A MES intelligent production control method based on cloud computing

Abstract

The present application relates to the field of production control technology, and in particular to a method for intelligent production control of MES based on cloud computing. The method includes: obtaining a real-time production data set of organic chemical products, analyzing the real-time production data set, and determining the production bottleneck link; based on the production bottleneck link, analyzing the real-time production data set, and determining a number of factors that can be optimized; according to a number of factors that can be optimized, simulating the production process of organic chemical products, and determining the adjustment results; according to the adjustment results, adjusting the real-time production data set, and determining the bottleneck optimization data set; according to the bottleneck optimization data set, controlling the production equipment to make corresponding adjustments. By analyzing the real-time production data set and controlling the production equipment to make corresponding adjustments, the present application can flexibly respond to the many variables in the organic chemical production process, make timely and precise adjustments to the production equipment, ensure the quality of organic chemical products, and reduce the probability of safety accidents.

Description

Technical Field

[0001] The present application relates to the field of production control technology, and in particular to a MES intelligent production control method based on cloud computing. Background Art

[0002] With the development of industrial informatization, the use of MES (Manufacturing Execution System) to manage and monitor industrial production processes can improve production efficiency and product quality, and help enterprises achieve the goals of lean production and information-based intelligent manufacturing.

[0003] When the existing MES system manages and monitors the production process of organic chemical products, due to the complexity of the production process of organic chemical products and the frequent dynamic changes of products during the production process, the existing MES system is difficult to flexibly respond to the many variables in the production process of organic chemical products, resulting in uneven quality of organic chemical products and even safety accidents. Summary of the invention

[0004] The present application provides a MES intelligent production control method based on cloud computing to solve the above technical problems.

[0005] In a first aspect, the present application provides a MES intelligent production control method based on cloud computing, the method comprising:

[0006] Acquire a real-time production data set of an organic chemical product, analyze the real-time production data set, and determine a production bottleneck;

[0007] Based on the production bottleneck, analyzing the real-time production data set to determine a number of optimizable factors;

[0008] According to the several optimizable factors, a simulation is performed on the production process of the organic chemical product to determine an adjustment result;

[0009] According to the adjustment result, adjusting the real-time production data set to determine the bottleneck optimization data set;

[0010] According to the bottleneck optimization data set, the production equipment is controlled to make corresponding adjustments.

[0011] The present application determines the production bottleneck link that causes uneven quality of organic chemical products by analyzing the real-time production data set in the production process of organic chemical products, and determines several optimizable factors in the production bottleneck link by analyzing the real-time production data set. On the basis of several optimizable factors, the application determines the adjustment result by simulating the production process of organic chemical products, and adjusts the real-time production data set according to the adjustment result to obtain the bottleneck optimization data set. According to the bottleneck optimization data set, the production equipment is controlled to make corresponding adjustments, so as to flexibly respond to many variables in the organic chemical production process, make timely and accurate adjustments to the production equipment, ensure the quality of organic chemical products, and reduce the probability of safety accidents.

[0012] Optionally, analyzing the real-time production data set to determine the production bottleneck includes:

[0013] According to the preset total analysis time and the preset separation time, time smoothing processing is performed on each real-time production data in the real-time production data set to determine the low-volatility smoothed data corresponding to each real-time production data under the preset analysis time;

[0014] Based on the low-volatility smoothed data and according to the real-time production data set, determining a volatility index of each real-time production data under the preset total analysis time;

[0015] According to the fluctuation index of each real-time production data, determining the relationship fluctuation index between each real-time production data and other real-time production data;

[0016] The relationship fluctuation index of each real-time production data is analyzed to determine the production bottleneck link.

[0017] Through the above technical scheme, according to the preset total analysis time and the preset analysis time, each real-time production data is time-smoothed to obtain low-volatility smoothed data that is easier to parse and process, and on the basis of the low-volatility smoothed data, a relationship volatility index is determined that can reflect the actual volatility of the current real-time production data under the influence of different real-time production data, and the production bottleneck link is determined based on the relationship volatility index. While improving the efficiency of the mathematical analysis process, the relationship volatility index can more comprehensively reflect the volatility of the real-time production data and improve the accuracy of the production bottleneck link.

[0018] Optionally, according to the preset total analysis time and the preset separation time, time smoothing processing is performed on each real-time production data in the real-time production data set to determine the low-volatility smoothed data corresponding to each real-time production data under the preset analysis time, referring to the following formula:

[0019]

[0020] Among them, t is the preset total analysis time, LD i,t is the low-volatility smoothed data of the ith real-time production data in the real-time production data set under the preset analysis time t, N is the preset separation time, X i,k It is the specific data corresponding to the i-th real-time production data at time node k.

[0021] Through the above technical solution, mathematical analysis methods are used to design a mathematical formula to perform uniform time smoothing on each real-time production data on the basis of a preset total analysis time and a preset separation time, so as to obtain the low-volatility smoothed data corresponding to each real-time production data under the preset analysis time, thereby reducing the instability of the real-time production data, reducing the sensitivity of the corresponding mathematical formula to noise when performing subsequent mathematical analysis on the low-volatility smoothed data, and improving the accuracy of the subsequent volatility index.

[0022] Optionally, based on the low-volatility smoothed data, according to the real-time production data set, a volatility index of each real-time production data in the preset total analysis time is determined, referring to the following formula:

[0023]

[0024] Among them, t is the preset total analysis time, σ i,t is the volatility index of the ith real-time production data under the preset total analysis time t, N is the preset separation time, X i,j is the specific value corresponding to the i-th real-time production data at time node j, LD i,t It is the low-fluctuation smoothed data of the i-th real-time production data in the preset analysis time t.

[0025] Through the above technical solution, mathematical analysis methods are used to design mathematical formulas to quantify the volatility of each real-time production data on the basis of low-volatility smooth data, and a volatility index is obtained. The volatility index can comprehensively and intuitively reflect the changes in the real-time production data on the time axis corresponding to the entire preset analysis time, so as to improve the accuracy and comprehensiveness of the volatility index.

[0026] Optionally, the relationship fluctuation index of each real-time production data is determined according to the fluctuation index of each real-time production data, referring to the following formula:

[0027]

[0028] Among them, σ i ′ ,t is the relationship fluctuation index of the ith real-time production data under the preset total analysis time t, σ i,tis the volatility index of the ith real-time production data under the preset total analysis time t, C ij is the preset data relationship matrix, σ j,t is the fluctuation index of the jth real-time production data under the preset total analysis time t; M represents the amount of the real-time production data;

[0029] Through the above technical solution, on the basis of each real-time production data fluctuation index, mathematical analysis methods are used to design mathematical formulas, comprehensively consider the impact of other real-time production data on the current real-time production data, quantify the relationship fluctuation index, and make the relationship fluctuation index more comprehensively reflect the fluctuation of the real-time production data, thereby making the subsequent production bottleneck link derived based on the relationship fluctuation index more accurate.

[0030] Optionally, analyzing the relationship fluctuation index of each real-time production number to determine the production bottleneck link includes:

[0031] According to the preset relationship fluctuation threshold, refer to the following formula to determine the location set of several abnormal relationship fluctuation indexes in the real-time production data set:

[0032]

[0033] Where S is the location set, I i ′ ,t is the indicator variable, σ i ′ ,t is the relationship fluctuation index of the i-th real-time production data under the preset total analysis time t, and T is the preset relationship fluctuation threshold;

[0034] Analyzing the real-time production data set according to the location set to determine a number of abnormal production data;

[0035] The production bottleneck link is determined according to the plurality of abnormal production data.

[0036] Through the above technical solution, according to the production bottleneck link, the real-time production data set is analyzed to determine a number of bottleneck production data related to the production bottleneck link; the several bottleneck production data are analyzed to determine the sensitivity of each bottleneck production data to a number of abnormal production data; based on the sensitivity of each bottleneck production data to the several abnormal production data and the preset sensitivity threshold, the several optimizable factors are determined.

[0037] Optionally, based on the production bottleneck link, the real-time production data set is analyzed to determine a number of optimizable factors, including:

[0038] According to the production bottleneck link, analyzing the real-time production data set, determining a number of bottleneck production data related to the production bottleneck link;

[0039] Analyze the plurality of bottleneck production data to determine the sensitivity of each bottleneck production data to the plurality of abnormal production data;

[0040] The plurality of optimizable factors are determined according to the sensitivity of each bottleneck production data to the plurality of abnormal production data and a preset sensitivity threshold.

[0041] Through the above technical solution, based on the production bottleneck link, a number of bottleneck production data are analyzed, a number of bottleneck production data are determined, and by analyzing a number of bottleneck production data, the sensitivity of each bottleneck production data to a number of abnormal production data is determined, and according to the sensitivity and the preset sensitivity threshold, a number of optimizable factors are determined. By introducing sensitivity, a number of bottleneck production data are screened, and a number of bottleneck production data with the highest necessity for optimization are determined, and used as a number of optimizable factors, so as to improve the subsequent adjustment efficiency of the production process, and help to accurately control the adjustment results of the subsequent production process.

[0042] Optionally, the analyzing the plurality of bottleneck production data to determine the sensitivity of each bottleneck production data to the plurality of abnormal production data may be performed with reference to the following formula:

[0043]

[0044] Among them, Y j is the sensitivity, β0 is the preset intercept term, ∈ is the preset disturbance term, β a Produce data for the current bottleneck, X j is the jth abnormal production data, F j is a preset relationship index between the jth abnormal production data and the current bottleneck production data, and n is the total number of the abnormal production data.

[0045] Through the above technical scheme, mathematical analysis methods are used to design a mathematical formula on the basis of several bottleneck production data to perform regression analysis on the sensitivity of each bottleneck production data to the several abnormal production data. The preset relationship index is used to reflect the closeness of the relationship between the current bottleneck production data and the current abnormal production data, and the sensitivity error is reduced through the disturbance term. Finally, the sensitivity of each bottleneck production data to the several abnormal production data is obtained, thereby improving the accuracy and comprehensiveness of the sensitivity.

[0046] Optionally, simulating the production process of the organic chemical product according to the plurality of optimizable factors to determine the adjustment result includes:

[0047] Simulating a real-time production process according to preset production process information and the real-time production data set;

[0048] According to the plurality of optimizable factors, the real-time production process is adjusted to simulate the adjusted production process;

[0049] The adjusted production process is analyzed, and a number of production data in the adjusted production process are determined as adjustment results.

[0050] Through the above technical scheme, the real-time production process is simulated according to the preset production process information and the real-time production data set, and the real-time production process is adjusted according to a number of optimizable factors. The adjusted production process is analyzed, and a number of production data corresponding to the adjusted production process are determined as the adjustment result, thereby improving the accuracy of the adjustment result, avoiding irreversible consequences caused by direct adjustments to the actual production process, and providing an intuitive data basis for subsequent adjustments to the actual production process.

[0051] Optionally, the method further includes:

[0052] According to the real-time production data set and the adjustment result, the data of each time node of the real-time production data and each production data in the adjustment result at the analysis time after being separated by the preset separation time length are recorded to form a dynamic data set;

[0053] Based on the dynamic data set, visual dynamic production data is determined and output for viewing by professionals.

[0054] Through the above technical solution, the real-time production data set and adjustment results are analyzed, the dynamic data set is determined, and the dynamic data set is visualized through data visualization technology to determine the visualized dynamic production data. The visualized dynamic production data is provided for analysis by professionals, so that professionals can understand the changes in production process data, and provide intuitive and comprehensive data for professionals to improve the organic chemical production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0056] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0057] Figure 2A flowchart of a MES intelligent production control method based on cloud computing is provided in one embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] In addition, 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. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0060] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0061] When the existing MES method is used to manage and monitor the production process of organic chemical products, due to the complexity of the production process of organic chemical products and the frequent dynamic changes of the products during the production process, the existing MES method is difficult to flexibly respond to the many variables in the production process of organic chemical products, resulting in uneven quality of organic chemical products and even safety accidents.

[0062] Based on this, the present application provides a MES intelligent production control method based on cloud computing, which analyzes the real-time production data set in the production process of organic chemical products, determines the production bottleneck link that causes the uneven quality of organic chemical products, and determines several optimizable factors in the production bottleneck link by analyzing the real-time production data set. On the basis of several optimizable factors, the adjustment result is determined by simulating the production process of organic chemical products, and the real-time production data set is adjusted according to the adjustment result to obtain the bottleneck optimization data set. According to the bottleneck optimization data set, the production equipment is controlled to make corresponding adjustments, so as to flexibly respond to the many variables in the organic chemical production process, and timely and accurately adjust the production equipment to ensure the quality of organic chemical products and reduce the probability of safety accidents.

[0063] Figure 1 A schematic diagram of an application scenario provided in the present application. In the production process of organic chemical products, the method provided in the present application is applied to analyze real-time production data and control the production equipment to make corresponding adjustments.

[0064] Specifically, the method provided in the present application is installed on any server, and the server communicates with the production equipment, obtains the real-time production data set provided by the production equipment through the equipment, analyzes the real-time production data set, determines the production bottleneck link that causes the uneven quality of organic chemical products, and determines several optimizable factors in the production bottleneck link by analyzing the real-time production data set. On the basis of several optimizable factors, the adjustment result is determined by simulating the production process of organic chemical products, and the real-time production data set is adjusted according to the adjustment result to obtain the bottleneck optimization data set. According to the bottleneck optimization data set, the production equipment is controlled to make corresponding adjustments, so as to flexibly respond to the many variables in the organic chemical production process, make timely and accurate adjustments to the production equipment, ensure the quality of organic chemical products, and reduce the probability of safety accidents.

[0065] For specific implementation methods, please refer to the following embodiments.

[0066] Figure 2 This is a flowchart of a cloud computing-based MES intelligent production control method provided in one embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes:

[0067] S201. Obtain a real-time production data set of organic chemical products, analyze the real-time production data set, and determine the production bottleneck link.

[0068] Organic chemical products can be chemical products based on organic compounds and produced through chemical reactions and processing.

[0069] Real-time production data can be a series of real-time parameters of the chemical reaction stage of the current organic chemical product production process, such as temperature, reaction pressure and pH value. Real-time production data can be obtained through various sensors arranged in the organic chemical product production line.

[0070] The production bottleneck link may be the link in the current production process of organic chemical products that has a negative impact on the final quality of organic chemical products.

[0071] Specifically, since the production process of organic chemical products involves complex chemical reactions and the various raw materials of the products are extremely sensitive to the reaction conditions during the reaction process, when the reaction conditions are abnormal, the quality of organic chemical products often fails in batches. Moreover, since organic chemical products are continuously in multi-stage chemical reactions in the production time series, when batches of products fail, it is difficult to locate and adjust the problematic links in a timely manner, resulting in uneven quality of organic chemical products and the inability to ensure a high yield rate.

[0072] The real-time production data in the production process of organic chemical products is analyzed through mathematical analysis to quantify the change status of each production data in the same production time series, thereby locating the abnormal bottleneck links and providing basic data for subsequent adjustments to the production process.

[0073] S202. Based on the production bottleneck, analyze the real-time production data set and determine several factors that can be optimized.

[0074] The optimizable factors may be the corresponding production data that needs to be adjusted in the bottleneck link.

[0075] Specifically, since each production link involves a series of production data, and a production link is determined as a production bottleneck link often due to the anomaly caused by the mutual influence between multiple production data, therefore, after determining the production bottleneck link, on the basis of the production bottleneck link, through mathematical analysis means, quantify the impact of production data in the real-time production data set on the production bottleneck link, so as to determine the corresponding several optimizable factors in the production bottleneck link, and provide a basis for the subsequent adjustment of organic chemical products.

[0076] S203. Based on a number of optimizable factors, simulate the production process of organic chemical products and determine adjustment results.

[0077] The adjustment result may be the adjustment values ​​of the corresponding optimizable factors obtained by optimizing the optimizable factors and simulating the production process of the organic chemical product.

[0078] Specifically, since organic chemical products are very sensitive to reaction conditions in the production process, after determining a number of optimizable factors, if the optimizable factors are directly adjusted, it is difficult to ensure real-time production effects. Therefore, based on a number of optimizable factors, the production process of organic chemical products is simulated through process flow simulation software, such as Aspen Plus (Advanced System for Process Engineering), to determine the values ​​of a number of optimizable factors that need to be adjusted, so as to provide accurate data for subsequent adjustments to the actual production process of organic chemical products.

[0079] S204. According to the adjustment result, adjust the real-time production data set and determine the bottleneck optimization data set.

[0080] The bottleneck optimization data set may be a set including all adjusted and unadjusted production data after the real-time production data set is adjusted according to the adjustment result.

[0081] Specifically, according to the adjustment values ​​corresponding to several optimizable factors in the adjustment results, the production data in the real-time production data set is adjusted to obtain a bottleneck optimization data set containing all adjusted and unadjusted production data, providing a data basis for the subsequent accurate adjustment of the control entity production equipment.

[0082] S205. Optimize the data set according to the bottleneck and control the production equipment to make corresponding adjustments.

[0083] Production equipment can be a series of physical equipment on a production line for producing organic chemical products.

[0084] Specifically, after determining the bottleneck optimization data set, the cloud computing platform communicates with the physical equipment, and the production equipment is remotely controlled according to the bottleneck optimization data set, so that the production equipment can make corresponding adjustments in real time to ensure the product qualification rate.

[0085] Through the method provided in this embodiment, the real-time production data set in the production process of organic chemical products is analyzed to determine the production bottleneck link that causes uneven quality of organic chemical products, and by analyzing the real-time production data set, several optimizable factors in the production bottleneck link are determined. On the basis of the several optimizable factors, the adjustment result is determined by simulating the production process of organic chemical products, and the real-time production data set is adjusted according to the adjustment result to obtain the bottleneck optimization data set. According to the bottleneck optimization data set, the production equipment is controlled to make corresponding adjustments, so as to flexibly respond to many variables in the organic chemical production process, and to make precise adjustments to the production equipment in a timely manner, so as to ensure the quality of organic chemical products and reduce the probability of safety accidents.

[0086] In some embodiments, time smoothing is performed on each real-time production data in the real-time production data set according to a preset total analysis time and a preset separation time, and low volatility smoothed data corresponding to each real-time production data under the preset analysis time is determined; based on the low volatility smoothed data, a volatility index of each real-time production data under the preset total analysis time is determined according to the real-time production data set; based on the volatility index of each real-time production data, a relationship volatility index between each real-time production data and other real-time production data is determined; and the relationship volatility index of each real-time production data is analyzed to determine the production bottleneck link.

[0087] The preset total analysis time may be a preset analysis time for real-time production data, and the preset total analysis time may be set according to actual production conditions.

[0088] The preset separation time length may be a division of a preset total analysis time length to determine the time length information of different time nodes.

[0089] Low-volatility smoothed data can be data that can clearly reflect the changing trend of real-time production data.

[0090] The volatility index may be data reflecting the severity of the fluctuation of real-time production data.

[0091] The relationship fluctuation index may be data indicating the degree to which the fluctuation of current real-time production data is affected by the fluctuation of other real-time production data.

[0092] Specifically, since the directly acquired production data set contains a large number of different types of production data, these production data are affected differently, and the measurement standards of the fluctuation amplitude are also very different, it is difficult to perform a unified mathematical analysis. The preset analysis time is divided into several time nodes by a preset separation time, and the production data corresponding to each time node in the real-time production data set is time-smoothed to obtain low-volatility smoothed data. While retaining the fluctuation characteristics of the production data, the noise in the production data is eliminated, and the sensitivity of subsequent mathematical analysis to noise is reduced. While reflecting the changing trend of the real-time production data, the low-volatility smoothed data is easier to parse and process, which helps to improve the efficiency of subsequent mathematical analysis.

[0093] After obtaining the low-volatility smooth data, mathematical analysis is used to use the quantitatively obtained volatility index to reflect the volatility of the current real-time volatility data on the time axis corresponding to the preset analysis time. At the same time, since different production data are not independent of each other, different production data will affect each other in the production process of organic chemical products. The volatility of a real-time production data needs to consider the impact of other real-time production data on it on the basis of the volatility index. Mathematical analysis is used on the basis of the volatility index to use the quantitatively obtained relationship volatility index to reflect the actual volatility of the current real-time production data under the influence of different real-time production data, and the corresponding production bottleneck link is determined by analyzing the abnormal fluctuations in the relationship volatility index.

[0094] Through the method of this embodiment, time smoothing is performed on each real-time production data according to the preset total analysis time and the preset analysis time to obtain low-volatility smoothed data that is easier to parse and process, and on the basis of the low-volatility smoothed data, a relationship fluctuation index is determined that can reflect the actual fluctuation of the current real-time production data under the influence of different real-time production data, and the production bottleneck link is determined based on the relationship fluctuation index. While improving the efficiency of the mathematical analysis process, the relationship fluctuation index can more comprehensively reflect the fluctuation of the real-time production data and improve the accuracy of the production bottleneck link.

[0095] In some embodiments, according to the preset total analysis time and the preset separation time, time smoothing processing is performed on each real-time production data set to determine the low-volatility smoothed data corresponding to each real-time production data under the preset analysis time, referring to formula (1):

[0096]

[0097] Among them, t is the preset total analysis time, LD i,t is the low-volatility smoothed data of the ith real-time production data in the real-time production data set under the preset analysis time t, N is the preset separation time, X i,k It is the specific data corresponding to the i-th real-time production data at time node k.

[0098] Specifically, the real-time production data values ​​at nearly N time nodes are uniformly smoothed by formula (1), and the low-volatility smoothed value of each real-time production data under the preset total analysis time is obtained, thereby reducing the instability of the real-time production data.

[0099] Through the method provided in this embodiment, mathematical analysis methods are used to design a mathematical formula to perform uniform time smoothing on each real-time production data on the basis of a preset total analysis time and a preset separation time, so as to obtain the low-volatility smoothed data corresponding to each real-time production data under the preset analysis time, thereby reducing the instability of the real-time production data, reducing the sensitivity of the corresponding mathematical formula to noise when performing subsequent mathematical analysis on the low-volatility smoothed data, and improving the accuracy of the volatility index obtained subsequently.

[0100] In some embodiments, based on the low-volatility smoothed data and according to the real-time production data set, a volatility index of each real-time production data under the preset total analysis time is determined, referring to formula (2):

[0101]

[0102] Among them, t is the preset total analysis time, σ i,t is the volatility index of the ith real-time production data under the preset total analysis time t, N is the preset separation time, X i,j is the specific value corresponding to the i-th real-time production data at time node j, LD i,t It is the low-fluctuation smoothed data of the i-th real-time production data in the preset analysis time t.

[0103] Specifically, by using X in formula (2) i,j -LD i,t Quantify the difference between the specific data of each real-time production data and the corresponding low-volatility smoothed data, and then reflect the volatility of each real-time production data through the standard deviation between the specific data of each real-time production data and the corresponding low-volatility smoothed data, and derive the volatility index, so that the volatility index can comprehensively and intuitively reflect the changes of the real-time production data on the time axis corresponding to the entire preset analysis time.

[0104] Through the method provided in this embodiment, mathematical analysis methods are used to design a mathematical formula to quantify the volatility of each real-time production data on the basis of low-volatility smoothed data, and a volatility index is obtained. The volatility index can comprehensively and intuitively reflect the changes in the real-time production data on the time axis corresponding to the entire preset analysis time, so as to improve the accuracy and comprehensiveness of the volatility index.

[0105] In some embodiments, the relationship fluctuation index of each real-time production data is determined according to the fluctuation index of each real-time production data, referring to formula (3):

[0106]

[0107] Among them, σ i ′ ,t is the relationship fluctuation index of the ith real-time production data under the preset total analysis time t, σ i,t is the volatility index of the ith real-time production data under the preset total analysis time t, C ij is the relationship coefficient between the i-th real-time production data and the j-th real-time production data in the preset data relationship matrix, σ j,t is the fluctuation index of the jth real-time production data under the preset total analysis time t, and M is the number of real-time production data in the real-time production data; M represents the number of real-time production data;

[0108] The preset relationship data matrix may be a preset symmetric matrix for storing relationship coefficients between any two real-time production data. The diagonal elements in the preset relationship data matrix are 0. The preset relationship data matrix may be obtained by analyzing historical data.

[0109] Specifically, based on the volatility index of each real-time production data, the influence of other real-time production data on the current real-time production data is taken into consideration through formula (3). The weighted sum of the volatility of the current real-time production data is calculated, and the relationship volatility index is obtained by adding the weighted sum of the volatility and the volatility index and comprehensively considering the impact of other real-time production data on the current real-time production data.

[0110] Through the method provided in this embodiment, on the basis of each real-time production data fluctuation index, a mathematical formula is designed through mathematical analysis means, and the influence of other real-time production data on the current real-time production data is comprehensively considered to quantify the relationship fluctuation index, so that the relationship fluctuation index can more comprehensively reflect the fluctuation of the real-time production data, thereby making the subsequent production bottleneck link derived based on the relationship fluctuation index more accurate.

[0111] In some embodiments, according to a preset relationship fluctuation threshold, referring to formula (4), a location set of several abnormal relationship fluctuation indexes in a real-time production data set is determined:

[0112]

[0113] Among them, S is the location set, I i ′ ,t is the indicator variable, σ i ′ ,t is the relationship fluctuation index of the i-th real-time production data under the preset total analysis time t, and T is the preset relationship fluctuation threshold; according to the location set, the real-time production data set is analyzed to determine several abnormal production data; according to several abnormal production data, the production bottleneck link is determined.

[0114] The preset relationship fluctuation threshold may be preset threshold data for determining whether the relationship fluctuation index is abnormal. The preset relationship fluctuation threshold may be obtained by analyzing historical data.

[0115] The abnormal relationship fluctuation index may be abnormal data that is greater than a preset relationship fluctuation threshold value among the current plurality of relationship fluctuation indexes.

[0116] The position set may be a set of position subscripts of real-time production data corresponding to a number of abnormal relationship fluctuation indexes in the real-time production data set.

[0117] Specifically, through formula (4) It is used to judge whether the relationship fluctuation index is abnormal, and to assign values ​​to the indicator variables, and then to calculate the relationship fluctuation index through i|I i ′ ,t =1 to screen the indicator variables, extract the position subscripts of the real-time production data corresponding to the abnormal relationship fluctuation index, and integrate several position subscripts to obtain a position set, locate the corresponding real-time production data in the real-time production data set through the position set, and obtain several abnormal production data, and according to the several abnormal production data, on the basis of the production process of organic chemical products, determine several production links corresponding to the several abnormal production data, and determine the production links with the highest priority in the execution order in several production environments as production bottleneck links.

[0118] Through the method provided in this embodiment, mathematical analysis methods are used to design a mathematical formula to determine whether there is an abnormality in the relationship fluctuation index based on the preset abnormal relationship fluctuation index, and the position subscript of the real-time production data corresponding to the abnormal relationship fluctuation index is extracted by assigning and screening the indicator variables, and several position subscripts are integrated to obtain a position set, and the production bottleneck link is located through several abnormal production data corresponding to the position set, making the production bottleneck link positioning process faster and more accurate.

[0119] In some embodiments, based on the production bottleneck link, the real-time production data set is analyzed to determine a number of bottleneck production data related to the production bottleneck link; the number of bottleneck production data are analyzed to determine the sensitivity of each bottleneck production data to a number of abnormal production data; based on the sensitivity of each bottleneck production data to the number of abnormal production data and a preset sensitivity threshold, the number of optimizable factors are determined.

[0120] The bottleneck production data may be a number of real-time production data that are highly relevant to the production bottleneck.

[0121] The sensitivity may be data used to indicate the degree to which the current bottleneck production data is affected by a number of abnormal production data.

[0122] The preset sensitivity threshold may be threshold data used to determine whether adjusting the current bottleneck production data will produce a significant effect on a number of abnormal production data. The preset sensitivity threshold may be obtained by analyzing historical data.

[0123] Specifically, in the production process of organic chemical products, there is a series of real-time production data that are highly correlated with it in one link. These real-time production data have an impact on each other, but these real-time production data cannot be directly adjusted. On the one hand, the adjustment efficiency is low, and on the other hand, it is difficult to stably control the adjustment results by directly adjusting the above real-time production data in batches. Therefore, according to the production bottleneck link, based on the production process of organic chemical products, the real-time production data set is analyzed, and several bottleneck production data corresponding to the production bottleneck link are determined. Through mathematical analysis, the several bottleneck production data are analyzed to determine the sensitivity of each bottleneck production data to the several abnormal production data. When the sensitivity of several bottleneck production data is greater than the preset sensitivity threshold, it means that the adjustment of these bottleneck production data can effectively affect several abnormal production data, and these bottleneck production data are determined as several optimizable factors.

[0124] Through the method provided in this embodiment, based on the production bottleneck link, a number of bottleneck production data are analyzed, a number of bottleneck production data are determined, and by analyzing the number of bottleneck production data, the sensitivity of each bottleneck production data to a number of abnormal production data is determined, and according to the sensitivity and the preset sensitivity threshold, a number of optimizable factors are determined, and by introducing sensitivity, a number of bottleneck production data are screened, and a number of bottleneck production data with the highest necessity for optimization are determined, and used as a number of optimizable factors, so as to improve the subsequent adjustment efficiency of the production process, and help to accurately control the adjustment results of the subsequent production process.

[0125] In some embodiments, a plurality of bottleneck production data are analyzed to determine the sensitivity of each bottleneck production data to a plurality of abnormal production data, referring to formula (5):

[0126]

[0127] Among them, Y j is the sensitivity, β0 is the preset intercept term, ∈ is the preset disturbance term, β a Produce data for the current bottleneck, X j is the jth abnormal production data, F j is the preset relationship index between the jth abnormal production data and the current bottleneck production data, and n is the total number of several abnormal production data.

[0128] The preset intercept term may be a constant term used to represent a sensitivity reference value, and the preset intercept term may be obtained by analyzing historical data.

[0129] The preset disturbance term may be a constant term used to represent an average calculation error of the sensitivity, and the preset disturbance term may be obtained by analyzing historical data.

[0130] The preset relationship index may be data used to express the degree to which bottleneck production data is affected by corresponding abnormal production data, and the preset relationship index may be obtained by analyzing historical data.

[0131] Specifically, based on several bottleneck production data, the sensitivity is regressed and analyzed by formula (5). The baseline value of the result of formula (5) is limited by a preset intercept term to prevent the sensitivity from deviating from the normal range. The preset relationship index is used to reflect the closeness of the relationship between the current bottleneck production data and the current abnormal production data. The sensitivity error is reduced by the disturbance term, and finally the sensitivity of each bottleneck production data to several abnormal production data is obtained.

[0132] Through the method provided in this embodiment, mathematical analysis methods are used to design a mathematical formula to perform regression analysis on the sensitivity of each bottleneck production data to the several abnormal production data on the basis of several bottleneck production data. The preset relationship index is used to reflect the closeness of the relationship between the current bottleneck production data and the current abnormal production data, and the sensitivity error is reduced through the disturbance term. Finally, the sensitivity of each bottleneck production data to the several abnormal production data is obtained, thereby improving the accuracy and comprehensiveness of the sensitivity.

[0133] In some embodiments, a real-time production process is simulated based on preset production process information and a real-time production data set; the real-time production process is adjusted based on a number of optimizable factors, and the adjusted production process is simulated; the adjusted production process is analyzed, and a number of production data in the adjusted production process are determined as adjustment results.

[0134] The preset production process flow information may be the production process flow of current organic chemical products, and the preset production process flow information may be provided by professional staff.

[0135] The real-time production process may be a virtual real-time production process simulated based on the real-time production data set.

[0136] The adjusted production process may be a virtual production process obtained by adjusting the real-time production process according to a number of optimizable factors.

[0137] Specifically, through process simulation software, such as Aspen Plus, the real-time production process is simulated according to preset production process information and real-time production data sets to obtain a virtual process of the real-time production process, and according to a number of optimizable factors, the virtual process of the real-time production process is adjusted through the process simulation software to simulate the adjusted production process, and a series of production data in the adjusted production process is extracted, and the above series of production data is determined as the adjustment results, so as to provide accurate data basis for the subsequent adjustment of the actual production process.

[0138] Through the method provided by this implementation, the real-time production process is simulated according to the preset production process information and the real-time production data set, and the real-time production process is adjusted according to a number of optimizable factors. The adjusted production process is analyzed, and a number of production data corresponding to the adjusted production process are determined as the adjustment result, thereby improving the accuracy of the adjustment result, avoiding irreversible consequences caused by direct adjustments to the actual production process, and providing an intuitive data basis for subsequent adjustments to the actual production process.

[0139] In some embodiments, based on the real-time production data set and the adjustment results, the data of each time node of each production data in the real-time production data and the adjustment results at the analysis time after being separated by a preset separation time are recorded to form a dynamic data set; based on the dynamic data set, the visualized dynamic production data is determined and output for professional personnel to view.

[0140] The dynamic data set may be a data set in which the real-time production data and the production data in the adjustment results change along with the production time series.

[0141] Visualized dynamic production data can be data obtained after visual processing of dynamic production data, which can be directly interactively analyzed by relevant personnel.

[0142] Professionals may be persons responsible for adjusting the organic chemical production process.

[0143] Specifically, dynamic data sets are dynamically visualized through data visualization processing libraries, such as D3.js (Data-Driven Documents), to obtain visualized dynamic production data, and the visualized dynamic production data is output through human-computer interaction devices, such as high-definition touch screens, for professionals to conduct interactive analysis.

[0144] Through the method provided in this embodiment, the real-time production data set and adjustment results are analyzed, the dynamic data set is determined, and the dynamic data set is visualized through data visualization technology to determine the visualized dynamic production data. The visualized dynamic production data is provided for analysis by professionals, so that professionals can understand the changes in production process data, and provide intuitive and comprehensive data for professionals to improve the organic chemical production process.

Claims

1. A MES intelligent production control method based on cloud computing, characterized in that: include: Acquire a real-time production data set of an organic chemical product, analyze the real-time production data set, and determine the production bottleneck link; Based on the production bottleneck, analyzing the real-time production data set to determine a number of optimizable factors; According to the several optimizable factors, a simulation is performed on the production process of the organic chemical product to determine an adjustment result; According to the adjustment result, the real-time production data set is adjusted to determine the bottleneck optimization data set; According to the bottleneck optimization data set, the production equipment is controlled to make corresponding adjustments; The analyzing the real-time production data set to determine the production bottleneck includes: According to the preset total analysis time and the preset separation time, time smoothing processing is performed on each real-time production data in the real-time production data set to determine the low-volatility smoothed data corresponding to each real-time production data under the preset analysis time; Based on the low-volatility smoothed data and according to the real-time production data set, determining a volatility index of each real-time production data under the preset total analysis time; According to the fluctuation index of each real-time production data, determine the relationship fluctuation index between each real-time production data and other real-time production data; Analyze the relationship fluctuation index of each real-time production data to determine the production bottleneck link; According to the preset total analysis time and the preset separation time, each real-time production data in the real-time production data set is subjected to time smoothing processing to determine the low-volatility smoothed data corresponding to each real-time production data under the preset analysis time, referring to the following formula: Among them, t is the preset total analysis time, LD i,t is the low-volatility smoothed data of the ith real-time production data in the real-time production data set under the preset analysis time t, N is the preset separation time, X i,k is the specific data corresponding to the i-th real-time production data at time node k; Based on the low-volatility smoothed data, according to the real-time production data set, the volatility index of each real-time production data under the preset total analysis time is determined, referring to the following formula: Among them, t is the preset total analysis time, σ i,t is the volatility index of the ith real-time production data under the preset total analysis time t, N is the preset separation time, X i,j is the specific value corresponding to the i-th real-time production data at time node j, LD i,t The low-volatility smoothed data of the i-th real-time production data in the preset analysis time t; According to the fluctuation index of each real-time production data, the relationship fluctuation index of each real-time production data is determined, referring to the following formula: Among them, σ i ′ ,t is the relationship fluctuation index of the ith real-time production data under the preset total analysis time t, σ i,t is the volatility index of the ith real-time production data under the preset total analysis time t, C ij is the preset data relationship matrix, σ j,t is the fluctuation index of the jth real-time production data under the preset total analysis time t; M represents the amount of the real-time production data; The step of analyzing the relationship fluctuation index of each real-time production number to determine the production bottleneck link includes: According to the preset relationship fluctuation threshold, refer to the following formula to determine the location set of several abnormal relationship fluctuation indexes in the real-time production data set: Where S is the location set, I i ′ ,t is the indicator variable, σ i ′ ,t is the relationship fluctuation index of the i-th real-time production data under the preset total analysis time t, and T is the preset relationship fluctuation threshold; Analyzing the real-time production data set according to the location set to determine a number of abnormal production data; Determine the production bottleneck link according to the abnormal production data; Based on the production bottleneck link, the real-time production data set is analyzed to determine several factors that can be optimized, including: According to the production bottleneck link, analyzing the real-time production data set to determine a number of bottleneck production data related to the production bottleneck link; Analyze the plurality of bottleneck production data to determine the sensitivity of each bottleneck production data to the plurality of abnormal production data; Determining the plurality of optimizable factors according to the sensitivity of each bottleneck production data to the plurality of abnormal production data and a preset sensitivity threshold; The analysis of the plurality of bottleneck production data to determine the sensitivity of each bottleneck production data to the plurality of abnormal production data is performed by referring to the following formula: Among them, Y j is the sensitivity, β0 is the preset intercept term, ∈ is the preset disturbance term, β a Produce data for the current bottleneck, X j is the jth abnormal production data, F j is a preset relationship index between the jth abnormal production data and the current bottleneck production data, and n is the total number of the abnormal production data.

2. The method according to claim 1, characterized in that The method of simulating the production process of the organic chemical product according to the plurality of optimizable factors to determine the adjustment result includes: Simulating a real-time production process according to preset production process information and the real-time production data set; According to the plurality of optimizable factors, the real-time production process is adjusted to simulate the adjusted production process; The adjusted production process is analyzed, and a number of production data in the adjusted production process are determined as adjustment results.

3. The method according to claim 2, characterized in that The method further comprises: According to the real-time production data set and the adjustment result, the data of each time node of the real-time production data and each production data in the adjustment result at the analysis time after being separated by the preset separation time length are recorded to form a dynamic data set; Based on the dynamic data set, visual dynamic production data is determined and output for viewing by professionals.

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