CN117311298B - Product optimization production method and system combining pH value control - Google Patents
Product optimization production method and system combining pH value control Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 63
- 238000005457 optimization Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 claims abstract description 98
- 230000008569 process Effects 0.000 claims abstract description 75
- 239000011159 matrix material Substances 0.000 claims abstract description 74
- 230000002159 abnormal effect Effects 0.000 claims abstract description 42
- 230000001687 destabilization Effects 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims description 38
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 30
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 15
- 239000001569 carbon dioxide Substances 0.000 claims description 15
- 230000002441 reversible effect Effects 0.000 claims description 15
- 238000011084 recovery Methods 0.000 claims description 14
- 238000012806 monitoring device Methods 0.000 claims description 13
- 230000006641 stabilisation Effects 0.000 claims description 13
- 238000011105 stabilization Methods 0.000 claims description 13
- 238000012549 training Methods 0.000 claims description 13
- 238000011156 evaluation Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 abstract description 11
- 238000001514 detection method Methods 0.000 abstract description 10
- 230000000694 effects Effects 0.000 abstract description 4
- 230000000087 stabilizing effect Effects 0.000 description 11
- 230000008859 change Effects 0.000 description 10
- 235000013361 beverage Nutrition 0.000 description 6
- 230000001105 regulatory effect Effects 0.000 description 5
- 238000010219 correlation analysis Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000796 flavoring agent Substances 0.000 description 2
- 235000013355 food flavoring agent Nutrition 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000000630 rising effect Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 239000006188 syrup Substances 0.000 description 2
- 235000020357 syrup Nutrition 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 238000012097 association analysis method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 235000015203 fruit juice Nutrition 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
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Abstract
The invention provides a product optimization production method and system combined with pH value control, and relates to the technical field of intelligent control, wherein the method comprises the following steps: obtaining a process flow node for producing a target product; acquiring the added component information and the operation environment information of each node in the process flow nodes; establishing a component association matrix and an environment association matrix; acquiring a PH fluctuation index; a PH destabilization recognition unit is established, PH fluctuation indexes are input into the PH destabilization recognition unit, and fluctuation destabilization probability is obtained; when the fluctuation stability back probability is smaller than the preset fluctuation stability back probability, the abnormal nodes are identified and the process optimization is carried out on the abnormal nodes, so that the technical effects that the control result of the PH value is inaccurate and the production quality is influenced due to the fact that the detection and the analysis of the PH value of different production processes are inaccurate in the prior art are solved, the control accuracy of the PH value is improved, and the production quality of products is improved are achieved.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to a method and a system for optimizing production of a product by combining pH value control.
Background
When producing beverages, it is necessary to control the quality of the beverage, wherein the PH is an important indicator for the quality of the beverage, and thus stable control of the PH is an effective means for controlling the quality of the beverage. In the prior art, the detection and analysis of the PH values of different production processes are inaccurate, so that the control result of the PH values is inaccurate, and the production quality is affected.
Disclosure of Invention
The invention provides a product optimization production method and system combined with pH value control, which are used for solving the technical problems that the control result of the pH value is inaccurate and the production quality is affected due to inaccurate detection and analysis of the pH value of different production processes in the prior art.
According to a first aspect of the present invention there is provided a method of optimizing production of a product incorporating pH control, comprising: obtaining a process flow node for producing a target product; acquiring the added component information and the operation environment information of each node in the process flow nodes; PH association identification is carried out based on the added component information and the operation environment information, and a component association matrix and an environment association matrix are established; carrying out PH fluctuation assessment on each node in the process flow nodes by utilizing the component correlation matrix and the environment correlation matrix to obtain PH fluctuation indexes; the method comprises the steps of establishing a PH destabilization identification unit according to historical production record data of each node in the process flow by collecting the historical production record data, inputting the PH fluctuation index into the PH destabilization identification unit, and obtaining fluctuation destabilization probability; and when the fluctuation stability back probability is smaller than the preset fluctuation stability back probability, identifying abnormal nodes and performing process optimization on the abnormal nodes.
According to a second aspect of the present invention there is provided a product optimisation production system incorporating pH control comprising: the process flow acquisition module is used for acquiring process flow nodes for producing the target product; the node information acquisition module is used for acquiring the added component information and the operation environment information of each node in the process flow nodes; the PH association identification module is used for carrying out PH association identification based on the added component information and the operation environment information, and establishing a component association matrix and an environment association matrix; the PH fluctuation evaluation module is used for carrying out PH fluctuation evaluation on each node in the process flow nodes by utilizing the component correlation matrix and the environment correlation matrix to obtain PH fluctuation indexes; the PH stability recovery identification module is used for establishing a PH stability recovery identification unit according to the historical production record data of each node in the process flow by collecting the historical production record data, inputting the PH fluctuation index into the PH stability recovery identification unit and obtaining fluctuation stability recovery probability; and the process optimization module is used for identifying abnormal nodes and performing process optimization on the abnormal nodes when the fluctuation stability back probability is smaller than a preset fluctuation stability back probability.
According to one or more technical solutions adopted by the present invention, the following beneficial effects are achieved:
acquiring process flow nodes for producing target products, acquiring added component information and operation environment information of each node in the process flow nodes, carrying out PH association identification based on the added component information and the operation environment information, establishing a component association matrix and an environment association matrix, carrying out PH fluctuation assessment on each node in the process flow nodes by utilizing the component association matrix and the environment association matrix, acquiring PH fluctuation indexes, establishing a PH stability recognition unit by acquiring historical production record data of each node in the process flow, inputting the PH fluctuation indexes into the PH stability recognition unit, acquiring fluctuation stability probability, and identifying abnormal nodes and carrying out process optimization on the abnormal nodes when the fluctuation stability probability is smaller than the preset fluctuation stability probability. Therefore, by carrying out fluctuation stability probability analysis, the abnormal nodes are identified to carry out process optimization, the control accuracy of the PH value is improved, and the technical effect of improving the production quality of products is further achieved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. The accompanying drawings, which are included to provide a further understanding of the invention, illustrate and explain the present invention, and together with the description serve to explain the principle of the invention, if not to limit the invention, and to enable others skilled in the art to make and use the invention without undue effort.
FIG. 1 is a schematic flow chart of a method for optimizing production of a product in combination with pH control according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a product optimization production system with pH control according to an embodiment of the present invention.
Reference numerals illustrate: the system comprises a process flow acquisition module 11, a node information acquisition module 12, a PH association identification module 13, a PH fluctuation evaluation module 14, a PH stability recovery identification module 15 and a process optimization module 16.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein.
The terminology used in the description is for the purpose of describing embodiments only and is not intended to be limiting of the invention. As used in this specification, the singular terms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises" and/or "comprising," when used in this specification, specify the presence of steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other steps, operations, elements, components, and/or groups thereof.
Unless defined otherwise, all terms (including technical and scientific terms) used in this specification should have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms, such as those defined in commonly used dictionaries, should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Like numbers refer to like elements throughout.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for presentation, analyzed data, etc.) related to the present invention are information and data authorized by the user or sufficiently authorized by each party.
Example 1
Fig. 1 is a diagram of a method for optimizing production of a product in combination with pH control according to an embodiment of the present invention, where the method includes:
obtaining a process flow node for producing a target product;
the target product generally refers to any type of product to be subjected to a production process optimization, and the quality of the target product is related to the PH, such as any type of beverage or the like. Obtaining process flow nodes for producing target products, wherein the process flow nodes refer to all process flows for producing the target products, such as a process for producing fruit juice; the process nodes comprise raw material peeling, cutting, syrup making, carbon dioxide mixing, additive adding and the like. It should be noted that, the process flow node generally refers to the process currently used by the target product, so that the process flow node can be directly invoked by a production factory connected with the target product or directly uploaded by a staff through a user terminal.
Acquiring the added component information and the operation environment information of each node in the process flow nodes;
the method comprises the steps of obtaining the added component information and the operation environment information of each node in the process flow nodes, wherein the added component information comprises component types and component contents, such as the types and the contents of added flavoring agents, and the like.
The operation environment information refers to the external operation environment, such as the operation temperature, the humidity, etc., when the process of each node is performed, and the external operation environment can be detected by the detection device of the existing temperature sensor and the humidity sensor, so that the operation environment information can be obtained.
PH association identification is carried out based on the added component information and the operation environment information, and a component association matrix and an environment association matrix are established;
and carrying out PH association identification based on the added component information and the operation environment information, and establishing a component association matrix and an environment association matrix, wherein the component association matrix consists of components related to the PH value of the target product, and the environment association matrix consists of environment conditions related to the PH value of the target product, and the specific acquisition process is as follows.
In a preferred embodiment, further comprising:
based on the added component information, establishing a component-PH value association index; establishing an environment-PH value association index based on the operation environment information; carrying out association index identification by using the component-PH value association index, identifying component types greater than or equal to a preset association index, and outputting a component association matrix by using the association index corresponding to the component types; and carrying out association index identification by using the environment-PH value association index, identifying environment parameters which are larger than or equal to the preset association index, and outputting an environment association matrix by using the association index corresponding to the environment parameters.
Based on the added component information, a component-PH value association index is established, wherein the component-PH value association index is a component index related to the PH value of a target product, such as the carbon dioxide content, syrup, various flavoring agents and the like when carbon dioxide mixing is carried out, specifically, historical production record data of each node in the past period of time when the target product is produced can be taken, the historical production record data comprises historical added component information and historical PH value detection information of each node, based on the historical PH value detection information and the historical added component information, the correlation analysis of the added component and the PH value is carried out by utilizing the existing correlation analysis method, such as gray correlation degree, so that the correlation degree corresponding to different added components is obtained, and the correlation degree corresponding to the added component information is extracted from the correlation degree to form the component-PH value association index.
Based on the operation environment information, an environment-PH value association index is established, wherein the environment-PH value association index is an environment index related to the PH value of a target product, for example, when the humidity of the operation environment is too high, the PH value can be reduced, specifically, historical operation environment information and historical PH value detection information of each node in the past period of time when the target product is produced can be taken, based on the historical PH value detection information and the historical operation environment information, the association analysis of the added environment and the PH value is carried out by utilizing the existing association analysis method, such as gray association degree, so that association degrees corresponding to different environments are obtained, and then the association degrees corresponding to the environment information are extracted from the association degrees to form the environment-PH value association index. It should be noted that, the correlation analysis is a common technical means for those skilled in the art, and is not developed.
And carrying out association index identification by using the component-PH value association index, and marking the component type larger than or equal to a preset association index, wherein the preset association index refers to a pre-designed association degree, and particularly refers to a corresponding association degree when the influence on the PH value of a target product is very small, and the association degree is set by a person skilled in the art through combining historical experience. And further screening component types which are larger than or equal to a preset association index from the component-PH value association index, and forming the component association matrix by the association index (i.e. association degree) corresponding to the component types. And similarly, carrying out association index identification by using the environment-PH value association index, and identifying environment parameters which are larger than or equal to the preset association index, namely, screening environment condition parameters (temperature, humidity and the like) which are larger than or equal to the preset association index from the environment-PH value association index, and forming the environment association matrix by using the association index (i.e. association degree) corresponding to the environment parameters. It should be noted that each node corresponds to a component association matrix and an environment association matrix. Therefore, the correlation analysis of the added components and the environment with the PH value is realized, a foundation is provided for the subsequent process optimization, the fluctuation analysis of the PH value is convenient, the stability and the accuracy of the PH value of the produced beverage are ensured, and the production quality is improved.
Carrying out PH fluctuation assessment on each node in the process flow nodes by utilizing the component correlation matrix and the environment correlation matrix to obtain PH fluctuation indexes;
and carrying out PH fluctuation assessment on each node in the process flow node by using the component correlation matrix and the environment correlation matrix to obtain PH fluctuation indexes, wherein the PH fluctuation assessment is based on the correlation degree of different added components, environment parameters and PH values in the component correlation matrix and the environment correlation matrix, and the variation range of the PH value corresponding to each node is obtained as the PH fluctuation indexes, and the specific obtaining process is as follows.
In a preferred embodiment, further comprising:
establishing a weight training layer according to the component correlation matrix and the environment correlation matrix, and carrying out PH fluctuation assessment on each node in the process flow nodes according to the weight training layer, wherein the PH fluctuation assessment comprises PH forward fluctuation amplitude and PH reverse fluctuation amplitude; and calculating according to the PH forward fluctuation amplitude and the PH reverse fluctuation amplitude, and outputting PH fluctuation indexes of all nodes.
According to the component correlation matrix and the environment correlation matrix, a weight training layer is established, the weight training layer is an existing machine learning model and is used for predicting the fluctuation value of the PH value of each node, in short, based on the correlation degree of different added components, environment information and the PH value in the component correlation matrix and the environment correlation matrix, the PH value change of each node in the process flow node is predicted (namely PH fluctuation assessment), and the superposition result of the correlation degree of the different added components, the environment information and the PH value and the current PH value is used as a fluctuation assessment result, wherein the fluctuation assessment result comprises PH forward fluctuation amplitude and PH reverse fluctuation amplitude, the PH forward fluctuation amplitude is the PH value increasing amplitude, and the PH reverse fluctuation amplitude is the PH value decreasing amplitude. Specifically, the corresponding historical component correlation matrix, the historical environment correlation matrix and the corresponding historical PH value fluctuation value can be called based on the component correlation matrix and the environment correlation matrix, the training construction of the weight training layer is carried out based on the existing machine learning model by using the historical component correlation matrix, the historical environment correlation matrix and the corresponding historical PH value fluctuation value, and the weight training layer meeting the requirements can be obtained by carrying out weight adjustment training on different component types and environment parameters through the corresponding historical PH value fluctuation value. And then, according to the weight training layer, carrying out PH fluctuation assessment on each node in the process flow nodes, wherein the PH fluctuation assessment comprises PH forward fluctuation amplitude and PH reverse fluctuation amplitude, and the PH forward fluctuation amplitude and the PH reverse fluctuation amplitude are all fluctuation amplitudes under a plurality of different times. And finally, calculating the PH forward fluctuation amplitude and the PH reverse fluctuation amplitude, and outputting PH fluctuation indexes of each node, namely, determining a forward fluctuation range and a reverse fluctuation range by the PH forward fluctuation amplitude and the PH reverse fluctuation amplitude, and determining a PH value change range as the PH fluctuation index by combining the forward fluctuation range and the reverse fluctuation range, wherein the PH fluctuation indexes also carry fluctuation period identifications, specifically, carrying out fluctuation trend analysis on the PH forward fluctuation amplitude and the PH reverse fluctuation amplitude, and obtaining the time length which is passed when the fluctuation amplitude is in a stable state as the fluctuation period. Therefore, the PH value fluctuation analysis of each node is realized, support is provided for the subsequent fluctuation stabilizing probability, process optimization is further carried out, and the control accuracy of the PH value is improved.
The method comprises the steps of establishing a PH destabilization identification unit according to historical production record data of each node in the process flow by collecting the historical production record data, inputting the PH fluctuation index into the PH destabilization identification unit, and obtaining fluctuation destabilization probability;
by collecting historical production record data of each node in the process flow, wherein the historical production record data is a production record in a past period of time, a factory capable of connecting a production target product can directly call the historical production record data, the historical production record data comprises PH value change data of each node, it is to be noted that the historical production record data is certain to eliminate abnormal data (such as data when quality detection is unqualified), then a PH stability recovery identification unit is established by the historical production record data, the PH fluctuation index is input into the PH stability recovery identification unit, fluctuation stability recovery probability is obtained, the fluctuation stability recovery probability represents the probability that the fluctuation range of the PH value under each node is at a normal value, and the specific acquisition process is described in detail below.
In a preferred embodiment, further comprising:
carrying out PH stable-returning period identification by using the historical production record data to obtain a mean stable-returning period corresponding to each node; inputting PH fluctuation indexes of all nodes into the PH stabilization identification unit, wherein the PH stabilization identification unit comprises a mean stabilization period corresponding to all the nodes and a preset PH value corresponding to all the nodes; and predicting the PH fluctuation index of each node according to the PH stability recognition unit to obtain the fluctuation stability probability, wherein the fluctuation stability probability is the probability that the PH value fluctuation tends to correspond to the preset PH value in the mean value stability period.
And carrying out PH stabilization period identification by using the historical production record data, wherein the PH stabilization period refers to a change amplitude recovery period when the PH value changes, for example, the PH value starts to rise at a certain moment and then falls along with time change, the PH value tends to a stable value after multiple rising and falling are carried out, the rising and falling time is the PH stabilization period, and therefore, the time period of the value tending to the stable value is obtained as the average stabilization period corresponding to each node through carrying out change trend analysis on the PH value change data of each node in the historical production record data, and the stable value which the PH value tends to is the preset PH value corresponding to each node.
And further inputting the PH fluctuation index of each node into the PH stability back identification unit, wherein the PH stability back identification unit comprises a mean stability back period corresponding to each node and a preset PH value corresponding to each node. Predicting the PH fluctuation index of each node according to the PH stability recognition unit to obtain the fluctuation stability probability, wherein the fluctuation stability probability is the probability that the PH value fluctuation tends to correspond to the preset PH value in the mean value stability period, namely simply comparing the PH value change range in the PH fluctuation index with the preset PH value to obtain the ratio of the difference value of the PH value change range and the preset PH value, and subtracting the ratio from 1 to obtain a calculation result; and then calculating the ratio of the difference value between the fluctuation period and the mean value destabilization period in the PH fluctuation index to the mean value destabilization period, subtracting 1 from the ratio to obtain another calculation result, and averaging the two calculation results to obtain mean value as fluctuation destabilization probability, wherein the above is the process of predicting PH fluctuation indexes of all nodes according to the PH destabilization recognition unit, so that analysis of fluctuation destabilization probability is realized, and the larger the fluctuation destabilization probability is, the more accurate the control of PH value is, namely, process optimization is not needed, thereby providing support for subsequent process optimization, and improving the production quality of target products.
In a preferred embodiment, further comprising:
the PH electrode monitoring device is connected to monitor PH values of all nodes in the process flow nodes in real time to obtain PH real-time monitoring data of all the nodes; the PH fluctuation index of each node is used as a constraint condition to predict PH real-time monitoring data of each node in real time, so as to obtain fluctuation deviation probability; and calculating the fluctuation stability probability under the condition of the fluctuation deviation probability.
The PH electrode monitoring device is connected with the PH electrode monitoring device, and the PH electrode monitoring device is a sensor for detecting the PH value. The PH value of each node in the process flow nodes is monitored in real time through the PH electrode monitoring device, PH real-time monitoring data of each node is obtained, and the PH real-time monitoring data is the data detected by the PH electrode monitoring device in real time and can be directly obtained. Further, PH fluctuation indexes of all nodes are used as constraint conditions to predict the PH real-time monitoring data of all nodes in real time to obtain fluctuation deviation probability, the PH fluctuation indexes are obtained according to the component correlation matrix and the environment correlation matrix, and as can be understood, the PH fluctuation indexes are predicted values, the PH real-time monitoring data are actual detection values, and the percentage error of the PH fluctuation indexes and the PH real-time monitoring data of all nodes is calculated to serve as the fluctuation deviation probability. And then compensating and correcting the fluctuation stability return probability according to the fluctuation deviation probability, namely, the fluctuation deviation probability can be understood as an error existing in the fluctuation stability return probability, and the updated fluctuation stability return probability can be obtained by correcting the error, so that the accuracy of the fluctuation stability return probability is improved.
And when the fluctuation stability back probability is smaller than the preset fluctuation stability back probability, identifying abnormal nodes and performing process optimization on the abnormal nodes.
The preset fluctuation stability back probability is set by a person skilled in the art, and represents the condition that the PH value is extremely small, namely the condition that the influence on the stability and the accuracy of the PH value of a target product is small, and can be set to be 95% by way of example, and can be specifically set by combining with historical experience. When the fluctuation stability back probability is smaller than the preset fluctuation stability back probability, identifying an abnormal node and performing process optimization on the abnormal node, namely, taking the node with the minimum fluctuation stability back probability as the abnormal node and marking, reminding a worker to perform process optimization on the node, thereby realizing control and adjustment of the PH value and improving the production quality of a target product.
In a preferred embodiment, further comprising:
judging whether the optimized fluctuation stability back probability is smaller than the preset fluctuation stability back probability, and if the optimized fluctuation stability back probability is smaller than the preset fluctuation stability back probability, acquiring abnormal nodes from all nodes in the process flow nodes, wherein the abnormal nodes are nodes with the minimum fluctuation stability back probability in all the nodes; and setting carbon dioxide pressurizing equipment by the abnormal node, and controlling the PH value of the abnormal node by using the carbon dioxide pressurizing equipment.
Judging whether the optimized fluctuation stability back probability is smaller than the preset fluctuation stability back probability, if so, considering that the control of the PH value is inaccurate, and acquiring abnormal nodes from all the nodes in the process flow node, wherein the abnormal nodes are the nodes with the minimum fluctuation stability back probability in all the nodes. That is, the smaller the fluctuation stability probability is, the larger the fluctuation range of the PH value is, and the larger the range of the PH value needs to be regulated and controlled, so that the comprehensive PH value level of all the nodes can be regulated and controlled only by the node with the smallest fluctuation stability probability, and the increase of the regulating difficulty caused by the linkage action of different nodes when the plurality of nodes are regulated is prevented, thereby achieving the effect of improving the efficiency. Therefore, the carbon dioxide pressurizing device is arranged on the abnormal node, the PH value of the abnormal node is controlled by the carbon dioxide pressurizing device, the carbon dioxide pressurizing device is the existing device for adjusting the content of the input carbon dioxide, and the model of the carbon dioxide pressurizing device is not limited. And the PH value of the abnormal node is controlled through the carbon dioxide pressurizing equipment, so that the PH value is restored to a normal level, and the PH value of the abnormal node can be monitored in real time, the standard PH value corresponding to the node is obtained, and the PH value is regulated according to the difference between the PH value and the standard PH value. Therefore, the process optimization is realized through the control and adjustment of the PH value, the accuracy of PH value control is improved, and the production quality of a target product is further improved.
In a preferred embodiment, further comprising:
judging whether the fluctuation stability back probability is smaller than a preset fluctuation stability back probability or not; when the fluctuation stability probability is greater than or equal to the preset fluctuation stability probability, configuring the monitoring cycle frequency of the PH electrode monitoring device, and carrying out real-time monitoring sampling on PH values of all nodes in the process flow nodes according to the monitoring cycle frequency to obtain secondary PH real-time monitoring data of all the nodes; and carrying out real-time prediction on the secondary PH real-time monitoring data of each node to obtain the optimized fluctuation stability probability.
Judging whether the fluctuation stabilizing probability is smaller than a preset fluctuation stabilizing probability, when the fluctuation stabilizing probability is larger than or equal to the preset fluctuation stabilizing probability, configuring the monitoring cycle frequency of the PH electrode monitoring device, namely, when the fluctuation stabilizing probability is larger than or equal to the preset fluctuation stabilizing probability, considering that the PH value of a target product meets the requirement, and not needing to carry out process optimization, but needing to carry out periodic monitoring on the fluctuation stabilizing probability, so as to carry out compensation correction on the fluctuation stabilizing probability in real time, prevent PH value change abnormality in the process execution process, carry out process optimization in time, wherein the monitoring cycle frequency refers to the time period of monitoring, and the monitoring cycle frequency can be set by a person in the field according to the size of the fluctuation stabilizing probability, namely, the larger the fluctuation stabilizing probability is, the longer the time period is, namely, the monitoring is carried out every time with longer intervals. Further, the PH electrode monitoring device is utilized to conduct real-time monitoring sampling on PH values of all nodes in the process flow nodes according to the monitoring period frequency, secondary PH real-time monitoring data of all the nodes are obtained, further, real-time prediction is conducted on the secondary PH real-time monitoring data of all the nodes, the optimized fluctuation stability probability is obtained, namely, the percentage error of PH fluctuation indexes of all the nodes and the secondary PH real-time monitoring data is calculated to serve as fluctuation deviation probability, further, compensation correction is conducted on the fluctuation stability probability according to the fluctuation deviation probability, and accuracy of PH monitoring control is improved.
Based on the analysis, the one or more technical schemes provided by the invention can achieve the following beneficial effects:
acquiring process flow nodes for producing target products, acquiring added component information and operation environment information of each node in the process flow nodes, carrying out PH association identification based on the added component information and the operation environment information, establishing a component association matrix and an environment association matrix, carrying out PH fluctuation assessment on each node in the process flow nodes by utilizing the component association matrix and the environment association matrix, acquiring PH fluctuation indexes, establishing a PH stability recognition unit by acquiring historical production record data of each node in the process flow, inputting the PH fluctuation indexes into the PH stability recognition unit, acquiring fluctuation stability probability, and identifying abnormal nodes and carrying out process optimization on the abnormal nodes when the fluctuation stability probability is smaller than the preset fluctuation stability probability. Therefore, by carrying out fluctuation stability probability analysis, the abnormal nodes are identified to carry out process optimization, the control accuracy of the PH value is improved, and the technical effect of improving the production quality of products is further achieved.
Example two
Based on the same inventive concept as the product optimization production method in combination with pH control in the foregoing embodiment, as shown in fig. 2, the present invention also provides a product optimization production system in combination with pH control, the system comprising:
a process flow obtaining module 11, wherein the process flow obtaining module 11 is used for obtaining a process flow node for producing a target product;
a node information obtaining module 12, where the node information obtaining module 12 is configured to obtain addition component information and operation environment information of each node in the process flow nodes;
the PH association identification module 13 is used for carrying out PH association identification based on the added component information and the operation environment information, and establishing a component association matrix and an environment association matrix;
the PH fluctuation assessment module 14 is configured to perform PH fluctuation assessment on each node in the process flow nodes by using the component correlation matrix and the environment correlation matrix, so as to obtain a PH fluctuation index;
the PH stability back identification module 15 is used for establishing a PH stability back identification unit according to the historical production record data by collecting the historical production record data of each node in the process flow, inputting the PH fluctuation index into the PH stability back identification unit and obtaining fluctuation stability back probability;
and the process optimization module 16 is used for identifying abnormal nodes and performing process optimization on the abnormal nodes when the fluctuation stability back probability is smaller than a preset fluctuation stability back probability.
Further, the PH-associated identification module 13 is further configured to:
based on the added component information, establishing a component-PH value association index;
establishing an environment-PH value association index based on the operation environment information;
carrying out association index identification by using the component-PH value association index, identifying component types greater than or equal to a preset association index, and outputting a component association matrix by using the association index corresponding to the component types;
and carrying out association index identification by using the environment-PH value association index, identifying environment parameters which are larger than or equal to the preset association index, and outputting an environment association matrix by using the association index corresponding to the environment parameters.
Further, the PH fluctuation evaluation module 14 is further configured to:
establishing a weight training layer according to the component correlation matrix and the environment correlation matrix, and carrying out PH fluctuation assessment on each node in the process flow nodes according to the weight training layer, wherein the PH fluctuation assessment comprises PH forward fluctuation amplitude and PH reverse fluctuation amplitude;
and calculating according to the PH forward fluctuation amplitude and the PH reverse fluctuation amplitude, and outputting PH fluctuation indexes of all nodes.
Further, the PH-returning identification module 15 is further configured to:
carrying out PH stable-returning period identification by using the historical production record data to obtain a mean stable-returning period corresponding to each node;
inputting PH fluctuation indexes of all nodes into the PH stabilization identification unit, wherein the PH stabilization identification unit comprises a mean stabilization period corresponding to all the nodes and a preset PH value corresponding to all the nodes;
and predicting the PH fluctuation index of each node according to the PH stability recognition unit to obtain the fluctuation stability probability, wherein the fluctuation stability probability is the probability that the PH value fluctuation tends to correspond to the preset PH value in the mean value stability period.
Further, the PH-returning identification module 15 is further configured to:
the PH electrode monitoring device is connected to monitor PH values of all nodes in the process flow nodes in real time to obtain PH real-time monitoring data of all the nodes;
the PH fluctuation index of each node is used as a constraint condition to predict PH real-time monitoring data of each node in real time, so as to obtain fluctuation deviation probability;
and calculating the fluctuation stability probability under the condition of the fluctuation deviation probability.
Further, the PH-returning identification module 15 is further configured to:
judging whether the fluctuation stability back probability is smaller than a preset fluctuation stability back probability or not;
when the fluctuation stability probability is greater than or equal to the preset fluctuation stability probability, configuring the monitoring cycle frequency of the PH electrode monitoring device, and carrying out real-time monitoring sampling on PH values of all nodes in the process flow nodes according to the monitoring cycle frequency to obtain secondary PH real-time monitoring data of all the nodes;
and carrying out real-time prediction on the secondary PH real-time monitoring data of each node to obtain the optimized fluctuation stability probability.
Further, the process optimization module 16 is further configured to:
judging whether the optimized fluctuation stability back probability is smaller than the preset fluctuation stability back probability, and if the optimized fluctuation stability back probability is smaller than the preset fluctuation stability back probability, acquiring abnormal nodes from all nodes in the process flow nodes, wherein the abnormal nodes are nodes with the minimum fluctuation stability back probability in all the nodes;
and setting carbon dioxide pressurizing equipment by the abnormal node, and controlling the PH value of the abnormal node by using the carbon dioxide pressurizing equipment.
The specific example of the product optimized production method with pH control in the first embodiment is also applicable to the product optimized production system with pH control in this embodiment, and those skilled in the art will clearly know the product optimized production system with pH control in this embodiment from the foregoing detailed description of the product optimized production method with pH control, so that the detailed description thereof will not be repeated for the sake of brevity.
It should be understood that the various forms of flow shown above, reordered, added or deleted steps may be used, as long as the desired results of the disclosed embodiments are achieved, and are not limiting herein.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (7)
1. A method for optimizing production of a product in combination with pH control, the method comprising:
obtaining a process flow node for producing a target product;
acquiring the added component information and the operation environment information of each node in the process flow nodes;
PH association identification is carried out based on the added component information and the operation environment information, and a component association matrix and an environment association matrix are established;
carrying out PH fluctuation assessment on each node in the process flow nodes by utilizing the component correlation matrix and the environment correlation matrix to obtain PH fluctuation indexes;
the method comprises the steps of establishing a PH destabilization identification unit according to historical production record data of each node in the process flow by collecting the historical production record data, inputting the PH fluctuation index into the PH destabilization identification unit, and obtaining fluctuation destabilization probability;
when the fluctuation stability back probability is smaller than a preset fluctuation stability back probability, identifying abnormal nodes and performing process optimization on the abnormal nodes;
the step of inputting the PH fluctuation index into the PH destabilization recognition unit to obtain fluctuation destabilization probability comprises the following steps:
carrying out PH stable-returning period identification by using the historical production record data to obtain a mean stable-returning period corresponding to each node;
inputting PH fluctuation indexes of all nodes into the PH stabilization identification unit, wherein the PH stabilization identification unit comprises a mean stabilization period corresponding to all the nodes and a preset PH value corresponding to all the nodes;
and predicting the PH fluctuation index of each node according to the PH stability recognition unit to obtain the fluctuation stability probability, wherein the fluctuation stability probability is the probability that the PH value fluctuation tends to correspond to the preset PH value in the mean value stability period.
2. The method of claim 1, wherein PH correlation identification is performed based on the added component information and the operating environment information, and a component correlation matrix and an environment correlation matrix are established, the method comprising:
based on the added component information, establishing a component-PH value association index;
establishing an environment-PH value association index based on the operation environment information;
carrying out association index identification by using the component-PH value association index, identifying component types greater than or equal to a preset association index, and outputting a component association matrix by using the association index corresponding to the component types;
and carrying out association index identification by using the environment-PH value association index, identifying environment parameters which are larger than or equal to the preset association index, and outputting an environment association matrix by using the association index corresponding to the environment parameters.
3. The method of claim 2, wherein PH fluctuation assessment is performed on each of the process flow nodes using the component correlation matrix and the environment correlation matrix to obtain a PH fluctuation index, the method comprising:
establishing a weight training layer according to the component correlation matrix and the environment correlation matrix, and carrying out PH fluctuation assessment on each node in the process flow nodes according to the weight training layer, wherein the PH fluctuation assessment comprises PH forward fluctuation amplitude and PH reverse fluctuation amplitude;
and calculating according to the PH forward fluctuation amplitude and the PH reverse fluctuation amplitude, and outputting PH fluctuation indexes of all nodes.
4. The method of claim 1, wherein predicting the PH fluctuation index of each node according to the PH instability identification unit obtains the fluctuation instability probability, the method comprising:
the PH electrode monitoring device is connected to monitor PH values of all nodes in the process flow nodes in real time to obtain PH real-time monitoring data of all the nodes;
the PH fluctuation index of each node is used as a constraint condition to predict PH real-time monitoring data of each node in real time, so as to obtain fluctuation deviation probability;
and calculating the fluctuation stability probability under the condition of the fluctuation deviation probability.
5. The method of claim 4, wherein the method comprises:
judging whether the fluctuation stability back probability is smaller than a preset fluctuation stability back probability or not;
when the fluctuation stability probability is greater than or equal to the preset fluctuation stability probability, configuring the monitoring cycle frequency of the PH electrode monitoring device, and carrying out real-time monitoring sampling on PH values of all nodes in the process flow nodes according to the monitoring cycle frequency to obtain secondary PH real-time monitoring data of all the nodes;
and carrying out real-time prediction on the secondary PH real-time monitoring data of each node to obtain the optimized fluctuation stability probability.
6. The method of claim 5, wherein the method further comprises:
judging whether the optimized fluctuation stability back probability is smaller than the preset fluctuation stability back probability, and if the optimized fluctuation stability back probability is smaller than the preset fluctuation stability back probability, acquiring abnormal nodes from all nodes in the process flow node, wherein the abnormal nodes are nodes with the minimum fluctuation stability back probability in all the nodes;
and setting carbon dioxide pressurizing equipment by the abnormal node, and controlling the PH value of the abnormal node by using the carbon dioxide pressurizing equipment.
7. A pH control-incorporated product optimization production system for performing the pH control-incorporated product optimization production method according to any one of claims 1 to 6, the system comprising:
the process flow acquisition module is used for acquiring process flow nodes for producing the target product;
the node information acquisition module is used for acquiring the added component information and the operation environment information of each node in the process flow nodes;
the PH association identification module is used for carrying out PH association identification based on the added component information and the operation environment information, and establishing a component association matrix and an environment association matrix;
the PH fluctuation evaluation module is used for carrying out PH fluctuation evaluation on each node in the process flow nodes by utilizing the component correlation matrix and the environment correlation matrix to obtain PH fluctuation indexes;
the PH stability recovery identification module is used for establishing a PH stability recovery identification unit according to the historical production record data of each node in the process flow by collecting the historical production record data, inputting the PH fluctuation index into the PH stability recovery identification unit and obtaining fluctuation stability recovery probability;
and the process optimization module is used for identifying abnormal nodes and performing process optimization on the abnormal nodes when the fluctuation stability back probability is smaller than a preset fluctuation stability back probability.
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