A control method for silica gel production equipment

Abstract

The present invention provides a method for controlling silica gel production equipment, relating to the technical field of silica gel production control. The method comprises collecting historical production record data of the equipment, performing production control analysis based on product demand, and forming demand production control reference data; performing cost analysis based on control adjustment based on the historical production record data of the equipment, and forming production control adjustment cost reference data; obtaining target product demand information, and performing production control analysis in combination with the demand production control reference data and the production control adjustment cost reference data, and forming target demand production control reference data; performing production control based on the target demand production control reference data, and monitoring the production process to form real-time production monitoring information. By combining the historical production control data of the equipment to establish production control adjustment methods for different silica gel products, the method greatly improves the adaptability of equipment production, production efficiency, and production quality.

Description

Technical Field

[0001] The present invention relates to the technical field of silica gel production control, and in particular to a silica gel production equipment control method. Background Art

[0002] Silicone products are widely used in various industries due to their stability and excellent performance. Therefore, the current demand for silicone products is very high. In order to ensure an adequate supply of silicone products, silicone production has gradually begun to be automated and intelligent, greatly improving the production efficiency of silicone products.

[0003] Currently, there are two types of equipment control for silicone product production. One is manual on-site control, which requires significant labor costs, especially for large-scale production lines. Of course, this approach can also lead to production problems due to human factors, impacting efficiency and quality. The other is intelligent control, which uses a control center to automate the production equipment on the production line. However, there is currently no systematic method for effectively adjusting and controlling different silicone products to ensure quality and production efficiency.

[0004] Therefore, designing a control method for silicone production equipment and establishing a production control adjustment method for different silicone products by combining the historical production control data of the equipment, which greatly improves the adaptability of equipment production as well as production efficiency and production quality, is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for controlling silicone production equipment, which collects historical production records of production equipment to extract data for adjusting production control parameters for different product requirements, and at the same time determines the cost situation generated by the adjustment of production control parameters. On this basis, reasonable production control parameters are set according to the requirements of target products, so as to achieve production control of equipment on the basis of cost savings as much as possible. Compared with directly setting production control parameters for target product requirements without data reference, the efficiency of production control parameter setting can be greatly improved, and the accuracy and rationality of production control parameter setting can also be improved, thereby ensuring the production efficiency of the product while ensuring the quality of the product produced, and providing a reliable data reference for the efficient and reliable realization of intelligent production of different silicone products.

[0006] In a first aspect, the present invention provides a method for controlling silicone production equipment, comprising collecting historical production record data of the equipment, conducting production control analysis based on product demand, and forming demand production control reference data; conducting cost analysis based on control adjustment based on the historical production record data of the equipment, and forming production control adjustment cost reference data; obtaining target product demand information, and conducting production control analysis in combination with the demand production control reference data and the production control adjustment cost reference data, and forming target demand production control reference data; conducting production control based on the target demand production control reference data, and monitoring the production process to form real-time production monitoring information.

[0007] In the present invention, the method collects historical production records of production equipment to extract data for adjusting production control parameters for different product requirements, and at the same time determines the cost situation generated by the adjustment of production control parameters. On this basis, reasonable production control parameters are set according to the requirements of the target product, so as to achieve production control of the equipment on the basis of cost savings as much as possible. Compared with directly setting production control parameters for target product requirements without data reference, the efficiency of setting production control parameters can be greatly improved, and the accuracy and rationality of setting production control parameters can also be improved, thereby ensuring the production efficiency of the product while ensuring the quality of the product produced, and providing a reliable data reference for the efficient and reliable realization of intelligent production of different silicone products.

[0008] As a possible implementation method, historical production record data of the equipment is collected, and production control analysis based on product demand is performed to form demand production control reference data, including: conducting raw material usage analysis based on product demand based on the historical production record data of the equipment to form demand production raw material usage reference data; conducting control parameter influence analysis based on product demand based on the historical production record data of the equipment to form demand production control parameter influence relationship data; and combining demand production raw material usage reference data and demand production control parameter influence relationship data to form demand production control reference data.

[0009] In the present invention, the production control analysis of the acquired equipment historical production record data for product demand mainly considers two aspects. One aspect is the relationship information between the amount of raw materials used to produce the product and the silicone product to be produced. Through the material analysis, it is possible to reasonably estimate the amount of raw materials used for different types of silicone products, which provides a reference for enterprise production in cost statistics and cost optimization. It can also provide a reasonable and intuitive reference for whether the equipment has undergone performance changes in terms of materials. The second aspect is the constraints for reasonable parameter settings of production control parameters based on the needs of different products. Through this constraint relationship, the product demand information can be intuitively linked to the setting of production control parameters, which can provide a more accurate and reasonable guidance for the adjustment and setting of production control parameters as a reference. The two aspects of data information lay the foundation for providing better production control parameter settings and material determination for the subsequent target product needs, making production control more efficient and accurate.

[0010] As a possible implementation method, based on the equipment's historical production record data, raw material usage analysis based on product demand is performed to form reference data for required production raw material usage, including: extracting the required product mold information for different historical production items in the equipment's historical production record data, and determining the corresponding effective plane area S based on the required product mold information. n ,in, n represents the number of different historical production projects, V n H represents the volume of the required product determined by the mold information of the required product corresponding to the historical production project numbered n. n Indicates the average thickness of the required product determined based on the required product mold information corresponding to the historical production project numbered n; extracts the different effective plane areas S in the equipment historical production record data n The corresponding actual raw material consumption Q n , and perform fitting analysis to determine the material relationship Q=F(S) between the actual raw material consumption and the effective plane area.

[0011] In the present invention, when extracting information about the relationship between product demand and material usage, it is considered that different silicone products are formed using different molds. Molds can provide the most accurate size and shape information for the product. The raw material usage used in product production is directly related to the product's size and shape. When the production process remains unchanged, the raw material usage and the product's size and shape have a stable relative relationship. Therefore, the relationship between the product and the raw material usage can be provided by analyzing the product's size and shape. Here, for silicone products, the main factor affecting its raw material usage is the product's volume. Secondly, different parts of the product will provide different raw material amounts due to the thickness of the formed product. After all, the excess material loss generated by parts of different thicknesses is different. Therefore, using the ratio of volume to average thickness of the product as a parameter to measure the raw material usage relationship of the product is reasonable and accurate. By fitting the relationship between the volume to average thickness ratio (i.e., the effective planar area) and the actual raw material usage of the product in historical records, a reasonable and accurate material usage relationship can be established.

[0012] As a possible implementation method, based on the historical production record data of the equipment, the influence analysis of the control parameters based on product demand is carried out to form the demand production control parameter influence relationship data, including: according to the effective plane area S n , for the same effective plane area S in the equipment historical production record data n The corresponding historical product demand production control data are clustered to form a historical effective production control data set A k , k represents the number of different historical effective production control data sets; for different historical effective production control data sets A k Conduct production control parameter impact relationship analysis on product demand to form effective product demand production control parameter impact relationship information; conduct comparative analysis of similar impact relationships on different effective product demand production control parameter impact relationship information to form demand production control parameter impact relationship data.

[0013] In the present invention, the influence relationship analysis between production control parameters and product demand is carried out. First, it is considered that the changes in production control parameters under different effective plane areas may be different. Therefore, when carrying out the influence relationship analysis, clustering based on effective plane areas must be carried out first. In this way, the production control data under the same effective plane area will have more obvious characteristics of the same influence relationship. In addition, for product demand, especially silicone products, there are not many demand conditions, but there are indeed more production control parameters. Therefore, it is necessary to clarify which production control parameters affect which product demand conditions, so as to more accurately determine the influence relationship between production control parameters and product demand. At the same time, considering that the influence relationship between production control parameters and product demand will have the same influence degree and influence correlation under different effective plane areas, it is also necessary to carry out reasonable merging, which greatly reduces the complexity of data analysis and can also accurately and reasonably realize the setting adjustment of production control parameters for product demand.

[0014] As a possible implementation method, different historical effective production control data sets A k Conduct production control parameter impact relationship analysis for product demand to form effective product demand production control parameter impact relationship information, including: historical effective production control data set A k , determine all production control parameters and the historical effective production control quantities corresponding to different production control parameters; for the historical effective production control data set A k For different product demands in the market, the historical effective production control quantities of all production control parameters corresponding to different product demands are clustered based on the change of single demand conditions to form a production control quantity set with single demand condition changes; the demand control impact analysis of the production control quantity sets with different single demand condition changes is carried out in the following way: the production control parameters that change after the single demand condition changes are determined as the production control impact parameters corresponding to the demand condition; according to the condition value of the single demand condition, and the historical effective production control quantities of all corresponding production control influencing parameters Perform relationship fitting to form a demand condition impact relationship function Among them, m represents the number of different demand conditions under the historical effective production control data set numbered k, and i represents the number of different production control influencing parameters corresponding to the demand condition numbered m under the historical effective production control data set numbered k; all demand condition influencing relationship functions are combined to form a demand condition influencing relationship function set corresponding to the historical effective production control data set; according to the production control quantity set of a single demand condition change, the maximum and minimum values corresponding to different production control influencing parameters are determined to form a single demand control parameter range corresponding to the production control influencing parameter.

[0015] In the present invention, a product demand condition is affected by multiple production control parameters. Simultaneously regulating these production control parameters is neither intuitive nor rational for determining how these adjustments affect product demand. Therefore, when establishing the impact relationship between different product demand conditions and different production control parameters, a single demand change analysis approach is more intuitive and rational. After all, when other demand conditions remain unchanged, a change in just one condition can intuitively determine which production control parameters influence this demand condition. This change data can also be used to establish the impact relationship between the demand condition and the multiple production control parameters affected.

[0016] As a possible implementation method, a comparative analysis of similar influence relationships is performed on the influence relationship information of different effective product demand production control parameters to form demand production control parameter influence relationship data, including: performing similar comparative analysis on different demand condition influence relationship function sets in the following manner: setting the similar relationship distance difference range C corresponding to different demand conditions m , for any two different demand conditions affecting the relationship function set: if there is any demand condition affecting the relationship function Then it is determined that the two demand condition influence relationship function sets are not similar influence relationship function sets, u and v both take any value of m, and u≠v; if any demand condition influence relationship function satisfies Then, the two demand condition influence relationship function sets are determined to be similar demand condition influence relationship function sets, and the two demand condition influence relationship functions with corresponding numbers in the two similar demand condition influence relationship function sets are merged and fitted to form a new demand condition influence relationship function, an effective area range is established for the effective plane area corresponding to the two demand condition influence relationship function sets, and the same single demand control parameter ranges in the two demand condition influence relationship function sets are merged to form a new single demand parameter range; all new demand condition influence relationship function sets formed after similar mergers and the remaining demand condition influence relationship function sets that have not undergone similar mergers are collected to form demand production control parameter influence relationship data.

[0017] In the present invention, the similarity analysis can reasonably merge different effective plane areas, and determine that within a certain effective plane area range, the degree and manner of influence of the adjustment of the production control parameters corresponding to the product demand conditions on the product demand conditions are consistent. Of course, taking into account the characteristics of big data analysis, when making similarity judgments, it is not necessary for the influence relationship functions to be completely corresponding to determine that they are similar, but it can be determined to be similar within a certain range of differences. The range size of the similarity relationship distance difference range can be set as needed, and can also be determined using big data analysis. After determining that the influence relationship function sets under two different effective plane area ranges are similar, reasonable merging processing can be performed. The merging process includes three aspects. One is to define the range of the effective plane area, and use the effective plane areas corresponding to the two influence relationship function sets as boundary values to form a corresponding range, indicating that the influence relationship between the product demand conditions and different production control parameters within this range is consistent. The second is to merge the different influence relationship functions in the two influence relationship function sets that are determined to be similar. The merging method is to fit the function curve. This fitting can be an average fitting or an extreme value fitting. The average fitting is to obtain the corresponding points between the two influence relationship functions, average them, and then fit all the averaged points. The extreme value fitting is to segment the two relationship functions and intercept the minimum or maximum value parts and smooth them to form a curve. The third is to merge the parameter value ranges of different production control parameters in the form of a union to ensure that the production control parameters have a wider range of optional values. Of course, this value is also desirable and reasonable. After all, they are all data obtained under two influence relationship functions.

[0018] As a possible implementation method, a cost analysis based on control adjustment is performed based on the historical production record data of the equipment to form production control adjustment cost reference data, including: extracting the energy consumption values of different production control parameters based on the historical production record data of the equipment, and determining the corresponding unit consumption costs based on the energy consumption values; sorting the production control parameters in order from small to large according to the unit consumption cost to form a cost consumption comparison order; and collecting the unit consumption costs corresponding to different production control parameters and the cost consumption comparison order to form production control adjustment cost reference data.

[0019] In the present invention, adjusting and controlling production control parameters inevitably results in changes, so cost evaluation of production control parameters can effectively achieve cost control. Similarly, sorting production control parameters by unit cost is primarily to provide guidance for sequential adjustment when subsequently adjusting control parameters for target products and to provide a sequential reference for data adjustment for cost control.

[0020] As a possible implementation method, target product demand information is obtained, and production control analysis is performed in combination with demand production control reference data and production control adjustment cost reference data to form target demand production control reference data, including: determining target mold information of the target product based on the target product demand information, and determining the target plane area S based on the target mold information. aim ; According to the target plane area S aim And the material relationship Q = F (S), determine the target raw material dosage Q aim ; According to the target product demand information, determine the target demand conditions and corresponding target condition values; according to the target plane area S aim , determine the demand condition impact relationship function set corresponding to the target product demand information; combine the demand condition impact relationship function set, target condition value and production control adjustment cost reference data to conduct production control analysis and form target demand production control reference data.

[0021] In this invention, adjusting and determining production control parameters based on target product demand information requires two key aspects: determining the raw material quantities required for the target product and setting and adjusting the production control parameters. The appropriate raw material quantities can be determined based on the mold information for the target product and the material relationships.

[0022] As a possible implementation method, production control analysis is performed in combination with the demand condition influence relationship function set, the target condition value and the production control adjustment cost reference data to form the target demand production control reference data, including: corresponding the different demand condition influence relationship functions in the demand condition influence relationship function set to the target condition value, and determining the different production control parameters in the demand condition influence relationship function in the following way: determining the corresponding minimum value according to the single demand parameter range corresponding to different production control parameters; adjusting the production control parameters in turn in order of increasing the cost consumption comparison order from small to large, and adjusting to ensure that the minimum number of production control parameter values are adjusted to meet the corresponding demand condition influence relationship function to obtain the corresponding target condition value; and collecting the target control values of all production control parameters to form the target demand production control reference data.

[0023] In the present invention, the influence relationship function can be used to reasonably determine different production control parameters based on target demand conditions. Of course, considering that different production control parameters have different impacts on costs, if the influence relationship function can be established, the cost-saving method is to start with the minimum possible value of the production control parameter and try to adjust the production control parameter with lower unit cost to ensure reasonable control costs.

[0024] As a possible implementation method, production control is performed according to the target demand production control reference data, and the production process is monitored to form real-time production monitoring information, including: setting parameters according to the target demand production control reference data, and real-time monitoring of parameter values in the following ways: setting the parameter allowable deviation range, when any real-time parameter value of the target demand production control parameter exceeds the parameter allowable deviation range, an early warning message is generated; obtaining the real-time raw material feeding amount, and based on the target raw material usage Q aim Carry out real-time monitoring in the following ways: If the real-time raw material feeding amount reaches the target raw material usage Q aim , then the feeding termination information is formed.

[0025] In the present invention, after determining the raw material usage and the parameter values of different production control parameters, the data produced by the equipment can be collected in real time for comparative analysis, and at the same time, monitoring and early warning are carried out for problems generated by the comparative analysis, effectively ensuring that the equipment can be maintained in time, thereby further improving production efficiency.

[0026] The beneficial effects of the silica gel production equipment control method provided by the present invention are:

[0027] This method collects historical production records of production equipment to extract data for adjusting production control parameters for different product needs, and at the same time determines the cost situation caused by the adjustment of production control parameters. On this basis, reasonable production control parameters are set according to the needs of target products, so as to achieve production control of equipment on the basis of cost savings as much as possible. Compared with directly setting production control parameters for target product needs without data reference, the method can greatly improve the efficiency of production control parameter setting, and can also improve the accuracy and rationality of production control parameter setting, ensure product production efficiency while ensuring the quality of the products produced, and provide a reliable data reference for the efficient and reliable realization of intelligent production of different silicone products. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 This is a step diagram of a method for controlling silica gel production equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION

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

[0031] Silicone products are widely used in various industries due to their stability and excellent performance. Therefore, the current demand for silicone products is very high. In order to ensure an adequate supply of silicone products, silicone production has gradually begun to be automated and intelligent, greatly improving the production efficiency of silicone products.

[0032] Currently, there are two types of equipment control for silicone product production. One is manual on-site control, which requires significant labor costs, especially for large-scale production lines. Of course, this approach can also lead to production problems due to human factors, impacting efficiency and quality. The other is intelligent control, which uses a control center to automate the production equipment on the production line. However, there is currently no systematic method for effectively adjusting and controlling different silicone products to ensure quality and production efficiency.

[0033] refer to Figure 1 , an embodiment of the present invention provides a method for controlling silicone production equipment. The method collects historical production records of the production equipment to extract data for adjusting the production control parameters for different product requirements, and at the same time determines the cost situation caused by the adjustment of the production control parameters. On this basis, reasonable production control parameters are set according to the requirements of the target product, so as to achieve production control of the equipment on the basis of saving costs as much as possible. Compared with directly setting the production control parameters according to the requirements of the target product without data reference, the efficiency of setting the production control parameters can be greatly improved, and the accuracy and rationality of setting the production control parameters can also be improved, thereby ensuring the production efficiency of the product while ensuring the quality of the product, and providing a reliable data reference for the efficient and reliable realization of intelligent production of different silicone products.

[0034] The silica gel production equipment control method specifically includes the following steps:

[0035] S1: Collect historical production record data of equipment, conduct production control analysis based on product demand, and form demand production control reference data.

[0036] Collect historical production record data of the equipment, conduct production control analysis based on product demand, and form demand production control reference data, including: conducting raw material usage analysis based on product demand based on historical production record data of the equipment, and forming demand production raw material usage reference data; conducting control parameter influence analysis based on product demand based on historical production record data of the equipment, and forming demand production control parameter influence relationship data; and combining demand production raw material usage reference data and demand production control parameter influence relationship data to form demand production control reference data.

[0037] The production control analysis of the acquired equipment historical production record data for product demand mainly considers two aspects. One aspect is the relationship information between the amount of raw materials used to produce the product and the silicone product to be produced. Through material analysis, it is possible to reasonably estimate the amount of raw materials used for different types of silicone products, which provides a reference for enterprise production in cost statistics and cost optimization. It can also provide a reasonable and intuitive reference for whether the equipment has undergone performance changes in terms of materials. The second aspect is the constraints on the reasonable setting of production control parameters based on the needs of different products. Through this constraint relationship, the product demand information can be intuitively linked to the setting of production control parameters. As a reference, it can provide more accurate and reasonable guidance for the adjustment and setting of production control parameters. The two aspects of data information lay the foundation for providing better production control parameter settings and material determination for the subsequent target product needs, making production control more efficient and accurate.

[0038] According to the equipment's historical production record data, the raw material usage analysis based on product demand is carried out to form the reference data of the required production raw material usage, including: extracting the required product mold information for different historical production items in the equipment's historical production record data, and determining the corresponding effective plane area S based on the required product mold information. n ,in, n represents the number of different historical production projects, V n H represents the volume of the required product determined by the mold information of the required product corresponding to the historical production project numbered n. n Indicates the average thickness of the required product determined based on the required product mold information corresponding to the historical production project numbered n; extracts the different effective plane areas S in the equipment historical production record data n The corresponding actual raw material consumption Q n , and perform fitting analysis to determine the material relationship Q=F(S) between the actual raw material consumption and the effective plane area.

[0039] When extracting information about the relationship between product requirements and material usage, consider that different silicone products are formed using different molds. These molds can provide the most accurate information about the product's size and shape. The raw material usage in product production is directly related to the product's size and shape. Assuming the production process remains unchanged, the relative relationship between raw material usage and product size and shape remains stable. Therefore, analyzing the product's size and shape can provide information about the relationship between the product and raw material usage. For silicone products, raw material usage is primarily influenced by the product's volume. Secondly, different parts of the product require different amounts of raw material depending on the thickness of the product. After all, different thicknesses result in different excess material losses. Therefore, using the ratio of volume to average product thickness as a reasonable and accurate parameter to measure the raw material usage relationship is a reasonable and accurate measure. By fitting the ratio of volume to average thickness (i.e., effective planar area) to the actual raw material usage of historically recorded products, a reasonable and accurate material usage relationship can be established.

[0040] According to the equipment's historical production record data, the influence analysis of control parameters based on product demand is carried out to form demand production control parameter influence relationship data, including: according to the effective plane area S n , for the same effective plane area S in the equipment historical production record data n The corresponding historical product demand production control data are clustered to form a historical effective production control data set A k , k represents the number of different historical effective production control data sets; for different historical effective production control data sets A k Conduct production control parameter impact relationship analysis on product demand to form effective product demand production control parameter impact relationship information; conduct comparative analysis of similar impact relationships on different effective product demand production control parameter impact relationship information to form demand production control parameter impact relationship data.

[0041] When analyzing the impact relationship between production control parameters and product demand, we first consider that the changes in production control parameters may be different under different effective plane areas. Therefore, when conducting the impact relationship analysis, we must first perform clustering based on the effective plane area. In this way, the production control data under the same effective plane area will have more obvious characteristics of the same impact relationship. In addition, for product demand, especially silicone products, there are not many demand conditions, but there are relatively many production control parameters. Therefore, it is necessary to clarify which production control parameters affect which product demand conditions, so as to more accurately determine the impact relationship between production control parameters and product demand. At the same time, considering that the impact relationship between production control parameters and product demand will have the same impact degree and impact correlation under different effective plane areas, it is also necessary to perform reasonable merging, which greatly reduces the complexity of data analysis and can also accurately and reasonably realize the setting and adjustment of production control parameters for product demand.

[0042] For different historical effective production control data sets A k Conduct production control parameter impact relationship analysis for product demand to form effective product demand production control parameter impact relationship information, including: historical effective production control data set A k , determine all production control parameters and the historical effective production control quantities corresponding to different production control parameters; for the historical effective production control data set A k For different product demands in the market, the historical effective production control quantities of all production control parameters corresponding to different product demands are clustered based on the change of single demand conditions to form a production control quantity set with single demand condition changes; the demand control impact analysis of the production control quantity sets with different single demand condition changes is carried out in the following way: the production control parameters that change after the single demand condition changes are determined as the production control impact parameters corresponding to the demand condition; according to the condition value of the single demand condition, and the historical effective production control quantities of all corresponding production control influencing parameters Perform relationship fitting to form a demand condition impact relationship function Among them, m represents the number of different demand conditions under the historical effective production control data set numbered k, and i represents the number of different production control influencing parameters corresponding to the demand condition numbered m under the historical effective production control data set numbered k; all demand condition influencing relationship functions are combined to form a demand condition influencing relationship function set corresponding to the historical effective production control data set; according to the production control quantity set of a single demand condition change, the maximum and minimum values corresponding to different production control influencing parameters are determined to form a single demand control parameter range corresponding to the production control influencing parameter.

[0043] A product's demand condition can be affected by multiple production control parameters, and adjusting these production control parameters simultaneously is neither intuitive nor rational for determining how these adjustments affect product demand. Therefore, when establishing the impact relationship between different product demand conditions and different production control parameters, a single demand change analysis is more intuitive and rational. After all, when other demand conditions remain unchanged, a change in just one condition can intuitively determine which production control parameters influence this demand condition. This change data can also be used to establish the impact relationship between the demand condition and the multiple affected production control parameters.

[0044] Conducting comparative analysis of similar impact relationships on different effective product demand production control parameter impact relationship information to form demand production control parameter impact relationship data, including: conducting similar comparative analysis on different demand condition impact relationship function sets in the following ways: setting the similar relationship distance difference range C corresponding to different demand conditions m , for any two different demand conditions affecting the relationship function set: if there is any demand condition affecting the relationship function Then it is determined that the two demand condition influence relationship function sets are not similar influence relationship function sets, u and v both take any value of m, and u≠v; if any demand condition influence relationship function satisfies Then, the two demand condition influence relationship function sets are determined to be similar demand condition influence relationship function sets, and the two demand condition influence relationship functions with corresponding numbers in the two similar demand condition influence relationship function sets are merged and fitted to form a new demand condition influence relationship function, an effective area range is established for the effective plane area corresponding to the two demand condition influence relationship function sets, and the same single demand control parameter ranges in the two demand condition influence relationship function sets are merged to form a new single demand parameter range; all new demand condition influence relationship function sets formed after similar mergers and the remaining demand condition influence relationship function sets that have not undergone similar mergers are collected to form demand production control parameter influence relationship data.

[0045] Similarity analysis can rationally merge different effective planar areas, determining that within a certain effective planar area range, the degree and manner of impact of adjustments to production control parameters corresponding to product demand conditions on product demand conditions are consistent. Of course, given the characteristics of big data analysis, similarity is not determined only when the influence relationship functions completely correspond. Rather, similarity can be determined within a certain range of differences. The range of similarity distance difference can be set as needed or determined using big data analysis. After determining that the influence relationship function sets within two different effective planar area ranges are similar, a rational merging process can be performed. This merging process includes three aspects: first, defining the effective planar area range. Using the effective planar areas corresponding to the two influence relationship function sets as boundary values, a corresponding range is formed, indicating that within this range, the influence relationship between product demand conditions and different production control parameters is consistent. The second is to merge the different influence relationship functions in the two influence relationship function sets that are determined to be similar. The merging method is to fit the function curve. This fitting can be an average fitting or an extreme value fitting. The average fitting is to obtain the corresponding points between the two influence relationship functions, average them, and then fit all the averaged points. The extreme value fitting is to segment the two relationship functions and intercept the minimum or maximum value parts and smooth them to form a curve. The third is to merge the parameter value ranges of different production control parameters in the form of a union to ensure that the production control parameters have a wider range of optional values. Of course, this value is also desirable and reasonable. After all, they are all data obtained under two influence relationship functions.

[0046] S2: Conduct cost analysis based on control adjustments based on the equipment’s historical production record data to generate production control adjustment cost reference data.

[0047] Based on the historical production record data of the equipment, a cost analysis based on control adjustment is performed to form production control adjustment cost reference data, including: extracting the energy consumption values of different production control parameters based on the historical production record data of the equipment, and determining the corresponding unit consumption costs based on the energy consumption values; sorting the production control parameters in order from small to large according to the unit consumption cost to form a cost consumption comparison order; and collecting the unit consumption costs corresponding to different production control parameters and the cost consumption comparison order to form production control adjustment cost reference data.

[0048] Adjusting production control parameters inevitably results in changes, so cost evaluation of these parameters can be a valuable tool for cost control. Similarly, sorting production control parameters by unit cost provides guidance for subsequent adjustments to target products and provides a sequential reference for cost control.

[0049] S3: Obtain target product demand information, and perform production control analysis in combination with demand production control reference data and production control adjustment cost reference data to form target demand production control reference data.

[0050] Obtain target product demand information, and conduct production control analysis in combination with demand production control reference data and production control adjustment cost reference data to form target demand production control reference data, including: determining target mold information of target product based on target product demand information, and determining target plane area S based on target mold information aim ; According to the target plane area S aim And the material relationship Q = F (S), determine the target raw material dosage Q aim ; According to the target product demand information, determine the target demand conditions and corresponding target condition values; according to the target plane area S aim , determine the demand condition impact relationship function set corresponding to the target product demand information; combine the demand condition impact relationship function set, target condition value and production control adjustment cost reference data to conduct production control analysis and form target demand production control reference data.

[0051] To adjust and determine production control parameters based on target product demand information, two aspects must be achieved: one is to determine the raw material dosage required for the target product, and the other is to set and adjust production control parameters. The raw material dosage can be determined based on the mold information formed by the target product and the material relationship to determine the appropriate material dosage.

[0052] Combined with the demand condition influence relationship function set, the target condition value and the production control adjustment cost reference data, a production control analysis is performed to form the target demand production control reference data, including: corresponding the different demand condition influence relationship functions in the demand condition influence relationship function set to the target condition value, and determining the different production control parameters in the demand condition influence relationship function in the following way: determining the corresponding minimum value according to the single demand parameter range corresponding to different production control parameters; adjusting the production control parameters in order of increasing the value according to the cost consumption comparison order from small to large, and adjusting to ensure that the minimum number of production control parameter values are adjusted to meet the corresponding demand condition influence relationship function to obtain the corresponding target condition value; and gathering the target control values of all production control parameters to form the target demand production control reference data.

[0053] By using the influence relationship function based on the target demand conditions, different production control parameters can be reasonably determined. Of course, considering that different production control parameters have different impacts on costs, if the influence relationship function can be established, the way to save costs is to start with the minimum value of the production control parameter and try to adjust the production control parameter with lower unit cost to ensure reasonable control costs.

[0054] S4: Perform production control based on target demand production control reference data, monitor the production process, and generate real-time production monitoring information.

[0055] According to the target demand production control reference data, production control is carried out, and the production process is monitored to form real-time production monitoring information, including: setting parameters according to the target demand production control reference data, and real-time monitoring of parameter values in the following ways: setting the parameter allowable deviation range, when any real-time parameter value of the target demand production control parameter exceeds the parameter allowable deviation range, an early warning message is generated; obtaining the real-time raw material feeding amount, and based on the target raw material usage Q aim Carry out real-time monitoring in the following ways: If the real-time raw material feeding amount reaches the target raw material usage Q aim , then the feeding termination information is formed.

[0056] After determining the raw material usage and parameter values of different production control parameters, the data produced by the equipment can be collected in real time for comparative analysis. At the same time, monitoring and early warning can be carried out for problems arising from the comparative analysis, effectively ensuring that the equipment can be maintained in a timely manner and further improving production efficiency.

[0057] In summary, the beneficial effects of the silica gel production equipment control method provided by the embodiment of the present invention are:

[0058] This method collects historical production records of production equipment to extract data for adjusting production control parameters for different product needs, and at the same time determines the cost situation caused by the adjustment of production control parameters. On this basis, reasonable production control parameters are set according to the needs of target products, so as to achieve production control of equipment on the basis of cost savings as much as possible. Compared with directly setting production control parameters for target product needs without data reference, the method can greatly improve the efficiency of production control parameter setting, and can also improve the accuracy and rationality of production control parameter setting, ensure product production efficiency while ensuring the quality of the products produced, and provide a reliable data reference for the efficient and reliable realization of intelligent production of different silicone products.

[0059] In the embodiment of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as 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. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association relationship between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (such as specified in the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0060] In addition, the specific indication method can also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can be referred to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, there may be a situation where the indication methods for different information are different. In the specific implementation process, the required indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment 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 period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiments of this application. The sending period and / or sending time of these sub-information can be predefined, for example, predefined according to a protocol, or can be configured by the transmitting device by sending configuration information to the receiving device.

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

[0063] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems. The embodiments of the present application do not make specific limitations on this.

[0064] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will perform corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to perform judgment actions 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 with each other are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present application is only 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 at the same time, and B exists alone. A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit differences. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete 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 the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or 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 read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (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 and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link 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 other combination. 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 program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (such as infrared, wireless, microwave, etc.) method. 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 data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, 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" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0070] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

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

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

[0073] Those skilled in the art will 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 aforementioned method embodiments and will not be repeated here.

[0074] In the 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 schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

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

[0077] If the 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 the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0078] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for controlling silica gel production equipment, characterized in that: include: Collect historical production record data of equipment, conduct production control analysis based on product demand, and form demand production control reference data; Conducting cost analysis based on control adjustments based on the historical production record data of the equipment to form production control adjustment cost reference data; Obtaining target product demand information, and performing production control analysis in combination with the demand production control reference data and the production control adjustment cost reference data to form target demand production control reference data; Perform production control based on the target demand production control reference data, monitor the production process, and generate real-time production monitoring information; Among them, the equipment's historical production record data is collected to conduct production control analysis based on product demand, forming demand production control reference data, including: Based on the historical production record data of the equipment, perform raw material usage analysis based on product demand to form reference data for required production raw material usage; Based on the historical production record data of the equipment, the influence analysis of the control parameters based on product demand is carried out to form the demand-production control parameter influence relationship data: According to the effective plane area S n , for the same effective plane area S in the historical production record data of the equipment n The corresponding historical product demand production control data are clustered to form a historical effective production control data set A k , k represents the number of different historical valid production control data sets; For different historical effective production control data sets A k Conduct production control parameter impact relationship analysis based on product demand to form effective product demand production control parameter impact relationship information; Performing comparative analysis on similar impact relationships of different effective product demand production control parameter impact relationship information to form demand production control parameter impact relationship data; The demand production raw material usage reference data and the demand production control parameter influence relationship data are collected to form the demand production control reference data.

2. The silica gel production equipment control method according to claim 1, characterized in that: The method of analyzing raw material usage based on product demand based on the historical production record data of the equipment to form reference data of required production raw material usage includes: Extract the required product mold information for different historical production items in the equipment's historical production record data, and determine the corresponding effective plane area S based on the required product mold information. n ,in, n represents the number of different historical production projects, V n H represents the volume of the required product determined by the mold information of the required product corresponding to the historical production project numbered n. n Indicates the average thickness of the required product determined based on the required product mold information corresponding to the historical production project numbered n; Extract the different effective plane areas S in the equipment historical production record data n The corresponding actual raw material consumption Q n , and perform fitting analysis to determine the material relationship Q=F(S) between the actual raw material consumption and the effective plane area.

3. The silica gel production equipment control method according to claim 2, characterized in that: The different historical effective production control data sets A k Conduct production control parameter impact relationship analysis based on product demand to form effective product demand production control parameter impact relationship information, including: For the historical effective production control data set A k , determine all production control parameters and historical effective production control quantities corresponding to different production control parameters; For the historical effective production control data set A k Clustering the historical effective production control quantities of all production control parameters corresponding to different product demands based on changes in single demand conditions to form a single demand condition change production control quantity set; The demand control impact analysis is performed on the production control quantity set of different single demand condition changes in the following manner: Determine the production control parameters that change when a single demand condition changes, and determine them as the production control impact parameters corresponding to the demand condition; Conditional value based on a single requirement condition and the historical effective production control quantities of all the corresponding production control influencing parameters Perform relationship fitting to form a demand condition impact relationship function Wherein, m represents the number of different demand conditions under the historical valid production control data set numbered k, and i represents the number of different production control influencing parameters corresponding to the demand condition numbered m under the historical valid production control data set numbered k; Gather all of the demand condition impact relationship functions to form a demand condition impact relationship function set corresponding to the historical effective production control data set; According to the single demand condition change production control quantity set, the maximum value and the minimum value corresponding to the different production control influencing parameters are determined to form a single demand control parameter range corresponding to the production control influencing parameter.

4. The silica gel production equipment control method according to claim 3, characterized in that: The comparative analysis of similar impact relationships of different effective product demand production control parameter impact relationship information to form the demand production control parameter impact relationship data includes: For different demand condition impact relationship function sets, similarity comparison analysis is performed in the following manner: Set the similarity relationship distance difference range C corresponding to different demand conditions m , for any two different demand conditions, the influence relationship function set is: If the influence relationship function of any of the demand conditions is It is determined that the two demand condition influence relationship function sets are not similar influence relationship function sets, u and v both take any value of m, and u≠v; If the influence relationship function of any of the requirements is satisfied then determining that the two demand condition influence relationship function sets are similar demand condition influence relationship function sets, and merging and fitting the two demand condition influence relationship functions with corresponding numbers in the two similar demand condition influence relationship function sets to form a new demand condition influence relationship function, establishing an effective area range for the effective plane areas corresponding to the two demand condition influence relationship function sets, and merging the same single demand control parameter ranges in the two demand condition influence relationship function sets to form a new single demand parameter range; All new demand condition impact relationship function sets formed after similar mergers occur and the remaining demand condition impact relationship function sets that have not undergone similar mergers are collected to form the demand production control parameter impact relationship data.

5. The silica gel production equipment control method according to claim 4, characterized in that: The process of performing cost analysis based on control adjustment according to the historical production record data of the equipment to form production control adjustment cost reference data includes: Extracting energy consumption values of different production control parameters based on the historical production record data of the equipment, and determining corresponding unit consumption costs based on the energy consumption values; Sorting the production control parameters according to the unit consumption cost from small to large to form a cost consumption comparison order; The unit consumption costs corresponding to different production control parameters and the cost consumption comparison sequence are collected to form the production control adjustment cost reference data.

6. The silica gel production equipment control method according to claim 5, characterized in that: The acquiring of target product demand information and performing production control analysis in combination with the demand production control reference data and the production control adjustment cost reference data to form target demand production control reference data includes: According to the target product demand information, the target mold information of the target product is determined, and the target plane area S is determined according to the target mold information. aim ; According to the target plane area S aim And the material relationship Q = F (S), determine the target raw material dosage Q aim ; Determining target demand conditions and corresponding target condition values based on the target product demand information; According to the target plane area S aim , determining a demand condition impact relationship function set corresponding to the target product demand information; The production control analysis is performed in combination with the demand condition influence relationship function set, the target condition value and the production control adjustment cost reference data to form the target demand production control reference data.

7. The silica gel production equipment control method according to claim 6, characterized in that: The step of combining the demand condition influence relationship function set, the target condition value, and the production control adjustment cost reference data to perform production control analysis and form the target demand production control reference data includes: Different demand condition impact relationship functions in the demand condition impact relationship function set are matched with target condition values, and different production control parameters in the demand condition impact relationship function are determined in the following manner: Determine the corresponding minimum value based on the single demand parameter range corresponding to different production control parameters; According to the order of the cost consumption comparison from small to large, the production control parameters are adjusted in order to increase their values, and the adjustment is made to ensure that the minimum number of production control parameter values are adjusted to meet the corresponding demand condition influence relationship function to obtain the corresponding target condition value; The target control values of all production control parameters are collected to form the target demand production control reference data.

8. The silica gel production equipment control method according to claim 7, characterized in that: The production control reference data is used to control the production and monitor the production process to form real-time production monitoring information, including: Parameters are set based on the target demand production control reference data, and parameter values are monitored in real time in the following ways: Setting a parameter allowable deviation range, when any real-time parameter value of the target demand production control parameter exceeds the parameter allowable deviation range, an early warning message is generated; Get the real-time raw material feeding amount and calculate the target raw material usage Q aim Perform real-time monitoring of the following methods: If the real-time raw material feeding amount reaches the target raw material usage Q aim , then the feeding termination information is formed.

Images