Productivity optimization method of optical fiber protection sleeve production line

Abstract

The invention provides a productivity optimization method for an optical fiber protection sleeve production line, and relates to the technical field of productivity optimization analysis. The method comprises the following steps: acquiring historical optical fiber protection sleeve production data, and performing parameter relation analysis based on productivity and yield influence to form productivity and yield relation data; according to historical optical fiber protection sleeve production data, parameter relation analysis based on productivity quality influence is carried out, and productivity quality relation data is formed; target capacity information is obtained, optimization feasibility analysis is carried out in combination with the capacity and yield relation data and the capacity and quality relation data, and standard selection data is determined; and collecting production material cost information, and determining cost selection data according to the standard selection data. According to the method, the productivity is reasonably analyzed from the control parameters of process production, the restrictive relationship among the control parameters, the yield, the quality and the cost is formed, then data reference is provided for comprehensive, sufficient and reasonable consideration of productivity improvement, and the comprehensive improvement effect of the productivity is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of production capacity optimization analysis, and more specifically, to a method for optimizing the production capacity of an optical fiber protective sleeve production line. Background Art

[0002] As an outer protective layer for optical fibers, the optical fiber protective sleeve provides functions such as good waterproofing, shock resistance, and extrusion resistance for optical fibers, effectively ensuring the optical fibers so that they can work stably. For the optical fiber protective sleeve, its production requires different processes, and the content and parameter control of the processes are set according to different layers of the optical fiber protective sleeve.

[0003] With the progress of society and the development of science, optical fibers are used more and more widely. Therefore, the output of the optical fiber protective sleeve also increases synchronously with the demand for optical fibers. Since the optical fiber protective sleeve needs to be completed through a series of controllable and accurate process implementations, it takes a certain amount of production time, and the production time also determines the production capacity of the optical fiber protective sleeve. To ensure that the optical fiber protective sleeve fully meets the market demand, it is extremely urgent to improve the production capacity of the optical fiber production protective sleeve. At present, the improvement of the production capacity of the optical fiber protective sleeve is basically to simply reduce the process time, without considering the entire production process, and it cannot effectively ensure the production capacity while taking into account the quality and production cost.

[0004] Therefore, designing a method for optimizing the production capacity of an optical fiber protective sleeve production line, reasonably analyzing the production capacity through the control parameters of the process production, forming a restrictive relationship between the control parameters and the output, quality, and cost, and then providing a data reference for comprehensively and fully considering the improvement of production capacity, and ensuring the comprehensive improvement effect of production capacity, is an urgent problem to be solved at present. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing the production capacity of an optical fiber protective sleeve production line. By obtaining the historical production data of the optical fiber protective sleeve, analyzing the relationship between the production capacity and all process control parameters, determining the influence of the process control parameters on the production capacity, and at the same time, according to the historical production data of the optical fiber protective sleeve, analyzing the relationship between the production capacity quality and all process control parameters, determining the influence of the process control parameters on the production capacity quality. On the basis of determining the required production capacity and production capacity quality targets, the adjustable range of the process control parameters is determined by using the relationship between the process control parameters and the production capacity and production capacity quality, and the cost range is determined according to these selected adjustment ranges, providing data guidance for optimizing the production capacity of the optical fiber protective sleeve, providing a reasonable and accurate reference for the enterprise to select reasonable process control parameter variables according to the actual situation, and making the production capacity optimization of the optical fiber protective sleeve reasonable, accurate, and economical.

[0006] In a first aspect, the present invention provides a method for optimizing the production capacity of an optical fiber protective cover production line, comprising obtaining historical optical fiber protective cover production data, and performing a parameter relationship analysis based on the impact of production capacity and output to form production capacity and output relationship data; performing a parameter relationship analysis based on the impact of production capacity and quality based on the historical optical fiber protective cover production data to form production capacity and quality relationship data; obtaining target production capacity information, and performing an optimization feasibility analysis in combination with the production capacity and output relationship data and the production capacity and quality relationship data to determine qualified selection data; collecting production material cost information, and determining cost selection data based on the qualified selection data.

[0007] In the present invention, the method obtains the historical production data of the optical fiber protective cover, analyzes the relationship between the production capacity and output and all the process control parameters, and determines the influence of the process control parameters on the production capacity and output. At the same time, according to the historical production data of the optical fiber protective cover, the relationship between the production capacity quality and all the process control parameters is analyzed to determine the influence of the process control parameters on the production capacity and quality. On the basis of determining the required production capacity and output and production capacity quality targets, the relationship between the process control parameters and the production capacity and output and production capacity quality is used to determine the selectable control range of the process control parameters, and the cost range is determined based on these selected adjustment ranges, which provides data guidance for optimizing the production capacity of the optical fiber protective cover, and provides a reasonable and accurate reference for enterprises to select reasonable process control parameter parameters according to actual conditions, so that the production capacity optimization of the optical fiber protective cover is reasonable, accurate and economical.

[0008] As a possible implementation method, historical optical fiber protective cover production data is obtained, and parameter relationship analysis based on the impact of production capacity and output is performed to form production capacity and output relationship data, including: determining the sequential production process of the optical fiber protective cover and all process control parameters under different sequential production processes based on the historical optical fiber protective cover production data; determining the minimum optional parameter value and the maximum optional parameter value of different process control parameters under all sequential production processes based on the historical optical fiber protective cover production data to form process control parameter values. The parameters can be adjusted within the range Among them, n represents the sequence number of different sequential production processes, and k represents the number of different process control parameters corresponding to the sequential production process numbered n; according to the historical optical fiber protective cover production data, the process efficiency influence relationship analysis of different process control parameters under the same sequential production process within the corresponding parameter adjustable range is performed to form process efficiency parameter influence relationship data; according to the historical optical fiber protective cover production data, the mutual constraint relationship analysis of different process control parameters under the same sequential production process is performed to form process parameter constraint relationship data; according to the historical optical fiber protective cover production data, combined with the process control parameter values of different process control parameters under different sequential production processes Determine the corresponding parameter reach adjustment range, process efficiency parameter influence relationship data, and process parameter constraint relationship data, and establish a production capacity-output relationship model.

[0009] In the present invention, to establish the relationship between the production capacity-output of the optical fiber protective sleeve and the process control parameters, three aspects need to be considered. On the one hand, from a macroscopic perspective of the process flow, the factor directly affecting the production capacity-output is the production efficiency of each process. Therefore, to establish a reasonable influence relationship, it is necessary to determine the relationship between the production capacity-output and the process production efficiency. The second aspect is that for the production efficiency of the process, the way to control the process production efficiency is to reasonably adjust and control different process parameters on the process. Therefore, for each process, determining the relationship between the process production efficiency and all the process control parameters on the process is a necessary condition for establishing the relationship between the production capacity-output and the process efficiency. The third aspect is that for the process control parameters in the production process of the optical fiber protective sleeve, the setting of different process control parameters determines the production efficiency of the process, and thus affects the production capacity-output. Therefore, it is necessary to determine the adjustable range of the process control parameters, which determines the combination of process control parameters with which the process efficiency can be achieved. Of course, for each process control parameter, it has a certain settable range due to physical conditions or production requirements. At the same time, considering that there is also a certain mutual influence relationship in the selection of parameter values between different process control parameters under the process, such as the mutual constraint relationship between the spraying duration and the drying duration of the coating, and the mutual constraint relationship between the pressing duration and the pressing pressure of the armor layer, etc. Therefore, when determining the process control parameters, it is also necessary to reasonably determine the influence on the parameter value range caused by the mutual influence between different process control parameters under the same process. By comprehensively considering the relationships among the three aspects, the relationship between the production capacity-output and the process control parameters can be accurately and reasonably determined. Of course, before determining the relationship, it should first be determined and divided into different processes of the optical fiber protective sleeve production and the corresponding process control parameters of the process should be extracted. Here, the division of the process can be carried out according to the completion of the different structural components of the optical fiber protective sleeve in the process, or based on the transfer of the production location, and can be specifically determined in combination with the actual situation. For the process control parameters, the threshold can be set according to the influence degree on the process output or quality from the control phases involved in the process.

[0010] As a possible implementation method, based on the historical production data of optical fiber sheaths, analyze the influence relationship between different process control parameters under the same sequential production process within the reachable adjustment range of the corresponding parameters, and form process efficiency parameter influence relationship data, including: determining the minimum optional efficiency value and the maximum optional efficiency value corresponding to different sequential production processes according to the historical production data of optical fiber sheaths, and forming the reachable efficiency adjustment range corresponding to the sequential production process; for different sequential production processes, obtain an efficiency control data group within the corresponding reachable efficiency adjustment range of the process according to the historical production data of optical fiber sheaths. The efficiency control data groups are grouped in groups of 3 to form an efficiency control data cluster, and the number of efficiency control data clusters is not less than k; in each efficiency control data cluster, k - 1 process control parameters are the same for different efficiency control data groups, and the process control parameter values of the remaining one process control parameter are different in different efficiency control data groups, and for each efficiency control data cluster, the types of process control parameters with different process control parameter values cover all types of process control parameters corresponding to the sequential production process; for each efficiency control data cluster, according to the different process efficiency values V n and the process control parameter values of different process control parameters, perform a single variable analysis to determine the influence relationship of the remaining one process control parameter on the process efficiency when k - 1 process control parameters are fixed values, and form a single parameter process efficiency influence relationship; for all efficiency control data clusters, fit the different single parameter process efficiency influence relationships to form a process efficiency parameter influence relationship formula: wherein, represents the quadratic power relationship constant of the process control parameter numbered k having an impact on the process efficiency, represents the linear power relationship constant of the process control parameter numbered k having an impact on the process efficiency, C n represents the process efficiency influence constant corresponding to the sequential production process numbered n.

[0011] In the present invention, establishing the influence relationship between different process control parameters in the same process on the process production efficiency can accurately grasp the influence degree of different process control parameters on the production capacity and output at the process level, which is a prerequisite for establishing the influence degree of the production efficiency of the process on the production capacity and output of the entire production process. Considering that the influence of different process control parameters in the same process on the process production efficiency is relatively comprehensive, in order to accurately grasp the influence degree of different process control parameters on the corresponding process production efficiency, it is analyzed and determined by using the big data for a single-factor control method. For the production of optical fiber protective sleeves, the influence of a single process control parameter on the process production efficiency is relatively direct and simple, and there will be no relatively complex influence relationship of parameters on the production efficiency. For example, there is basically no complex influence and restriction relationship of high power for the relationship between the control of the coating thickness and the process production efficiency. Therefore, when analyzing the influence relationship between the process control parameter and the process production efficiency in this application, the functional relationship is set with the highest power being a quadratic power function. Since the influence relationship function of the process control parameter on the process production capacity and output is a quadratic power function, at least 3 sets of function equations are required to determine the constant term in the function during the single-factor analysis. Here, the function equations of the influence relationship between different process control parameters and the process production efficiency are given in the form of a group of 3 efficiency control data groups. Of course, for the case where the number of groups formed by the efficiency control data groups exceeds k, the average fitting or other function merging methods can be used for the process control parameters with repeated single variables to obtain a reasonable relationship function. After all, the data of the groups of repeated efficiency control data are all valid and can be fully utilized to improve the accuracy of data analysis. It should be noted here that for the case where other process parameters present constant values during the single-variable analysis, when comprehensively forming the influence relationship formula of the process efficiency parameter from the influence relationship formulas corresponding to different process control parameters, it is also necessary to consider the corresponding levels of the values of other process control parameters that are constant on the process control parameter with a single variable. For example, after determining the quadratic power influence relationship function of a process control parameter, when comprehensively combining the quadratic power influence relationship function with other quadratic power influence relationship functions, use the big data to provide how the two relationship functions are combined when the process control parameters corresponding to the two relationship functions change while other process control parameters remain constant. That is, the constant term of the power function formed by the finally combined relationship formula may not be simply the sum of the constant terms of the two quadratic power influence relationship functions, and corresponding adjustments need to be made.

[0012] As a possible implementation method, based on the historical production data of optical fiber sheaths, analyze the mutual restriction relationships among different process control parameters under the same sequential production process to form process parameter restriction relationship data, including: extracting the combinations of different process control parameter values under each sequential production process in the historical production data of optical fiber sheaths to form corresponding process parameter restriction analysis data, and conducting the following process parameter restriction relationship analysis: for each sequential production process, determine any process control parameter as the starting analysis parameter; sequentially extract any remaining process control parameter except the starting analysis parameter as the relationship analysis parameter, and obtain the combinations of process control parameter values in the process parameter restriction analysis data where only the values of the starting analysis parameter and the relationship analysis parameter change, and conduct relationship fitting analysis: if the value of the starting analysis parameter and the value of the relationship analysis parameter are regular, determine the starting analysis parameter and the relationship analysis parameter as mutually restrictive parameters, and conduct relationship fitting to determine the two-parameter restriction fitting relationship formula; if the value of the starting analysis parameter and the value of the relationship analysis parameter are not regular, determine that the starting analysis parameter and the relationship analysis parameter are not mutually restrictive; exclude the starting analysis parameter, and continue to extract a new starting analysis parameter and conduct relationship fitting analysis from the remaining process control parameters in the sequential production process until only one process control parameter remains after excluding the starting analysis parameter; conduct repeated analysis on the determined two-parameter restriction fitting relationship formula: if there are the same process control parameters in different two-parameter restriction fitting relationship formulas, conduct combined fitting on different two-parameter restriction fitting relationship formulas to form a multi-parameter restriction fitting relationship formula; obtain different multi-parameter restriction fitting relationship formulas corresponding to the sequential production process and the remaining two-parameter restriction fitting relationship formulas to form the process parameter restriction relationship set B corresponding to the sequential production process n 。

[0013] In the present invention, for different process control parameters under the same process, since there is a certain mutual constraint relationship between some or all of the process control parameters, it is necessary to determine the relationship between the process control parameters, and then accurately and reasonably provide the possible values of the process control parameters and the matching method of the parameter values between each other when optimizing the production capacity. Of course, the purpose of considering the mutual constraint relationship between the process control parameters is to reasonably determine the parameter value range and value that can be adjusted for each process control parameter. Therefore, when determining the mutual constraint relationship, it is necessary to determine it from the aspect of the process control parameter affecting the production capacity. In terms of production capacity and output, the process control parameters usually define the time parameters to be consumed by the corresponding process through the obtained parameter values, and the time parameters determine the efficiency of the process production. Therefore, it is more reasonable to determine the mutual constraint relationship between different process control parameters by the process control parameters involved in the process based on big data to determine the degree of influence on the process production efficiency, so that the constraint relationship is more accurate for the parameter selection and control of the process control parameters. Here, for the constraint relationship between different process control parameters, it is determined whether there is a mutual constraint relationship by determining the influence of each process control parameter under the process and other process control parameters on the process efficiency after relative changes and adjustments. Regardless of the production process, if there is a constraint relationship between the settings of two mutually constrained process control parameters, they will inevitably show regularity. Therefore, by analyzing the regularity of the parameter value changes of only these two process control parameters, it is possible to determine whether there is a constraint relationship between the two process control parameters. At the same time, when determining that there is a constraint relationship, the constraint relationship formula can be extracted according to the regularity of the set values. In this way, the constraint relationship between all two process control parameters can be determined. Of course, it is not necessarily that the process control parameters are only mutually constrained between two, but there are also mutual constraints between multiple process control parameters. Therefore, after obtaining the constraint relationship between two process control parameters, it is possible to determine whether there are constraints between multiple process control parameters by analyzing whether there are repeated parts in these constraint relationships. That is, as long as any process control parameters that have a constraint relationship share the process control parameters, the constraint relationship of the several process control parameters involved can be integrated to form a constraint relationship between multiple process control parameters. This synthesis is to use repeated process control parameters to establish a relationship connection and then form a new multi-factor relationship, which is equivalent to combining different constraint relationships.

[0014] As a possible implementation method, based on the historical optical fiber protective cover production data, combined with the process control parameter values of different process control parameters under different production processes The corresponding parameter reach adjustment range, process efficiency parameter influence relationship data, and process parameter constraint relationship data are used to determine and establish a production capacity-output relationship model, including: extracting the process efficiency values and corresponding production capacity-output efficiency values corresponding to all sequential production processes in each production from the historical optical fiber sheath production data to form different production capacity-output efficiency groups; performing relationship analysis on all production capacity-output efficiency groups in the following manner to establish a production capacity-output relationship model: making arbitrary combinations of different production capacity-output efficiency groups to form different production capacity-output efficiency clusters, and the number of production capacity-output efficiency groups in each production capacity-output efficiency cluster is n + 1; for the production capacity-output efficiency groups in different production capacity-output efficiency clusters, using the corresponding production capacity-output efficiency value as the dependent variable and the process efficiency values corresponding to different sequential production processes as independent variables, performing fitting of the value relationship to form the following initial production capacity-output relationship formula: represents the production capacity-output process influence constant corresponding to the sequential production process numbered n in the production capacity-output efficiency cluster numbered i, represents the initial production capacity-output efficiency influence constant corresponding to the production capacity-output efficiency cluster numbered i; for different initial production capacity-output relationship formulas, performing average fitting to form a production capacity-output efficiency relationship model: Q = ζ1*V1 + … + ζ n *V n + q0, q0 represents the production capacity-output efficiency influence constant, where, for V n satisfies: and in each sequential production process, different the relationship between them satisfies the corresponding process parameter constraint relationship set B n .

[0015] In the present invention, by starting from the influence of the production efficiency of the process on the production capacity-output, the influence of the process control parameters on the production efficiency of the process, and the parameter value range of the process control parameters themselves, a relationship between the process control parameters and the production capacity-output is established layer by layer, and then a reasonable and accurate production capacity-output efficiency relationship model is formed. It should be noted here that there are also constraint relationships between the process control parameters of different processes for the process control parameters. However, the constraint relationships between the process control parameters of different processes are mainly dimensional constraints. For example, different processes provide adjustable ranges for the thickness of the coating layer, and finally the overall thickness dimension of the entire coating layer needs to be controlled. Then, there are mutual constraint relationships between the coating layer thickness control parameters under different processes. And since the process control parameters in terms of dimensions have determined a reasonable range through big data, and each process control parameter in terms of dimensions also has a constraint relationship with other control parameters of the same process, there is no need to additionally consider the constraint relationships between the process control parameters of different processes. This kind of constraint relationship has been implicitly included in the range setting of the process control parameters themselves and the constraint relationships between different process control parameters under the same process.

[0016] As a possible implementation, based on the historical production data of the optical fiber protective sleeve, analyze the parameter relationship based on the impact of production capacity on quality to form production capacity-quality relationship data, including: determining the impact relationship between different process control parameters on process quality in different sequential production processes according to the historical production data of the optical fiber protective sleeve to form process quality impact relationship data; determining the impact relationship between different sequential production processes on production capacity-quality according to the historical production data of the optical fiber protective sleeve, and establishing a production capacity-quality relationship model.

[0017] In the present invention, the production capacity optimization not only considers the output problem of production capacity, but the change in output also directly affects the quality of the product. Therefore, when analyzing the production capacity optimization, it is necessary to consider and analyze the production capacity-quality. In order to ensure that the production capacity output and the production capacity-quality can have the characteristics of comparable analysis, the production capacity-quality is also analyzed using the process control parameters. In this way, when setting the production capacity output target subsequently, the production capacity-quality can be reasonably restricted, fully ensuring the production efficiency and quality. The analysis of the production capacity-quality is also carried out from two different levels: the quality of the process and the impact of the process quality on the production capacity-quality.

[0018] As a possible implementation, according to the historical production data of the optical fiber protective sleeve, determine the impact relationship between different process control parameters on process quality in different sequential production processes to form process quality impact relationship data, including: extracting the quality inspection index values of different sequential production processes for each production, and using the maximum value of the quality inspection index values as the quality inspection standard value of the corresponding sequential production process For the quality inspection index values of the same sequential production process under different production times, according to the different process control parameter values under the corresponding production times and the quality inspection standard value carry out a single-factor analysis for the process control parameters to form the following process quality impact relationship formula: R n represents the change amount of the quality inspection index value obtained by the sequential production process numbered n relative to the corresponding quality inspection standard That is represents the impact weight of the process control parameter numbered k on the process quality under the sequential production process numbered n, represents obtaining the minimum value of the parameter reach adjustment range of represents obtaining the parameter reach adjustment range range span value, that is, the difference between the maximum value and the minimum value within the parameter reach adjustment range of

[0019] In the present invention, it can be understood that especially for the production of optical fiber protective sleeves with high precision, whether different processes meet the technical standards is the key factor affecting the final product. Therefore, quality inspections are provided for different processes, and the quality inspection results are used to judge the quality. Of course, the quality of the quality inspection in the process is essentially achieved through the setting and control of the parameter values of the process control parameters of the process. Therefore, different values of the process control parameters often determine the degree of influence on the process quality. Thus, through single-factor analysis based on big data for each process control parameter on the process affecting the process quality, the degree of influence of different process control parameters on the process quality can be accurately and reasonably determined. Since the degree of influence on the process control parameters, different values thereof do not necessarily have a linear influence on the process quality, it is more reasonable to represent the degree of influence in the form of a percentage. Therefore, the influence of different process control parameters on the process quality is characterized by the relative change in the process quality corresponding to the relative value of the influence amount with respect to the standard quality of the process. Here, taking the best quality of the process obtained from big data as the reference for the standard quality of the process can better reflect the reachability of the quality. The relative value of the process control parameter is measured proportionally through its own value range. It should be noted that different process control parameters have different degrees of influence on the process quality, and this difference can be characterized by the influence weight.

[0020] As a possible implementation, based on historical optical fiber protective sleeve production data, determine the influence relationship of different sequential production processes on the production capacity quality, and establish a production capacity quality relationship model, including: extracting the production quality index values of each production and the quality inspection index values of different sequential production processes, and performing single-factor analysis on the quality inspection index values to form the following production capacity quality relationship model: where W represents the production capacity quality index value, and z n represents the influence weight of different sequential production processes on the production capacity quality.

[0021] In the present invention, the quality of the process directly affects the production capacity quality, and different process qualities have different degrees of influence on the production capacity quality. Therefore, reasonable single-factor analysis is required to determine the degree of influence of different process qualities on the production capacity quality, which is also characterized in the form of an influence weight to form an accurate and reasonable production capacity quality relationship model.

[0022] As a possible implementation, obtain the target production capacity information, and perform an optimization feasibility analysis by combining the production capacity-output relationship data and the production capacity-quality relationship data to determine the compliance selection data, including: determining the target production capacity-output efficiency value and the target production capacity-quality index value according to the target production capacity information; determining the parameter-output selection range of different process control parameter values on all sequential production processes according to the target production capacity-output efficiency value and in combination with the production capacity-output efficiency relationship model; determining the parameter-quality selection range of different process control parameter values on all sequential production processes according to the target production capacity-quality index value and in combination with the production capacity-quality relationship model; performing a union operation on the corresponding parameter-output selection range and parameter-quality selection range for different process control parameters to determine the corresponding compliance selection range.

[0023] In the present invention, after obtaining the production capacity-output relationship model and the production capacity-quality relationship model, reasonable and accurate output and quality guidance can be provided during production capacity optimization. The target production capacity information provides the required target production capacity-output and target production capacity-quality. Then, by using the production capacity-output relationship model and the production capacity-quality relationship model, the selection range of process control parameters that meet and are higher than the target production capacity-output and target production capacity-quality can be determined, thereby providing a direction for adjusting the process control parameters for production capacity optimization and making the production capacity optimization more reasonable and efficient.

[0024] As a possible implementation, collect the production material cost information, and determine the cost selection data according to the compliance selection data, including: collecting the unit control cost of different process control parameters under all sequential production processes to form the process parameter control cost; determining the cost selection reference range according to the compliance selection range of different process control parameters and in combination with the corresponding process parameter control cost.

[0025] In the present invention, of course, while improving the process control parameters during production capacity optimization, it will also cause cost changes in the enterprise. Therefore, it is necessary to pay attention to the cost of production capacity optimization. Thus, by combining the parameter values of the process control parameters that can be adjusted during production capacity optimization to trace the corresponding cost unit, and then comprehensively forming the cost range corresponding to production capacity optimization, a reasonable cost control direction can be provided for production capacity optimization, greatly optimizing the cost control of production capacity optimization.

[0026] The beneficial effects of the production capacity optimization method for an optical fiber protection sleeve production line provided by the present invention are as follows:

[0027] This method analyzes the relationship between production capacity and output and all process control parameters by obtaining the historical production data of the optical fiber protective sleeve, determines the influence of process control parameters on production capacity and output, and also analyzes the relationship between production capacity and quality and all process control parameters based on the historical production data of the optical fiber protective sleeve, and determines the influence of process control parameters on production capacity and quality. On the basis of determining the required production capacity and production quality targets, the adjustable range of process control parameters is determined by using the relationship between process control parameters, production capacity and production quality, and the cost range is determined according to these selected adjustment ranges, providing data guidance for optimizing the production capacity of the optical fiber protective sleeve, providing a reasonable and accurate reference for enterprises to select reasonable process control parameter variables according to the actual situation, and making the production capacity optimization of the optical fiber protective sleeve reasonable, accurate and economical. Brief Description of the Drawings

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0029] Figure 1 It is a flowchart of the method for optimizing the production capacity of the optical fiber protective sleeve production line provided by the embodiment of the present invention. Detailed Embodiments

[0030] The technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention.

[0031] As an outer protective layer of the optical fiber, the optical fiber protective sleeve provides good functions such as waterproof, shockproof and extrusion protection for the optical fiber, effectively ensuring the optical fiber and enabling the optical fiber to work stably. For the optical fiber protective sleeve, its production requires different processes, and the content and parameter control of the processes are set according to different layers of the optical fiber protective sleeve.

[0032] With the progress of society and the development of science, optical fibers are used more and more widely. Therefore, the output of the optical fiber protective sleeve also increases synchronously with the demand for optical fibers. Since the optical fiber protective sleeve needs to be completed through a series of controllable and accurate process implementations, it takes a certain amount of production time, and the production time also determines the production capacity of the optical fiber protective sleeve. In order to ensure that the optical fiber protective sleeve fully meets the market demand, it is extremely urgent to improve the production capacity of the optical fiber production protective sleeve. At present, the improvement of the production capacity of the optical fiber protective sleeve is basically to simply reduce the process time, without considering the entire production process, and it cannot effectively ensure the production capacity while taking into account the quality and production cost.

[0033] Reference Figure 1 ,The embodiment of the present invention provides a method for optimizing the production capacity of an optical fiber protection sleeve production line. This method obtains the historical production data of the optical fiber protection sleeve, analyzes the relationship between the production capacity and all process control parameters, determines the influence of the process control parameters on the production capacity, and at the same time, based on the historical production data of the optical fiber protection sleeve, analyzes the relationship between the production capacity quality and all process control parameters, and determines the influence of the process control parameters on the production capacity quality. On the basis of determining the required production capacity and production capacity quality targets, the regulation range that can be selected for the process control parameters is determined by using the relationship between the process control parameters and the production capacity and production capacity quality, and the cost range is determined according to these selected adjustment ranges, providing data guidance for optimizing the production capacity of the optical fiber protection sleeve, providing a reasonable and accurate reference for the enterprise to select reasonable process control parameter variables according to the actual situation, and making the production capacity optimization of the optical fiber protection sleeve reasonable, accurate and economical.

[0034] The method for optimizing the production capacity of the optical fiber protection sleeve production line specifically includes the following steps:

[0035] S1: Obtain the historical production data of the optical fiber protection sleeve, and conduct a parameter relationship analysis based on the influence of the production capacity, to form production capacity relationship data.

[0036] Obtaining the historical production data of the optical fiber protection sleeve and conducting a parameter relationship analysis based on the influence of the production capacity to form production capacity relationship data includes: determining the sequential production processes of the optical fiber protection sleeve and all process control parameters under different sequential production processes according to the historical production data of the optical fiber protection sleeve; determining the minimum selectable parameter value and the maximum parameter selectable value of different process control parameters under all sequential production processes according to the historical production data of the optical fiber protection sleeve, to form the parameter reach adjustment range of the process control parameter value of the process control parameter value where n represents the sequential number of different sequential production processes, and k represents the number of different process control parameters corresponding to the sequential production process numbered n; according to the historical production data of the optical fiber protection sleeve, conduct an analysis of the influence relationship of the process efficiency on different process control parameters under the same sequential production process within the corresponding parameter reach adjustment range, to form process efficiency parameter influence relationship data; according to the historical production data of the optical fiber protection sleeve, conduct an analysis of the mutual restriction relationship of different process control parameters under the same sequential production process, to form process parameter restriction relationship data; according to the historical production data of the optical fiber protection sleeve, and combining the parameter reach adjustment range corresponding to the process control parameter value of different process control parameters under different sequential production processes, the process efficiency parameter influence relationship data, and the process parameter restriction relationship data, determine and establish a production capacity relationship model.

[0037] To establish the relationship between the production capacity and output of the optical fiber protective sleeve and the process control parameters, three aspects need to be considered. On the one hand, from a macroscopic perspective of the process flow, the factor directly affecting the production capacity and output is the production efficiency of each process. Therefore, to establish a reasonable influence relationship, it is necessary to determine the relationship between the production capacity and output and the process production efficiency. On the second hand, for the production efficiency of the process, the way to control the process production efficiency is to reasonably adjust and control different process parameters on the process. Therefore, for each process, determining the relationship between the production efficiency of the process and all process control parameters on the process is a necessary condition for establishing the relationship between the production capacity and output and the process efficiency. On the third hand, for the process control parameters in the production process of the optical fiber protective sleeve, the setting of different process control parameters determines the production efficiency of the process, which in turn affects the production capacity and output. Therefore, it is necessary to determine the adjustable range of the process control parameters, which determines the combination of process control parameters with which the process efficiency can be achieved. Of course, for each process control parameter, it has a certain settable range due to physical conditions or production requirements. At the same time, considering that there are also certain mutual influence relationships in the selection of parameter values between different process control parameters under the process, such as the mutual restriction relationship between the spraying duration and drying duration of the coating, and the mutual restriction relationship between the pressing duration and pressing pressure of the armor layer. Therefore, when determining the process control parameters, it is also necessary to reasonably determine the influence on the parameter value range caused by the mutual influence between different process control parameters under the same process. Considering the relationships among the three aspects comprehensively, the relationship between the production capacity and output and the process control parameters can be determined accurately and reasonably. Of course, before determining the relationship, it is first necessary to determine and divide the different processes of the optical fiber protective sleeve production and extract the corresponding process control parameters of the processes. Here, the division of the processes can be carried out according to the completion of the different structural components of the optical fiber protective sleeve in the process, or based on the transfer of the production location, and can be determined specifically in combination with the actual situation. For the process control parameters, the threshold can be set according to the influence degree on the process output or quality from the control phases involved in the process.

[0038] According to the historical production data of optical fiber sheaths, analyze the influence relationship of different process control parameters in the same sequential production process on the process efficiency within the reachable adjustment range of the corresponding parameters, and form process efficiency parameter influence relationship data, including: determining the minimum optional efficiency value and the maximum optional efficiency value corresponding to different sequential production processes according to the historical production data of optical fiber sheaths, and forming the reachable efficiency adjustment range of the sequential production process; for different sequential production processes, obtain a set of efficiency control data within the reachable efficiency adjustment range of the corresponding process according to the historical production data of optical fiber sheaths. The efficiency control data set is grouped in groups of 3 to form an efficiency control data cluster, and the number of efficiency control data clusters is not less than k; in each efficiency control data cluster, k - 1 process control parameters of different efficiency control data sets are the same, and the process control parameter values of the remaining one process control parameter are different in different efficiency control data sets, and for each efficiency control data cluster, the types of process control parameters with different process control parameter values cover all types of process control parameters corresponding to the sequential production process; for each efficiency control data cluster, according to the different process efficiency values V n corresponding to different efficiency control data sets and the process control parameter values of different process control parameters, perform a single variable analysis to determine the influence relationship of the remaining one process control parameter on the process efficiency when k - 1 process control parameters are fixed values, and form a single parameter process efficiency influence relationship; for all efficiency control data clusters, fit the different single parameter process efficiency influence relationships to form a process efficiency parameter influence relationship formula: wherein, represents the quadratic power relationship constant of the process control parameter numbered k having an impact on the process efficiency, represents the linear power relationship constant of the process control parameter numbered k having an impact on the process efficiency, and C n represents the process efficiency influence constant corresponding to the sequential production process numbered n.

[0039] Establishing the influence relationship of different process control parameters on the process production efficiency in the same process can accurately grasp the influence degree of different process control parameters on the production capacity and output at the process level, which is a prerequisite for establishing the influence degree of the production efficiency of the process on the production capacity and output of the entire production process. Considering that the influence of different process control parameters on the process production efficiency in the same process is relatively comprehensive, in order to accurately grasp the influence degree of different process control parameters on the corresponding process production efficiency, it is analyzed and determined by using the control method of single factor with big data. For the production of optical fiber protective sleeves, the influence of a single process control parameter on the process production efficiency is relatively direct and simple, and there will be no relatively complex influence relationship of parameters on the production efficiency. For example, there is basically no complex influence and restriction relationship of high power between the control of the coating thickness and the process production efficiency. Therefore, when analyzing the influence relationship of process control parameters on the process production efficiency in this application, it is set with a functional relationship of the highest power being the quadratic power. Since the influence relationship function of process control parameters on the process production capacity and output is a quadratic power function, at least 3 sets of function equations are required to determine the constant term in the function during single factor analysis. Here, the function equations of the influence relationship between different process control parameters and the process production efficiency are given in the form of a group of 3 efficiency control data groups. Of course, for the situation where the number of groups formed by the efficiency control data groups exceeds k, the average fitting or other function merging methods can be used for the process control parameters with repeated single variables to obtain a reasonable relationship function. After all, the data of the groups with repeated efficiency control data groups are all valid and can be fully utilized to improve the accuracy of data analysis. It should be noted here that for the situation where other process parameters show constant values during single variable analysis, when comprehensively forming the influence relationship formula of process efficiency parameters from the influence relationship formulas corresponding to different process control parameters, it is also necessary to consider the corresponding levels of the values of other process control parameters that are constant on the process control parameter with a single variable. For example, after determining the quadratic power influence relationship function of a process control parameter, when comprehensively combining the quadratic power influence relationship function with other quadratic power influence relationship functions, use big data to provide how the two relationship functions are combined when the process control parameters corresponding to them change while other process control parameters are constant values. That is, the constant term of the power function formed by the finally combined relationship formula may not be simply the sum of the constant terms of the two quadratic power influence relationship functions, and corresponding adjustments need to be made.

[0040] According to the historical production data of optical fiber sheaths, analyze the mutual restraint relationship among different process control parameters under the same sequential production process to form process parameter restraint relationship data, including: extracting the combinations of different process control parameter values under each sequential production process in the historical production data of optical fiber sheaths to form corresponding process parameter restraint analysis data, and conducting the following method of process parameter restraint relationship analysis: for each sequential production process, determine any one of the process control parameters as the starting analysis parameter; sequentially extract any one of the remaining process control parameters except the starting analysis parameter as the relationship analysis parameter, and obtain the combinations of process control parameter values in the process parameter restraint analysis data where only the values of the starting analysis parameter and the relationship analysis parameter change, and conduct relationship fitting analysis: if the value of the starting analysis parameter and the value of the relationship analysis parameter are regular, determine the starting analysis parameter and the relationship analysis parameter as mutually restrictive parameters, and conduct relationship fitting to determine the two-parameter restraint fitting relationship formula; if the value of the starting analysis parameter and the value of the relationship analysis parameter are not regular, determine that the starting analysis parameter and the relationship analysis parameter are not mutually restrictive; exclude the starting analysis parameter, and continue to extract a new starting analysis parameter and conduct relationship fitting analysis from the remaining process control parameters in the sequential production process until only one process control parameter remains after excluding the starting analysis parameter; conduct repeated analysis on the determined two-parameter restraint fitting relationship formula: if there are the same process control parameters in different two-parameter restraint fitting relationship formulas, conduct combined fitting on different two-parameter restraint fitting relationship formulas to form a multi-parameter restraint fitting relationship formula; obtain different multi-parameter restraint fitting relationship formulas corresponding to the sequential production process and the remaining two-parameter restraint fitting relationship formulas to form the process parameter restraint relationship set B corresponding to the sequential production process n 。

[0041] For different process control parameters under the same process, since there is a certain mutual constraint relationship between some or all of the process control parameters, it is necessary to determine the relationship between the process control parameters, and then accurately and reasonably provide the possible values of the process control parameters and the matching method of the parameter values between each other when optimizing the production capacity. Of course, the purpose of considering the mutual constraint relationship between the process control parameters is to reasonably determine the parameter value range and value that can be adjusted for each process control parameter. Therefore, when determining the mutual constraint relationship, it is necessary to determine it from the aspect of the process control parameter affecting the production capacity. In terms of production capacity and output, the process control parameters usually define the time parameters to be consumed by the corresponding process through the obtained parameter values, and the time parameters determine the efficiency of the process production. Therefore, it is more reasonable to determine the mutual constraint relationship between different process control parameters by using the big data-based method to determine the degree of influence of the process control parameters involved in the process on the process production efficiency, so that the constraint relationship is more accurate for the parameter selection and control of the process control parameters. Here, for the constraint relationship between different process control parameters, it is determined whether there is a mutual constraint relationship by determining the influence of each process control parameter under the process and other process control parameters on the process efficiency after relative changes and adjustments. Regardless of the production process, if there is a constraint relationship between the settings of two mutually constrained process control parameters, they will inevitably show regularity. Therefore, by analyzing the regularity of the parameter value changes of only these two process control parameters, it is possible to determine whether there is a constraint relationship between the two process control parameters. At the same time, when determining that there is a constraint relationship, the constraint relationship formula can be extracted according to the regularity of the set values. In this way, the constraint relationship between all two process control parameters can be determined. Of course, it is not necessarily that the process control parameters are only mutually constrained between two, but there are also mutual constraints between multiple process control parameters. Therefore, after obtaining the constraint relationship between two process control parameters, it is possible to determine whether there are constraints between multiple process control parameters by analyzing whether there are repeated parts in these constraint relationships. That is, as long as any process control parameters that have a constraint relationship share the process control parameters, the constraint relationship of the several process control parameters involved can be integrated to form a constraint relationship between multiple process control parameters. This synthesis is to use repeated process control parameters to establish a relationship connection and then form a new multi-factor relationship, which is equivalent to combining different constraint relationships.

[0042] Based on the historical optical fiber protective sleeve production data, combined with the process control parameter values of different process control parameters under different production processes Corresponding parameter reach adjustment ranges, process efficiency parameter influence relationship data, and process parameter constraint relationship data are used to determine and establish a production capacity - output relationship model, including: extracting the process efficiency values and corresponding production capacity - output efficiency values corresponding to all sequential production processes in each production of historical optical fiber sheath production data to form different production capacity - output efficiency groups; performing relationship analysis on all production capacity - output efficiency groups in the following way to establish a production capacity - output relationship model: arbitrarily combining different production capacity - output efficiency groups to form different production capacity - output efficiency clusters, and the number of production capacity - output efficiency groups in each production capacity - output efficiency cluster is n + 1; for the production capacity - output efficiency groups in different production capacity - output efficiency clusters, using the corresponding production capacity - output efficiency values as the dependent variables and the process efficiency values corresponding to different sequential production processes as the independent variables, performing fitting of the value relationship to form the following initial production capacity - output relationship formula: represents the production capacity - output process influence constant corresponding to the sequential production process numbered n in the production capacity - output efficiency cluster numbered i, represents the initial production capacity - output efficiency influence constant corresponding to the production capacity - output efficiency cluster numbered i; for different initial production capacity - output relationship formulas, performing average fitting to form a production capacity - output efficiency relationship model: Q = ζ1*V1+…+ζ n *V n +q0, q0 represents the production capacity - output efficiency influence constant, where, for V n satisfies: and in each sequential production process, different the relationship between them satisfies the corresponding process parameter constraint relationship set B n .

[0043] By establishing the relationship between process control parameters and production capacity - output layer by layer from the influence of process production efficiency on production capacity - output, the influence of process control parameters on process production efficiency, and the parameter value range of process control parameters themselves, a reasonable and accurate production capacity - output efficiency relationship model is formed. It should be noted here that there are also constraint relationships between process control parameters of different processes. However, the constraint relationships between process control parameters of different processes are mainly dimensional constraints. For example, different processes provide adjustable ranges of cladding layer thickness, and finally the overall cladding layer thickness dimension needs to be controlled. Then, there are mutual constraint relationships between the cladding layer thickness control parameters under different processes. And since the dimensional process control parameters have determined a reasonable range through big data, and each dimensional process control parameter also has a constraint relationship with other control parameters of the same process, there is no need to consider the constraint relationship between process control parameters of different processes additionally. This constraint relationship is already implicit in the range setting of process control parameters themselves and the constraint relationship between different process control parameters under the same process.

[0044] S2: Analyze the parameter relationship based on the impact of production capacity and quality according to the historical production data of optical fiber protective sleeves, and form the production capacity-quality relationship data.

[0045] According to the historical production data of optical fiber protective sleeves, analyze the parameter relationship based on the impact of production capacity and quality, and form the production capacity-quality relationship data, including: Determine the impact relationship between different process control parameters and process quality in different sequential production processes according to the historical production data of optical fiber protective sleeves, and form the process quality impact relationship data; Determine the impact relationship between different sequential production processes and production capacity-quality according to the historical production data of optical fiber protective sleeves, and establish a production capacity-quality relationship model.

[0046] The optimization of production capacity not only considers the output of production capacity, but the change in output also directly affects the quality of products. Therefore, when analyzing the optimization of production capacity, it is necessary to consider and analyze production capacity-quality. In order to ensure that production capacity output and production capacity-quality can have the characteristics of comparable analysis, the analysis of production capacity-quality also uses process control parameters for analysis, so that the production capacity-quality can be reasonably restricted when setting the target of production capacity output later, and the production efficiency and quality can be fully guaranteed. The analysis of production capacity-quality is also carried out from two different levels: the quality of the process and the impact of process quality on production capacity-quality.

[0047] According to the historical production data of optical fiber protective sleeves, determine the impact relationship between different process control parameters and process quality in different sequential production processes, and form the process quality impact relationship data, including: Extract the quality inspection index values of different sequential production processes for each production, and use the maximum value of the quality inspection index values as the quality inspection standard value of the corresponding sequential production process For the quality inspection index values of the same sequential production process under different production times, according to the values of different process control parameters under the corresponding production times and the quality inspection standard value Conduct a single-factor analysis for process control parameters to form the following process quality impact relationship formula: R n represents the change amount of the quality inspection index value obtained by the sequential production process numbered n relative to the corresponding quality inspection standard That is represents the impact weight of the process control parameter numbered k on the process quality under the sequential production process numbered n, represents obtaining the minimum value of the parameter reach adjustment range of represents obtaining the parameter reach adjustment range range span value, that is, the parameter reach adjustment range the difference between the maximum value and the minimum value within.

[0048] It is understandable that, especially for the production of optical fiber protective sleeves with high precision, whether different processes meet the technical standards is the key to ultimately affecting the product. Therefore, quality inspections of the process quality are provided in different processes, and the quality inspection results are used to judge the quality. Of course, the essence of the quality inspection of the process is still achieved through the setting and control of the parameter values of the process control parameters of the process. Therefore, different values of the process control parameters often determine the degree of influence on the process quality. Thus, through single-factor analysis of the influence of each process control parameter on the process quality based on big data, the degree of influence of different process control parameters on the process quality can be accurately and reasonably determined. Since the influence degree for the process control parameters, different values of the process control parameters do not necessarily have a linear influence on the process quality, it is more reasonable to characterize the influence degree in the form of a percentage. Therefore, the influence of different process control parameters on the process quality is characterized by the relative change in the process quality corresponding to the relative value of the influence compared to the standard quality of the process. Here, taking the best quality of the process obtained from big data as the reference for the standard quality of the process better reflects the reachability of the quality. The relative value of the process control parameter is measured proportionally through its own value range. It should be noted that different process control parameters have different degrees of influence on the process quality, and this difference can be characterized by the influence weight.

[0049] According to the historical production data of optical fiber protective sleeves, determine the influence relationship of different sequential production processes on the production capacity quality, and establish a production capacity quality relationship model, including: extracting the production quality index values of each production and the quality inspection index values of different sequential production processes, and conducting single-factor analysis on the quality inspection index values to form the following production capacity quality relationship model: Among them, W represents the production capacity quality index value, and z n represents the influence weight of different sequential production processes on the production capacity quality.

[0050] The quality of the process directly affects the production capacity quality, and different process qualities have different degrees of influence on the production capacity quality. Therefore, reasonable single-factor analysis is required to determine the degree of influence of different process qualities on the production capacity quality, which is also characterized in the form of an influence weight to form an accurate and reasonable production capacity quality relationship model.

[0051] S3: Obtain the target production capacity information, and combine the production capacity-output relationship data and the production capacity-quality relationship data to conduct an optimization feasibility analysis to determine the qualified selection data.

[0052] Obtain the target production capacity information, and conduct an optimization feasibility analysis by combining the production capacity-output relationship data and the production capacity-quality relationship data to determine the compliance selection data, including: according to the target production capacity information, determine the target production capacity-output efficiency value and the target production capacity-quality index value; according to the target production capacity-output efficiency value, and in combination with the production capacity-output efficiency relationship model, determine the parameter-output selection range of different process control parameters on all sequential production processes; according to the target production capacity-quality index value, and in combination with the production capacity-quality relationship model, determine the parameter-quality selection range of different process control parameters on all sequential production processes; for different process control parameters, perform the union operation of the corresponding parameter-output selection range and the parameter-quality selection range to determine the corresponding compliance selection range.

[0053] After obtaining the production capacity-output relationship model and the production capacity-quality relationship model, it is possible to provide reasonable and accurate output and quality guidance during production capacity optimization. The target production capacity information provides the target production capacity-output and the target production capacity-quality that need to be obtained. Then, by using the production capacity-output relationship model and the production capacity-quality relationship model, the selection range of process control parameters that meet and are higher than the target production capacity-output and the target production capacity-quality can be determined. Furthermore, it provides the direction for adjusting the process control parameters for production capacity optimization, making the production capacity optimization more reasonable and efficient.

[0054] S4: Collect the production material cost information, and determine the cost selection data according to the compliance selection data.

[0055] Collect the production material cost information, and determine the cost selection data according to the compliance selection data, including: collect the unit control cost of different process control parameters under all sequential production processes to form the process parameter control cost; according to the compliance selection range of different process control parameters, and in combination with the corresponding process parameter control cost, determine the cost selection reference range.

[0056] Of course, while improving the process control parameters, production capacity optimization will also cause changes in the enterprise's costs. Therefore, it is necessary to pay attention to the costs of production capacity optimization. Thus, by combining the parameter values of the process control parameters that can be adjusted by production capacity optimization to trace the corresponding cost units, and then comprehensively forming the cost range corresponding to production capacity optimization, it can provide a reasonable cost control direction for production capacity optimization, greatly optimizing the cost control of production capacity optimization.

[0057] In summary, the beneficial effects of the production capacity optimization method for the optical fiber protection sleeve production line provided by the embodiments of the present invention are as follows:

[0058] This method analyzes the relationship between production capacity and output and all process control parameters by obtaining the historical production data of the optical fiber protective sleeve, determines the influence of process control parameters on production capacity and output, and at the same time analyzes the relationship between production capacity and quality and all process control parameters according to the historical production data of the optical fiber protective sleeve, and determines the influence of process control parameters on production capacity and quality. On the basis of determining the required production capacity and production quality targets, the adjustable range of process control parameters is determined by using the relationship between process control parameters and production capacity and production quality, and the cost range is determined according to these selected adjustable ranges, providing data guidance for optimizing the production capacity of the optical fiber protective sleeve, providing a reasonable and accurate reference for the enterprise to select reasonable process control parameter variables according to the actual situation, and making the production capacity optimization of the optical fiber protective sleeve reasonable, accurate and economical.

[0059] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. Taking the information indicated by a certain piece of information as the information to be indicated, there are many ways to indicate the information to be indicated in the specific implementation process. For example, but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated, etc. The information to be indicated can also be indirectly indicated by indicating other information, where there is an association relationship between the other information and the information to be indicated. It is also possible to only indicate a part of the information to be indicated, while the other parts of the information to be indicated are known or pre-agreed. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information pre-agreed (such as stipulated in the protocol), so as to reduce the indication overhead to a certain extent. At the same time, the common part of each piece of information can be identified and indicated uniformly to reduce the indication overhead caused by separately indicating the same information.

[0060] In addition, the specific indication method can also be various existing indication methods, such as, but not limited to, the above indication methods and their various combinations, etc. The specific details of various indication methods can refer to the prior art and will not be elaborated herein. As can be seen from the above, for example, when it is necessary to indicate multiple pieces of information of the same type, there may be a situation where the indication methods of different pieces of information are different. In the specific implementation process, the required indication method can be selected according to the specific needs. The embodiments of the present application do not limit the selected indication method. In this way, the indication methods involved in the embodiments of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0061] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately, and the sending periods and / or sending timings of these sub-information can be the same or different. The specific sending method is not limited in the embodiments of the present application. Among them, the sending periods and / or sending timings of these sub-information can be predefined, for example, predefined according to a protocol, or can be configured by the sending device by sending configuration information to the receiving device.

[0062] "Predefined" or "preconfigured" can be implemented by pre-saving corresponding codes, tables or other ways that can be used to indicate relevant information in the device. The embodiments of the present application do not limit its specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be separately provided, or can be integrated in an encoder, a decoder, a processor, or a communication device. The one or more memories can also be partially separately provided and partially integrated in a decoder, a processor, or a communication device. The type of the memory can be any form of storage medium, which is not limited in the embodiments of the present application.

[0063] The "protocol" involved in the embodiments of the present application can refer to a protocol family in the communication field, a standard protocol with a frame structure similar to that of a protocol family, or a related protocol applied to a future communication system. The embodiments of the present application do not make specific limitations thereto.

[0064] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if", and "when" all refer to that the device will perform corresponding processing under a certain objective situation, which does not limit the time, and does not require the device to have a judgment action when implemented, nor does it mean that there are other limitations.

[0065] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B may represent A or B. The "and / or" in the embodiments of the present application is merely a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Also, in the description of the embodiments of the present application, unless otherwise specified, "a plurality of" means two or more than two. "At least one (item)" or its similar expression refers to any combination of these items, including any combination of a single item or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple. Additionally, for the convenience of clearly describing the technical solutions of the embodiments of the present application, in the embodiments of the present application, terms such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and roles. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and "first", "second", etc. do not necessarily mean different. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific way for easy understanding.

[0066] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and this processor may also be other general - purpose processors, digital signal processors (DSPs), application - specific integrated circuits (ASICs), field - programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general - purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0067] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0068] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0069] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context before and after.

[0070] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0071] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0072] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0073] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0074] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0077] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0078] As described above, the above are only specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for optimizing the production capacity of an optical fiber protective sleeve production line, characterized in that, Including: Obtain historical production data of optical fiber protection sleeves, and conduct an analysis of the parameter relationship based on the impact on production capacity and output, to form production capacity-output relationship data; According to the historical production data of optical fiber protection sleeves, conduct an analysis of the parameter relationship based on the impact on production capacity and quality, to form production capacity-quality relationship data; Obtain target production capacity information, and combine the production capacity-output relationship data and the production capacity-quality relationship data to conduct an optimization feasibility analysis, and determine compliance selection data; Collect production material cost information, and determine cost selection data according to the compliance selection data.

2. The method for optimizing the production capacity of the optical fiber protection sleeve production line according to claim 1, characterized in that The obtaining of the historical production data of optical fiber protection sleeves, and the analysis of the parameter relationship based on the impact on production capacity and output, to form production capacity-output relationship data, includes: According to the historical production data of optical fiber protection sleeves, determine the sequential production processes of the optical fiber protection sleeves and all process control parameters under different sequential production processes; Determine the minimum selectable parameter values and the maximum parameter selectable values of different process control parameters under all the sequential production processes according to the historical optical fiber protection sleeve production data, and form the reachable adjustment range of the process control parameter values wherein, n represents the sequential number of different sequential production processes, and k represents the number of different process control parameters corresponding to the sequential production process numbered n; The reachable adjustment range of the parameters According to the historical production data of optical fiber protection sleeves, conduct an analysis of the impact relationship of process efficiency within the reachable adjustment range of the corresponding parameters for different process control parameters under the same sequential production process, to form process efficiency parameter impact relationship data; According to the historical production data of optical fiber protection sleeves, conduct an analysis of the mutual restriction relationship for different process control parameters under the same sequential production process, to form process parameter restriction relationship data; Based on the historical production data of the optical fiber protection sleeve, and in combination with the process control parameter values of different process control parameters under different production processes in the given order Determine the corresponding reachable adjustment range of the parameters, the influence relationship data of the process efficiency parameters, and the constraint relationship data of the process parameters, and establish a production capacity and output relationship model.

3. The production capacity optimization method of the optical fiber protection sleeve production line according to claim 2, characterized in that, The conducting of the analysis of the impact relationship of process efficiency within the reachable adjustment range of the corresponding parameters for different process control parameters under the same sequential production process according to the historical production data of optical fiber protection sleeves, to form process efficiency parameter impact relationship data, includes: According to the historical production data of optical fiber protection sleeves, determine the minimum optional efficiency value and the maximum optional efficiency value corresponding to different sequential production processes, to form the reachable process efficiency adjustment range corresponding to the sequential production process; For different sequential production processes, according to the historical production data of optical fiber protection sleeves, obtain a group of efficiency control data within the corresponding reachable process efficiency adjustment range, and the group of efficiency control data is grouped in sets of 3 to form an efficiency control data group, and the number of the efficiency control data groups is not less than k; In each efficiency control data group, k-1 of the process control parameters are the same for different efficiency control data groups, and the process control parameter value of the remaining one process control parameter is different in different efficiency control data groups, and for each efficiency control data group, the types of the process control parameters with different determined process control parameter values cover all types of process control parameters corresponding to the sequential production process; For each of the efficiency control data groups, according to the different process efficiency values V corresponding to different efficiency control data groups n and the process control parameter values of different process control parameters, perform a single variable analysis to determine the influence relationship of the remaining one process control parameter on the process efficiency when k - 1 process control parameters are fixed values, and form a single parameter process efficiency influence relationship; For all the efficiency control data groups, fit the relationship of different single-parameter process efficiency impacts to form a process efficiency parameter impact relationship formula: Among them, represents the quadratic power relationship constant of the process control parameter numbered k having an impact on the process efficiency, represents the linear power relationship constant of the process control parameter numbered k having an impact on the process efficiency, C n represents the process efficiency impact constant corresponding to the sequential production process numbered n.

4. The method for optimizing the production capacity of the optical fiber protective sleeve production line according to claim 3, wherein, The conducting of the analysis of the mutual restriction relationship for different process control parameters under the same sequential production process according to the historical production data of optical fiber protection sleeves, to form process parameter restriction relationship data, includes: Extract the combinations of different process control parameter values under each of the sequential production processes in the historical optical fiber sheath production data to form corresponding process parameter constraint analysis data, and conduct process parameter constraint relationship analysis in the following manner: For each of the sequential production processes, determine any one of the process control parameters as the starting analysis parameter; Successively extract any one of the remaining process control parameters except the starting analysis parameter as the relationship analysis parameter, and obtain the combinations of process control parameter values in the process parameter constraint analysis data where only the values of the starting analysis parameter and the relationship analysis parameter change, and conduct relationship fitting analysis: If the value of the starting analysis parameter and the value of the relationship analysis parameter show regularity, determine that the starting analysis parameter and the relationship analysis parameter are mutually constrained parameters, and conduct relationship fitting to determine the two-parameter constraint fitting relationship formula; If the value of the starting analysis parameter and the value of the relationship analysis parameter do not show regularity, determine that the starting analysis parameter and the relationship analysis parameter are not mutually constrained; Exclude the starting analysis parameter, and continue to extract a new starting analysis parameter and conduct relationship fitting analysis from the remaining process control parameters in the sequential production process until only one process control parameter remains after excluding the starting analysis parameter; Conduct repeated analysis on the determined two-parameter constraint fitting relationship formula: If there are the same process control parameters in different two-parameter constraint fitting relationship formulas, conduct combined fitting on the different two-parameter constraint fitting relationship formulas to form a multi-parameter constraint fitting relationship formula; Obtain the different multi-parameter constraint fitting relationships corresponding to the sequential production processes and the remaining two-parameter constraint fitting relationships, and form the process parameter constraint relationship set B corresponding to the sequential production processes n 。 5. The method for optimizing the production capacity of the optical fiber protection sleeve production line according to claim 4, wherein, Based on the historical production data of the optical fiber protection sheath and combining the process control parameter values of different process control parameters under different production processes in the given order Determine and establish a production capacity - output relationship model based on the corresponding reachable adjustment range of the parameters, the data of the influence relationship between the process efficiency parameters, and the data of the restrictive relationship between the process parameters, including: Extract the process efficiency values and the corresponding production capacity and output efficiency values corresponding to all the sequential production processes in each production in the historical optical fiber sheath production data to form different production capacity and output efficiency groups; Conduct relationship analysis in the following manner on all the production capacity and output efficiency groups to establish a production capacity and output relationship model: Arbitrarily combine different production capacity and output efficiency groups to form different production capacity and output efficiency clusters, and the number of production capacity and output efficiency groups in each production capacity and output efficiency cluster is n + 1; For the production capacity and output efficiency groups in different production capacity and output efficiency clusters, use the corresponding production capacity and output efficiency values as the dependent variables and the process efficiency values corresponding to different sequential production processes as the independent variables, and conduct fitting of the value relationship to form the following initial production capacity and output relationship formula: represents the production capacity and output process influence constant corresponding to the sequential production process numbered n in the production capacity and output efficiency group numbered i, represents the initial production capacity and output efficiency influence constant corresponding to the production capacity and output efficiency group numbered i; Conduct average fitting on different initial production capacity and output relationship formulas to form a production capacity and output efficiency relationship model: Q = ζ1*V1 + … + ζ n *V n + q0, q0 represents the influence constant of production capacity and output efficiency, where, for V n Satisfy: and different among each of the said sequential production processes the relationship between them satisfies the corresponding set B of process parameter constraint relationships n .

6. The production capacity optimization method of the optical fiber protection sleeve production line according to claim 5, characterized in that Based on the historical optical fiber sheath production data, conduct parameter relationship analysis based on the influence of production capacity and quality to form production capacity and quality relationship data, including: According to the historical optical fiber sheath production data, determine the influence relationship of different process control parameters on process quality in different sequential production processes to form process quality influence relationship data; According to the historical optical fiber sheath production data, determine the influence relationship of different sequential production processes on production capacity and quality, and establish a production capacity and quality relationship model.

7. The production capacity optimization method of the optical fiber protection sleeve production line according to claim 6, characterized in that Determining the influence relationship of different process control parameters on the process quality in different sequential production processes according to the historical optical fiber sheath production data, and forming process quality influence relationship data, including: Extract the quality inspection index values for each production of different production processes in the said order, and use the maximum value of the quality inspection index values as the quality inspection standard value for the corresponding production process in the said order For the quality inspection index values of the same sequential production processes under different production batches, according to different values of the process control parameters under the corresponding production batches and the quality inspection standard values perform a single-factor analysis on the process control parameters to form the following process quality influence relationship formula: R n represents the change amount of the quality inspection index value obtained in the sequential production process numbered n relative to the corresponding quality inspection standard i.e., represents the influence weight of the process control parameter numbered k on the process quality under the sequential production process numbered n, represents obtaining the reachable adjustment range of the parameter the minimum value represents obtaining the reachable adjustment range of the parameter the range span value, i.e., the reachable adjustment range of the parameter is the difference between the maximum value and the minimum value within the range 8. The method for optimizing the production capacity of the optical fiber protection sleeve production line according to claim 7, characterized in that, Determining the influence relationship of different sequential production processes on the production capacity quality according to the historical optical fiber sheath production data, and establishing a production capacity quality relationship model, including: Extracting the production quality index values of each production and the quality inspection index values of different sequential production processes corresponding thereto, and performing single-factor analysis on the quality inspection index values to form the following production capacity quality relationship model: Among them, W represents the production capacity quality index value, and z n represents the influence weight of different sequential production processes on production capacity quality.

9. The method for optimizing the production capacity of the optical fiber protective sleeve production line according to claim 8, characterized in that Obtaining the target production capacity information, and performing an optimization feasibility analysis in combination with the production capacity output relationship data and the production capacity quality relationship data to determine the qualified selection data, including: Determining the target production capacity output efficiency value and the target production capacity quality index value according to the target production capacity information; Determining the parameter output selection range of different process control parameter values on all sequential production processes according to the target production capacity output efficiency value and in combination with the production capacity output efficiency relationship model; Determining the parameter quality selection range of different process control parameter values on all sequential production processes according to the target production capacity quality index value and in combination with the production capacity quality relationship model; Performing a union operation on the corresponding parameter output selection range and the parameter quality selection range for different process control parameters to determine the corresponding qualified selection range.

10. The method for optimizing the production capacity of the optical fiber protection sleeve production line according to claim 9, characterized in that, Collecting the production material cost information, and determining the cost selection data according to the qualified selection data, including: Collecting the unit control cost of different process control parameters under all sequential production processes to form the process parameter control cost; Determining the cost selection reference range according to the qualified selection range of different process control parameters and in combination with the corresponding process parameter control cost.

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