Silica gel production equipment control method

Abstract

The invention provides a silica gel production equipment control method, and relates to the technical field of silica gel production control. The method comprises the steps of collecting historical production record data of equipment, performing production control analysis based on product demands, and forming demand production control reference data; according to the historical production record data of the equipment, cost analysis based on control adjustment is carried out, and production control adjustment cost reference data is formed; target product demand information is obtained, production control analysis is carried out in combination with the demand production control reference data and the production control adjustment cost reference data, and target demand production control reference data is formed; and performing production control according to the target demand production control reference data, and monitoring the production process to form real-time production monitoring information. According to the method, production control adjustment modes for different silica gel products are established by combining historical production control data of the equipment, and the adaptability, production efficiency and production quality of equipment production are greatly improved.

Description

Technical Field

[0001] The 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, so the current demand for silicone products is very large. In order to ensure sufficient supply of silicone products, silicone production has gradually begun to be automated and intelligent, greatly improving the production efficiency of silicone products.

[0003] At present, there are two types of equipment control for silicone product production. One is manual on-site control, which requires a large manpower cost, especially for large-scale production lines. Of course, production problems may occur due to human factors, affecting production efficiency and quality. The other is intelligent control, which realizes automatic control of production equipment on the production line through the control center. However, there is no systematic method system for how to reasonably and efficiently adjust and control different silicone products to ensure the quality and production efficiency of silicone products.

[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 control method for silicone production equipment, which collects historical production records of production equipment to extract data for setting and adjusting production control parameters for different product needs, and at the same time determines the cost situation generated by the adjustment of production control parameters, and on this basis, reasonably sets production control parameters 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 efficiency of setting production control parameters can be greatly improved, and the accuracy and rationality of setting production control parameters can also be improved, so as to ensure the production efficiency of products while ensuring the quality of products produced, and provide reliable data reference for 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 according to 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 according to 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 parameter settings for different product needs, and at the same time determines the cost situation generated by the adjustment of production control parameters, and on this basis, reasonably sets production control parameters for the target product needs, 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 the target product needs 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, so as to ensure the production efficiency of the products 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.

[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: performing 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; performing 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 relatively accurate amount of raw materials for different types of silicone products, which provides a reference for enterprise production in cost statistics and cost optimization, and can also be used as a reasonable and intuitive reference for whether the equipment has a performance change in terms of materials. The second aspect is the constraint conditions for reasonable parameter 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, and as a reference, it can provide more accurate and reasonable guidance for adjusting and setting production control parameters. The two aspects of data information lay the foundation for providing better production control parameter settings and material determination for the target product needs in the future, making production control more efficient and accurate.

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

[0011] In the present invention, when extracting the relationship information between product demand and materials, it is considered that different silicone products are formed by different molds. The mold can provide the most accurate size and shape information of the product, and the amount of raw materials used in product production is directly related to the size and shape of the product. When the production process does not change, the amount of raw materials and the size and shape of the product have a stable relative relationship, so the relationship between the product and the amount of raw materials can be provided by analyzing the size and shape of the product. Here, for silicone products, the main reason affecting the amount of raw materials is the volume of the product. Secondly, for different parts of the product, different amounts of raw materials will be provided due to the thickness of the formed product. After all, the excess material loss generated by parts of different thicknesses is different. Therefore, it is reasonable and accurate to use the ratio of volume to the average thickness of the product as a parameter to measure the relationship between the raw material usage of the product. A reasonable and accurate material relationship is established by fitting the relationship between the ratio of volume to average thickness, that is, the effective plane area and the actual raw material usage of the product in the historical records.

[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 is 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 performed. First, it is considered that the changes of production control parameters under different effective plane areas may be different. Therefore, when performing the influence relationship analysis, clustering based on the effective plane area must be performed first. In this way, the production control data under the same effective plane area 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, so 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 degree of influence and influence association 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 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 Based on the different product demands in the cluster, the historical effective production control quantities of all production control parameters corresponding to different product demands are clustered based on the changes 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 performed 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 quantity of all corresponding production control influencing parameters Perform relationship fitting to form a demand condition influence 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 influence relationship functions are combined to form a demand condition influence 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 will be affected by multiple production control parameters, and it is not intuitive and reasonable to adjust the production control parameters at the same time to determine how the adjustment affects the product demand. Therefore, when establishing the influence relationship between different product demand conditions and different production control parameters, it is more intuitive and reasonable to adopt a single demand change analysis method. After all, when other demand conditions remain unchanged, only the change of one condition can intuitively determine which production control parameters affect this demand condition, and at the same time, the change data can also be used to establish the influence relationship between the demand condition and the affected multiple production control parameters.

[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, the influence relationship function set: if there is any demand condition influence 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 the influence relationship function of any demand condition 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 range in the two demand condition influence relationship function sets is 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 adjustment of the production control parameters corresponding to the product demand conditions has the same degree of influence and influence method on the product demand conditions. Of course, considering 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 difference range. The range size of the similarity relationship distance difference range can be set as needed, and it can also be determined using big data analysis. After judging 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 is consistent within this range. 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 and then fit all the averaged points. The extreme value fitting is the curve formed by segmenting the minimum or maximum value of the two relationship functions and smoothing them. 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 according to 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 according to the historical production record data of the equipment, and determining the corresponding unit consumption costs according to 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 combining the unit consumption costs corresponding to different production control parameters and the cost consumption comparison order to form the production control adjustment cost reference data.

[0019] In the present invention, the adjustment and control of production control parameters will inevitably cause changes, so cost evaluation of production control parameters can achieve cost control well. Similarly, the production control parameters are sorted in order of unit cost, mainly to provide guidance for sequential adjustment when the control parameters are adjusted for the target product 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 according to the target product demand information, and determining the target plane area S 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 ; 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 the present invention, the target product demand information needs to realize the adjustment and determination of the production control parameters, which requires two aspects: one is to determine the amount of raw materials required for the target product, and the other is to set and adjust the production control parameters. The amount of raw materials can be determined reasonably according to the mold information formed by the target product and the material relationship.

[0022] As a possible implementation method, a production control analysis is performed in combination with a demand condition influence relationship function set, target condition values, and production control adjustment cost reference data to form target demand production control reference data, including: corresponding different demand condition influence relationship functions in the demand condition influence relationship function set to target condition values, and determining different production control parameters in the demand condition influence relationship function in the following manner: determining the corresponding minimum value based on 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 combining the target control values ​​of all production control parameters to form target demand production control reference data.

[0023] In the present invention, different production control parameters can be reasonably determined by using the influence relationship function according to the target demand conditions. Of course, considering that the influence of different production control parameters on costs is different, when 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 cost.

[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 dosage and 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 the 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] The method collects historical production records of production equipment to extract data for adjusting production control parameter settings for different product needs, determines the cost situation generated by the adjustment of production control parameters, and on this basis, reasonably sets production control parameters for the target product needs, 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 the target product needs 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, so as to ensure the production efficiency of the products 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 drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A step diagram of a silica gel production equipment control method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be described below in conjunction with 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, so the current demand for silicone products is very large. In order to ensure sufficient supply of silicone products, silicone production has gradually begun to be automated and intelligent, greatly improving the production efficiency of silicone products.

[0032] At present, there are two types of equipment control for silicone product production. One is manual on-site control, which requires a large manpower cost, especially for large-scale production lines. Of course, production problems may occur due to human factors, affecting production efficiency and quality. The other is intelligent control, which realizes automatic control of production equipment on the production line through the control center. However, there is no systematic method system for how to reasonably and efficiently adjust and control different silicone products to ensure the quality and production efficiency of silicone products.

[0033] refer to Figure 1 The embodiment of the present invention provides a control method for 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 cost saving as much as possible. Compared with directly setting the production control parameters for the target product requirements 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, so as to ensure the production efficiency of the products 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.

[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 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 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 equipment, and forming demand production control parameter influence relationship data; 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 products to be produced. Through material analysis, it is possible to reasonably estimate the amount of raw materials 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 performance changes in terms of materials. The second aspect is to set constraints on reasonable parameter settings for 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 adjusting and setting production control parameters. The two aspects of data information lay the foundation for providing better production control parameter settings and material determination for the target product needs in the future, 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 required production raw material usage reference data, 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 according to 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 required product mold information corresponding to the historical production project numbered n. n represents the average thickness of the required product determined according to the required product mold information corresponding to the historical production project numbered n; extracting different effective plane areas S in the historical production record data of the equipment n The corresponding actual raw material consumption Q n , and perform fitting analysis to determine the material relationship between the actual raw material consumption and the effective plane area Q = F (S).

[0039] When extracting the relationship information between product demand and materials, it is considered that different silicone products are formed by different molds. The mold can provide the most accurate size and shape information of the product, and the amount of raw materials used in the production of the product is directly related to the size and shape of the product. When the production process does not change, the amount of raw materials and the size and shape of the product have a stable relative relationship. Therefore, the relationship between the product and the amount of raw materials can be provided by analyzing the size and shape of the product. Here, for silicone products, the main reason affecting the amount of raw materials is the volume of the product. Secondly, for different parts of the product, different amounts of raw materials will be provided due to the thickness of the formed product. After all, the residual material loss generated by parts of different thicknesses is different. Therefore, it is reasonable and accurate to use the ratio of volume to average thickness of the product as a parameter to measure the relationship between the raw material usage of the product. A reasonable and accurate material relationship is established by fitting the relationship between the ratio of volume to average thickness, that is, the effective plane area and the actual raw material usage of the product in the historical records.

[0040] According to 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 influence relationship data of the demand production control parameters, 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 is 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 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 make a reasonable merger, which greatly reduces the complexity of data analysis and can 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 Based on the different product demands in the cluster, the historical effective production control quantities of all production control parameters corresponding to different product demands are clustered based on the changes 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 performed 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 quantity of all corresponding production control influencing parameters Perform relationship fitting to form a demand condition influence 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 influence relationship functions are combined to form a demand condition influence 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 demand condition will be affected by multiple production control parameters, and it is not intuitive and reasonable to adjust the production control parameters at the same time to determine how the adjustment affects the product demand. Therefore, when establishing the influence relationship between different product demand conditions and different production control parameters, it is more intuitive and reasonable to use a single demand change analysis method. After all, when other demand conditions remain unchanged, only the change of one condition can intuitively determine which production control parameters affect this demand condition, and at the same time, the change data can also be used to establish the influence relationship between the demand condition and the affected multiple production control parameters.

[0044] Conducting a comparative analysis of similar influence relationships on different effective product demand production control parameter influence relationship information to form demand production control parameter influence relationship data, including: conducting 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, the influence relationship function set: if there is any demand condition influence 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 the influence relationship function of any demand condition 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 range in the two demand condition influence relationship function sets is 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] The similarity analysis can reasonably merge different effective plane areas and determine that within a certain effective plane area range, the adjustment of the production control parameters corresponding to the product demand conditions has the same degree and mode of influence on the product demand conditions. Of course, considering 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 similarity, but it can be determined to be similar within a certain difference range. The range of the similarity relationship distance difference can be set as needed, or it can be determined using big data analysis. After judging that the influence relationship function sets under two different effective plane area ranges are similar, reasonable merging can be performed. The merging process includes three aspects. One is to define the scope of the effective plane area, using the effective plane areas corresponding to the two influence relationship function sets as the boundary values ​​to form the corresponding range, indicating that the influence relationship between the product demand conditions and different production control parameters is consistent within this range. 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 and then fit all the averaged points. The extreme value fitting is the curve formed by segmenting the minimum or maximum value of the two relationship functions and smoothing them. 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 adjustment according to the historical production record data of the equipment to form reference data for production control adjustment costs.

[0047] According to 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 according to the historical production record data of the equipment, and determining the corresponding unit consumption costs according to 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 combining 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] Adjustment of production control parameters will inevitably cause changes, so cost evaluation of production control parameters can achieve cost control well. Similarly, sorting production control parameters in order of unit cost is mainly to provide guidance for sequential adjustment when adjusting control parameters for target products and to provide a sequential reference for data adjustment 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 the target product demand information, and perform production control analysis in combination with the demand production control reference data and the production control adjustment cost reference data to form the target demand production control reference data, including: determining the target mold information of the target product according to the target product demand information, and determining the target plane area S 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 ; 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 the production control parameters, the target product demand information needs to achieve two aspects: one is to determine the amount of raw materials required for the target product, and the other is to set and adjust the production control parameters. The amount of raw materials can be determined reasonably based on the mold information formed by the target product and the material relationship.

[0052] Combined with the demand condition impact relationship function set, the target condition value and the production control adjustment cost reference data, production control analysis is performed to form the target demand production control reference data, including: corresponding different demand condition impact relationship functions in the demand condition impact relationship function set to the target condition value, and determining the different production control parameters in the demand condition impact 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 sequence 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 impact relationship function to obtain the corresponding target condition value; and combining the target control values ​​of all production control parameters to form the target demand production control reference data.

[0053] According to the target demand conditions, the influence relationship function can be used to reasonably determine different production control parameters. Of course, considering that different production control parameters have different effects on costs, when 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 parameters with lower unit costs to ensure reasonable control costs.

[0054] S4: Perform production control based on target demand production control reference data, monitor the production process, and form 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 according to 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 dosage 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] The method collects historical production records of production equipment to extract data for adjusting production control parameter settings for different product needs, determines the cost situation generated by the adjustment of production control parameters, and on this basis, reasonably sets production control parameters for the target product needs, 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 the target product needs 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, so as to ensure the production efficiency of the products 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 embodiments 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 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 (for example, specified by 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 may 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 refer 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, different indication methods may be used for different information. In the specific implementation process, the desired 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 embodiment of the present application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to the protocol, or can be configured by the sending end device by sending configuration information to the receiving end 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, which is not limited by the embodiments of the present application.

[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, and 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 make corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to have a judgment action when implementing it, 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 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, where 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 its similar expression refers 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, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate 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 the difference. 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 designs. 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. A general-purpose processor may be a microprocessor or the 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 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 programs 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 site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access 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 hard disk.

[0069] It should be understood that the term "and / or" in this article is only a description of the association relationship of 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, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0070] In this application, "at least one" means one or more, and "more than one" 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 be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[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 of ordinary skill 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 to be beyond 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 aforementioned method embodiments and will not be repeated here.

[0074] In the several embodiments provided in the present 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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 separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[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 can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0078] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope 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 adjustment according to 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; Production control is performed based on the target demand production control reference data, and the production process is monitored to form real-time production monitoring information.

2. The silica gel production equipment control method according to claim 1, characterized in that: The collecting of equipment historical production record data, performing production control analysis based on product demand, and forming demand production control reference data include: According to the historical production record data of the equipment, raw material usage analysis based on product demand is performed to form reference data of required production raw material usage; According to the historical production record data of the equipment, the influence analysis of the control parameters based on the product demand is carried out to form the demand-production control parameter influence 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.

3. The silica gel production equipment control method according to claim 2, characterized in that: The method of analyzing the raw material usage based on product demand according to the historical production record data of the equipment to form reference data of the raw material usage for production demand includes: Extract the required product mold information for different historical production items in the historical production record data of the equipment, and determine the corresponding effective plane area S according to 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 required product mold information corresponding to the historical production project numbered n. n It represents the average thickness of the required product determined according to 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 between the actual raw material consumption and the effective plane area Q = F (S).

4. The silica gel production equipment control method according to claim 3, characterized in that: The step of performing control parameter influence analysis based on product demand according to the equipment historical production record data to form demand-production control parameter influence relationship data includes: 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 is clustered to form a historical effective production control data set A k , k represents the serial 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 for product demand to form effective product demand production control parameter impact relationship information; A comparative analysis of similar impact relationships is performed on different information on the impact relationships of the effective product demand production control parameters 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 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, including: For the historical effective production control data set A k , determine all production control parameters and the historical effective production control quantities corresponding to the different production control parameters; For the historical effective production control data set A k According to different product demands, the historical effective production control quantities of all production control parameters corresponding to different product demands are clustered based on the change of a single demand condition to form a production control quantity set with a single demand condition change; 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 after a single demand condition changes, and determine them as the production control influencing 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 influence relationship function Wherein, 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; 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.

6. The silica gel production equipment control method according to claim 5, 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 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 above demand conditions satisfies 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; The new demand condition impact relationship function set formed after all 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.

7. The silica gel production equipment control method according to claim 6, 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 according to the historical production record data of the equipment, and determining corresponding unit consumption costs according to the energy consumption values; Sorting the production control parameters according to the order of 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.

8. The silica gel production equipment control method according to claim 7, 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 include: 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 ​​according to the target product demand information; According to the target plane area S aim , determine 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.

9. The silica gel production equipment control method according to claim 8, 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 influence relationship functions in the demand condition influence relationship function set are matched to the target condition value, and different production control parameters in the demand condition influence relationship function are determined in the following manner: According to the single demand parameter range corresponding to different production control parameters, the corresponding minimum value is determined; According to the order of the cost consumption comparison from small to large, the production control parameters are adjusted in order to increase the value, 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.

10. The silica gel production equipment control method according to claim 9, characterized in that: The 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: Parameters are set according to the target demand production control reference data, and parameter values ​​are monitored in real time 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, generating an early warning message; Get the real-time raw material feeding amount, and according to the target raw material usage Q aim 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.

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