferably, the multi-dimensional first feature parameters include appearance defect parameters, dimensional shape defect parameters, electrical connection defect parameters, and functional defect parameters. Description of the Drawings

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

[0043] Figure 1 is an overall flowchart of an intelligent control method for a TV assembly line according to an embodiment of the present invention;

[0044] Figure 2 is a flowchart of a specific method for acquiring the real-time attention degree and the real-time attention trend value of each assembly area according to an embodiment of the present invention;

[0045] Figure 3 is a flowchart of a specific method for acquiring multi-dimensional first feature parameters according to an embodiment of the present invention;

[0046] Figure 4 is a flowchart of a specific method for acquiring the monitoring index of each assembly area in the target TV assembly line according to an embodiment of the present invention;

[0047] Figure 5 is an overall structural diagram of an intelligent control of a TV assembly line according to an embodiment of the present invention. Detailed Embodiments

[0048] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be 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 protection scope of the present invention.

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

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

[0051] In this invention, the so-called "first" and "second" do not represent specific quantities and orders, but are only used for name distinction.

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

[0053] For TV assembly lines, such as some TV assembly lines for OEM processing, there are often more than one. For multiple TV assembly lines for OEM processing, for TVs of the same series but different models or the same model but different batches, most of their component areas are often the same or have slight differences, and they have similar production conditions and quality standards. Based on this, after selecting the target TV assembly line, some production data of other related TV assembly lines are of reference significance and can be used to adjust the control decisions (such as monitoring strategies or judgment of assembly quality, etc.) of the target TV assembly line.

[0054] In addition, due to differences in monitoring resources such as vision inspection cameras, monitors, and computers for different TV assembly lines, their monitoring strategies and monitoring degrees for different assembly areas are generally adjusted according to the actual situation of the assembly line. For the quality control and judgment of assembly quality in different areas and even the entire production line of the TV assembly line, it can also be appropriately adjusted according to the actual situation, such as adjusting the direction and intensity of quality inspection and quality control according to consumers' attention to the functions of certain parts of the TV.

[0055] In the existing TV assembly lines, after a large amount of retrieval, there is temporarily no found TV assembly line control method that combines multi-dimensional different data of related TV assembly lines and consumers' attention and dynamically adjusts the monitoring strategies and assembly quality judgment criteria for different areas.

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

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

[0058] The preset rules are set by technicians or managers, and they can construct an assembly line network including multiple servers according to the degree of association between TV assembly lines. For example, an assembly line network can be constructed according to the TV series, models, and batches assembled by the TV assembly lines. The servers of the TV assembly lines that produce the same model in different batches or different models in different batches of the same series within a preset time period, such as one year or half a year, are classified into the assembly line network.

[0059] The preset rules can also be generated according to the similarity between the plate areas of the TVs assembled by the TV assembly lines. For example, a general TV includes plate areas such as a power board, a main board, a screen, a cabinet, a speaker, a screen cable, a backlight module, and an adapter board. For TVs of the same series with the same model or different models, the structures of some plate areas are mostly the same or similar, such as speakers and adapter boards. Preset rules are generated according to the number of the same or similar structures of the plate areas of the TVs assembled by the TV assembly lines, that is, taking the number of the same or similar structures of the plate areas of the TVs reaching a preset value as the preset rule, and the servers of the corresponding TV assembly lines are classified into a set, and an assembly line network including multiple servers is constructed according to this set.

[0060] During assembly, the production data of the plate areas of different TV assembly lines can be shared for reference. A simple example is to adjust the TV assembly control decision parameters and / or the quality judgment criteria of quality inspection in a timely manner according to the specific pass rate or defects existing in a corresponding plate area in multiple production lines. For example, when the pass rate decreases and there are more defects, the quality inspection judgment criteria are improved, and at the same time, the control process parameters are optimized to improve the production efficiency and quality of the TV assembly line.

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

[0062] S2. For each TV assembly line in the assembly line network, divide the TV assembly line into multiple different assembly areas, and assign key assembly area information to each of the assembly areas.

[0063] Specifically, the TV assembly line can be divided into multiple different assembly areas according to the functional blocks of the TV. Each assembly area serves as a process station for assembling the corresponding functional block structure. For example, the TV assembly line can be divided into assembly areas including a power board, a main board, a screen, a chassis, speakers, a screen cable, a backlight module, and an 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 chassis includes structures such as a front frame, a middle frame, and a rear 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 chassis front frame, middle frame, and rear cover.

[0065] By dividing into multiple different assembly areas, it is beneficial to assign key assembly area information to each assembly area to obtain more accurate real-time attention and real-time attention trend values for the assembly area. Generally speaking, the more detailed the assembly area division, the more accurate the real-time attention and real-time attention trend values obtained for the assembly area, and the more able to adjust the TV assembly control decision parameters and / or the quality judgment criteria for quality inspection in a timely manner according to the production data shared by the assembly line network server, optimize the control process parameters, and improve the production efficiency and quality of the TV assembly line.

[0066] The key assembly area information assigned to each of the assembly areas includes at least one keyword, which can be used to describe the structural characteristics or functional characteristics of the assembly area. Preferably, the key assembly area information assigned to each of the assembly areas includes multiple keywords to obtain more accurate real-time attention and real-time attention trend values, and then adjust the TV assembly control decision parameters and / or the quality judgment criteria for quality inspection in a timely and accurate manner, and optimize the control process parameters.

[0067] S3. According to the key assembly area information, obtain the real-time attention and real-time attention trend values for each of the assembly areas.

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

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

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

[0071] S32. Obtain the real-time attention degree according to the real-time popularity data, and obtain 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 = (search popularity + discussion popularity + spread 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 this search engine, such as Baidu search index, Weibo search index, etc.

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

[0074] The real-time attention trend value of each assembly area where RATV' j represents the average value of the real-time attention trend of the key information in the assembly area obtained by the j-th search engine, and 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 in the assembly area obtained by the j-th search engine N represents the number of the key information in the assembly area, and RATV i represents the real-time attention trend value of the i-th key information in the search engine.

[0075] The real-time attention degree of each assembly area where RAL' j represents the average value of the real-time attention degree of the key information in the assembly area obtained by the j-th search engine, and 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 degree of the key information in the assembly area obtained by the j-th search engine N represents the number of key information of the assembly area, RAL i represents 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 value of the second characteristic parameter of the i-th dimension, λ i represents the adjustment coefficient of the value of the second characteristic parameter of the i-th dimension, u i represents an intermediate variable, and e represents the natural constant.

[0078] p' 2i and λ i are all set by technicians. The multi-dimensional second characteristic parameter includes but is not limited to the daily active user number, registered user number, total user score, PC and / or mobile download volume, and overall satisfaction score of the search engine. It can be understood that the multi-dimensional second characteristic parameter is used to evaluate the overall ranking or popularity of the search engine.

[0079] Combined with the multi-dimensional second characteristic parameter and according to the formula Obtaining the search engine weight coefficient is beneficial to dynamically adjust the monitoring strategies and assembly quality judgment criteria of different regions by combining multi-dimensional different data of the associated TV set assembly line, as well as the real-time attention and real-time attention trend value of consumers.

[0080] S4. Obtain the target TV set assembly line. For other TV set assembly lines except the target TV set assembly line, obtain the multi-dimensional first characteristic parameter through the server corresponding to the other TV set assembly line, and obtain the monitoring index of each assembly area in the target TV set assembly line according to the multi-dimensional first characteristic parameter, the real-time attention, and the real-time attention trend value.

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

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

[0083] S42. According to the characteristic parameter sharing rule, obtain the multi-dimensional first characteristic parameter from the server corresponding to the other TV set assembly line.

[0084] Generally speaking, it should be meaningful to obtain multi-dimensional first feature parameters from the servers corresponding to other TV assembly lines. The types of feature parameters participating in sharing and the construction of the update frequency are used to limit the types and degrees of newness and oldness of the multi-dimensional first feature parameters obtained. The types of multi-dimensional first feature parameters include, but are not limited to, appearance defect parameters, dimension and shape defect parameters, electrical connection defect parameters, and functional defect parameters. Specifically, the feature parameter sharing rule can be constructed according to the set range of the types of feature parameters participating in sharing and the range of the update frequency, and the feature parameters that meet the set range of types and the range of the update frequency are shared as multi-dimensional first feature parameters.

[0085] Appearance defects in the assembly area may lead to poor visual effects of the product, thus affecting the market competitiveness of the product. Generally, they include scratches or marks on the product surface, dust or dirt, accumulated bubbles or gas residues, color differences, label or identification errors, etc.; dimension and shape defects in the assembly area may cause damage to the functional performance of the product or inability to work properly. Generally, they include the product being too large or too small in size, bent or deformed, and mismatched mating parts; electrical connection defects in the assembly area may cause unstable circuits or inability to transmit correct signals. Generally, they include poor soldering 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 fail to work properly or fail to meet the expected performance requirements. Generally, they include 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, as Figure 4 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 feature parameter, the real-time attention degree, and the real-time attention trend value includes the following steps:

[0087] S43, respectively obtain the correlation degrees between the target TV assembly line and other TV assembly lines.

[0088] Regarding the correlation degree, it can be set by technicians or managers according to experience. One method for obtaining the correlation degree is as follows: Through the corresponding server database, obtain the series, model, and batch of the televisions produced by each television assembly line (including the target television assembly line and other television assembly lines) within a preset assembly time period. Calculate the correlation degree through the quantity of the same series, model, and batch of the televisions produced by the target television assembly line and other television assembly lines and the corresponding correlation degree weight coefficients. Among them, the series, model, and batch of the televisions are respectively assigned corresponding correlation degree weight coefficients. Specifically, the correlation degree between the target television assembly line and a certain other television assembly line where m1 and m2 respectively represent the quantity of the same series of the televisions produced by the target television assembly line and a certain other television assembly line within the preset assembly time period and the quantity of the same model under the same series, and α1, α2, and α3 respectively represent the correlation degree weight coefficients assigned to the television series, model, and batch, m1, Type i 、Batch i respectively represent the quantity of the same series of the televisions produced by the target television assembly line and a certain other television assembly line within the preset assembly time period, the quantity of the same model of the i-th same series, and the total quantity of the batches of the same model under the i-th same series.

[0089] For example, the television series produced by the television assembly line are divided into A1, A2, A3... A10. The television series produced by the target television assembly line within the preset assembly time period include A1, A2, A4, A6, and A7. The television series produced by a certain other television assembly line include A1, A2, A3, A6, and A9. Then the same series includes A1, A2, and A6, and m1 is 3. If in the same series A2, the television models produced by the target television assembly line within the preset assembly time period include B1, B2, B4, B5, B7, B9, and B10, and the television models produced by a certain other television assembly line include B1, B2, B4, B5, and B15, then the same models of the same series A2 include B1, B2, B4, B5, and the quantity of the same models of the same series A2 is 4, Type 2 = 4. If the quantities of the same models under the same series A1 and A6 are 3 and 7 respectively, then the quantity of the same models m2 = 3 + 4 + 7 = 14.

[0090] If the batches of the televisions produced by the target television assembly line and a certain other television assembly line under the 5th same model are 2 and 3 respectively, then Batch i = 2 + 3 = 5.

[0091] For multiple OEM TV assembly lines, for different models in the same series and different batches of the same model of TV sets, most of their component areas are often the same or have minor differences, and the corresponding assembly areas have similar production conditions and quality standards. Therefore, the correlation degree can be calculated based on the number of the same series, model, and batch of TV sets produced by the target TV assembly line and other TV assembly lines, as well as the corresponding correlation weight coefficients. Based on this correlation degree, the production data of other TV assembly lines can be better referred to obtain more accurate monitoring indexes, and the monitoring strategies and assembly quality judgment criteria of the assembly areas corresponding to the monitoring indexes can be adjusted. Through this correlation degree acquisition method, the sharing and utilization efficiency of the production data of each TV assembly line in the assembly line network can be improved, and at the same time, a new idea is provided for calculating the correlation degree between TV assembly lines.

[0092] S44 Obtain the multi-dimensional second characteristic parameters of each of the search engines, and obtain the search engine weight coefficients according to the multi-dimensional second characteristic parameters.

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

[0094] S45 Obtain the monitoring indexes of each assembly area in the target TV assembly line according to the correlation degree, the search engine weight coefficients, the multi-dimensional first characteristic parameters, the real-time attention degree, and the real-time attention trend value.

[0095] Preferably, the monitoring index

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

[0097] For the value of the first characteristic parameter, it can be the quantity, area, frequency, or severity, etc. of the first characteristic parameter. For example, the values of the first characteristic parameter in multiple dimensions include but are not limited to the quantity and area of appearance scratches, the area of paint peeling, the number of screen jumps, the severity of unstable signal or noise interference, the area or severity of component bending and deformation, etc. More specifically, for the convenience of calculation, technicians can quantify and assign values to the values of the first characteristic parameter in multiple dimensions.

[0098] The monitoring index comprehensively considers multiple factors such as the comprehensive weight of the server, the comprehensive weight of the search engine, the first characteristic parameters in multiple dimensions, the real-time attention degree and real-time trend value of the assembly area, and the correlation degree between the target TV assembly line and other TV assembly lines. Based on big data technology, it combines data parameters from multiple different angles and dimensions, and can be used as an effective reference basis 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, and can improve the automation and intelligence level of the TV assembly line.

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

[0100] Regarding adjusting the monitoring strategy of the assembly area corresponding to the monitoring index according to the monitoring index, it can be understood as adjusting the monitoring frequency and the amount of monitoring resources of the assembly area according to the monitoring index.

[0101] The amount of monitoring resources includes but is not limited to the number of monitoring cameras and the computer memory for image analysis and processing of the images collected by the monitoring cameras. Preferably, the monitoring frequency and the amount of monitoring resources are proportional to the monitoring index, that is, according to the size of the monitoring index, the level of the monitoring frequency and the size of the amount of monitoring resources are adjusted accordingly. Specifically, the monitoring frequency MF = MF'×e MonitorIndex , and the amount of monitoring resources MC = MC'×e MonitorIndex; where, MF' and MC' respectively represent the preset monitoring frequency standard value and the preset monitoring resource quantity standard value, both of which can be set by technicians. 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 there are more likely to be relevant defects and flaws in this assembly area of the TV set. Through the monitoring index, the monitoring and control strategy of the TV assembly line can be adjusted timely, dynamically and specifically, improving the production quality of the TV set and reducing the defective rate.

[0102] Regarding adjusting the assembly quality judgment standard of the assembly area corresponding to the monitoring index, it can be understood as adjusting the assembly quality inspection judgment conditions or the assembly quality inspection judgment threshold of the assembly area according to the monitoring index. For example, adjusting the threshold for judging good products. One example is when judging the appearance defects of the assembly area based on vision image technology, adjusting the similarity threshold of defects according to the monitoring index, so that the higher the monitoring index, the lower the similarity threshold of defects or the higher the threshold for judging good products. Generally speaking, the higher the monitoring index, the higher the corresponding assembly quality judgment standard, and the smaller the allowable or acceptable defect value.

[0103] 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 there are more likely to be relevant defects and flaws in this assembly area of the TV set. Through the monitoring index, the assembly quality judgment standard of the TV assembly line can be adjusted timely, dynamically and specifically, improving the production quality of the TV set and reducing the defective rate. That is to say, by obtaining the monitoring index, the quality inspection and quality control direction and intensity can be adjusted according to the attention degree of consumers to the functions of certain parts of the TV set, improving its automation and intelligence level, and it has a wide range of applications.

[0104] For the assembly, robots such as automatic soldering robots, automatic screw robots, and automatic painting robots can be controlled to assemble parts in each assembly area of the target TV assembly line. During this process, the monitoring index can be dynamically updated at a certain frequency, and the monitoring strategy and assembly quality judgment standard of the assembly area corresponding to the monitoring index can be dynamically adjusted.

[0105] In summary, the intelligent control system for the TV assembly line divides the TV assembly line into multiple different assembly areas by constructing an assembly line network including multiple servers, and assigns key information for each assembly area. Then, based on the key information of the assembly area, the real-time attention degree and real-time attention trend value of each assembly area are obtained. By combining the real-time attention degree, real-time attention trend value of each assembly area and multi-dimensional first characteristic parameters of other TV assembly lines, the monitoring index of each assembly area in the target TV assembly line is calculated. When obtaining the monitoring index, different data in multiple dimensions are taken into account, and the monitoring strategy and assembly quality judgment standard corresponding to the monitoring index can be flexibly and dynamically adjusted based on the monitoring index, which can effectively improve the accuracy and flexibility of the decision control parameters of the TV assembly line and improve the intelligent and automated level of the TV assembly line.

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

[0107] The present invention also provides an intelligent control system for a TV assembly line for implementing the intelligent control method for the TV assembly line as Figure 5 shown, which includes a network construction module, a key information distribution 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, where each server corresponds to a TV assembly line; the key information distribution module is used to divide each TV assembly line in the assembly line network into multiple different assembly areas and assign key information for 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 according to 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, multi-dimensional first characteristic parameters are obtained through the servers corresponding to the other TV assembly lines.

[0110] The key information for each assembly area assigned includes multiple keywords to obtain a more accurate real-time attention degree and real-time attention trend value, so as to adjust the TV assembly control decision parameters and / or the quality judgment standard of quality inspection in a timely and accurate manner and optimize the control process parameters.

[0111] The keywords include, but are not limited to, the back cover, scratches, screen discoloration, screen flashing on startup, screen flickering, indicator light not on, unable to power on, loose screws, motherboard failure, poor sound effect, scratches on the surface of the casing, cracking and paint peeling, etc.

[0112] The first feature parameter acquisition module includes a rule construction unit and a first feature parameter acquisition unit. The rule construction unit is used to construct a feature parameter sharing rule, and the feature parameter sharing rule includes the type of feature parameters participating in the sharing and the update frequency; the first feature parameter acquisition unit is used to obtain multi-dimensional first feature parameters from the servers corresponding to other TV assembly lines according to the feature parameter sharing rule.

[0113] Specifically, the multi-dimensional first feature 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 according to the multi-dimensional first feature parameters, 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 the 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 the 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 degree 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 + spread 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 certain key information in each search engine is equal to the average value of the change rate of the search volume SV within a continuous plurality of preset time periods ΔT, that is, the real-time attention trend value of a certain key information in each search engine where K is the number of preset time periods ΔT, and SV iRepresents the search volume within the i-th preset time period ΔT.

[0119] The real-time attention trend value of each said assembly area Wherein, RATV' j Represents the average real-time attention trend 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 real-time attention trend of the key information of the assembly area obtained by the j-th search engine N represents the number of the key information of the said assembly area, RATV i Represents the real-time attention trend value of the i-th key information in the search engine.

[0120] The real-time attention degree of each said assembly area Wherein, RAL' j Represents the average real-time attention degree 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 real-time attention degree of the key information of the assembly area obtained by the j-th search engine N represents the number of the key information of the said assembly area, RAL i Represents the real-time attention degree of the i-th key information in the search engine.

[0121] Preferably, the search engine weight coefficient

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

[0123] p' 2i And λ i Are all set by technicians. The multi-dimensional second feature parameter includes, but is not limited to, the daily active user number, registered user number, total user score, PC and / or mobile download volume, and overall satisfaction score of the search engine. It can be understood that the multi-dimensional second feature parameter is used to evaluate the overall ranking or popularity of the search engine.

[0124] Combined with the multi-dimensional second feature parameter and according to the formula Obtaining the search engine weight coefficient is beneficial to dynamically adjust the monitoring strategies for different regions and the assembly quality judgment criteria by combining multi-dimensional different data of the associated television assembly line, as well as the real-time attention of consumers and the real-time attention trend value.

[0125] The monitoring index acquisition module includes a correlation 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 television assembly line and other television 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 according to 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 television assembly line according to the correlation, the search engine weight coefficient, the multi-dimensional first characteristic parameters, the real-time attention, and the real-time attention trend value.

[0127] For the correlation, it can be set by technicians or managers according to experience. One method for obtaining the correlation is: through the corresponding server database, obtain the series, model, and batch of the 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 through the number of the same series, model, and batch of the televisions produced by the target television assembly line and other television assembly lines and the corresponding correlation weight coefficients. Among them, the series, model, and batch of the televisions are respectively given corresponding correlation weight coefficients; specifically, the correlation between the target television assembly line and a certain other television assembly line Among them, m1 and m2 respectively represent the number of the same series of televisions produced by the target television assembly line and a certain other television assembly line within the preset assembly time period and the number of the same models under the same series, and α1, α2, and α3 respectively represent the correlation weight coefficients given to the television series, model, and batch, m1, Type i , Batch i respectively represent the number of the same series of televisions produced by the target television assembly line and a certain other television assembly line within the preset assembly time period, the number of the same models of the i-th same series, and the total number of batches of the same models under the i-th same series.

[0128] Preferably, the monitoring index

[0129]

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

[0131] The monitoring index comprehensively considers multiple factors such as the server comprehensive weight, the search engine comprehensive weight, the first characteristic parameters of multiple dimensions, the real-time attention degree and real-time trend value of the assembly area, and the correlation degree between the target TV assembly line and other TV assembly lines. Based on big data technology, it combines data parameters from multiple different angles and dimensions, and can be used as an effective reference basis 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, and can improve the automation and intelligence level of the TV assembly line.

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

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

[0134] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0135] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A method for intelligent control of a TV assembly line, characterized in that: The television assembly line intelligent control method comprises the following steps: Based on preset rules, construct 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 the real-time attention trend value of each assembly area; Obtain a target TV assembly line, and for other TV assembly lines except the target TV assembly line, obtain a multi-dimensional first characteristic parameter through a server corresponding to the other TV assembly lines, and obtain a 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; 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 implemented according to the adjusted monitoring strategy and assembly quality judgment standard.

2. The intelligent control method for a TV assembly line according to claim 1, characterized in that: According to the key information of the assembly area, a specific method for obtaining the real-time attention degree and the real-time attention trend value of each assembly area comprises 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 TV assembly line intelligent control method as claimed in claim 2, characterized in that: The specific method for obtaining the multi-dimensional first characteristic parameter through the server corresponding to other TV assembly lines 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, the multi-dimensional first characteristic parameters are obtained from the servers corresponding to other TV assembly lines.

4. A TV assembly line intelligent control method as claimed in claim 3, characterized in that: 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 comprises 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 of the search engines, and obtaining a search engine weight coefficient according to the multi-dimensional second characteristic parameter; The monitoring index of each assembly area in the target TV 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.

5. A TV assembly line intelligent control method as claimed in claim 4, characterized in that: Search engine weight coefficient 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, p2i represents the value of the second characteristic parameter 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.

6. An intelligent control system for a television assembly line, used to implement the intelligent control method for a television assembly line as claimed in any one of claims 1 to 5, characterized in that: The TV assembly line intelligent control system comprises: A network construction module, used to construct an assembly line network including a plurality of servers based on preset rules, wherein each of the servers corresponds to a television assembly line; A key information allocation module, for dividing each television assembly line in the assembly line network into a plurality of different assembly areas, and allocating assembly area key information to each of the assembly areas; A real-time attention information acquisition module, used for acquiring 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; A first characteristic parameter acquisition module is used to acquire a target TV assembly line, and for other TV assembly lines except the target TV assembly line, acquire multi-dimensional first characteristic parameters through servers corresponding to other TV assembly lines; A monitoring index acquisition module, used for acquiring a 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; 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 implement the monitoring and assembly of the target TV assembly line according to the adjusted monitoring strategy and assembly quality judgment standard.

7. The intelligent control system for a television assembly line according to claim 6, characterized in that: The real-time attention information acquisition module includes: A search engine selection unit, used for selecting a plurality of search engines, and obtaining 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 acquire the real-time attention degree according to the real-time heat data, and to acquire the real-time attention trend value according to the search volume data.

8. The intelligent control system for a television assembly line as claimed in claim 7, characterized in that: The first characteristic parameter acquisition module includes: A rule construction unit, used to construct a feature parameter sharing rule, wherein the feature parameter sharing rule includes the type of feature parameters involved in sharing and the update frequency; The first characteristic parameter acquisition unit is used to acquire multi-dimensional first characteristic parameters from servers corresponding to other TV assembly lines according to the characteristic parameter sharing rule.

9. The intelligent control system for a television assembly line according to claim 8, characterized in that: The monitoring index acquisition module includes: A correlation acquisition unit, used to respectively acquire the correlation between the target TV assembly line and other TV assembly lines; A search engine weight coefficient acquisition unit, used to acquire a multi-dimensional second characteristic parameter of each of the search engines, and acquire a search engine weight coefficient according to the multi-dimensional second characteristic parameter; A monitoring index acquisition unit is used to acquire 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 characteristic parameter, the real-time attention degree and the real-time attention trend value.

10. The intelligent control system for a television assembly line according to claim 9, 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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