CN118052403A - Industrial production flow adjustment method and device, storage medium and electronic equipment - Google Patents
Industrial production flow adjustment method and device, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN118052403A CN118052403A CN202410194256.4A CN202410194256A CN118052403A CN 118052403 A CN118052403 A CN 118052403A CN 202410194256 A CN202410194256 A CN 202410194256A CN 118052403 A CN118052403 A CN 118052403A
- Authority
- CN
- China
- Prior art keywords
- production
- industrial
- evaluation
- data
- flow
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 113
- 238000009776 industrial production Methods 0.000 title claims abstract description 85
- 238000003860 storage Methods 0.000 title claims abstract description 20
- 238000004519 manufacturing process Methods 0.000 claims abstract description 441
- 238000011156 evaluation Methods 0.000 claims abstract description 142
- 238000004458 analytical method Methods 0.000 claims abstract description 75
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 25
- 230000008569 process Effects 0.000 claims description 27
- 238000004364 calculation method Methods 0.000 claims description 17
- 238000012545 processing Methods 0.000 claims description 13
- 230000015572 biosynthetic process Effects 0.000 claims description 5
- 230000007246 mechanism Effects 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 description 25
- HEMHJVSKTPXQMS-UHFFFAOYSA-M Sodium hydroxide Chemical compound [OH-].[Na+] HEMHJVSKTPXQMS-UHFFFAOYSA-M 0.000 description 24
- 238000010586 diagram Methods 0.000 description 10
- 230000001105 regulatory effect Effects 0.000 description 8
- 235000011121 sodium hydroxide Nutrition 0.000 description 8
- 230000007613 environmental effect Effects 0.000 description 7
- 238000005265 energy consumption Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000007726 management method Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000004927 fusion Effects 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 description 2
- 229910052801 chlorine Inorganic materials 0.000 description 2
- 239000000460 chlorine Substances 0.000 description 2
- 239000003086 colorant Substances 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000002354 daily effect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 239000003973 paint Substances 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000005507 spraying Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an industrial production process adjustment method and device, a storage medium and electronic equipment. Wherein the method comprises the following steps: acquiring whole-flow production data of an industrial production process; evaluating the multi-level production index based on the whole-flow production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining the production flow to be adjusted; analyzing and predicting the evaluation result and the whole-process production data by using an analysis and prediction model to obtain a target production strategy of the production process to be adjusted; and adjusting the production flow to be adjusted according to the target production strategy. The invention solves the technical problems that in the prior art, the accuracy of an evaluation result is low due to the evaluation of the industrial production flow based on manual production experience, and the production efficiency is low due to the fact that the industrial production flow is not adjusted in time.
Description
Technical Field
The invention relates to the technical field of industrial production management, in particular to an industrial production flow adjustment method, an industrial production flow adjustment device, a storage medium and electronic equipment.
Background
In the industrial production process of enterprises, the business between each production subsystem and industrial software is independent, and resultant force is not formed, so that the problems of reduced product use effect, deviation of production targets and enterprise total targets and the like are easily caused. In the prior art, the produced product is generally evaluated based on the production experience of the production personnel so as to evaluate whether the product meets the requirement of the enterprise total target, and when the product does not meet the requirement of the enterprise total target, the abnormal production flow is determined according to the analysis of the production experience, and then the production flow is adjusted so that the subsequent product meets the requirement of the enterprise total target. However, since production experience of production personnel is limited, the existing production experience is difficult to judge that the accuracy of a new abnormal guide evaluation result of a product is low, and when an industrial production process is complex, the production personnel is difficult to accurately position the abnormal production process, so that the industrial production process is not adjusted timely, and the production efficiency is low.
From the above analysis, it is known that, aiming at the problems that in the prior art, the accuracy of the evaluation result is low due to the evaluation of the industrial production process based on the manual production experience, and the production efficiency is low due to the untimely adjustment of the industrial production process, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides an industrial production process adjustment method, an industrial production process adjustment device, a storage medium and electronic equipment, which at least solve the technical problems that in the prior art, the accuracy of an evaluation result is low due to the fact that the industrial production process is evaluated based on manual production experience, and further the production efficiency is low due to the fact that the industrial production process is not adjusted in time.
According to an aspect of an embodiment of the present invention, there is provided an industrial process flow adjustment method including:
acquiring whole-flow production data of an industrial production process; evaluating the multi-level production index based on the whole-flow production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining the production flow to be adjusted; analyzing and predicting the evaluation result and the whole-process production data by using an analysis and prediction model to obtain a target production strategy of the production process to be adjusted; and adjusting the production flow to be adjusted according to the target production strategy.
Optionally, the above industrial production process adjustment method further includes: constructing a top-level production index of an industrial production process; and decomposing the top-layer production index step by step from top to bottom according to the execution sequence of the production flow in the industrial production process and the images of the multi-level manager to obtain the multi-level production index.
Optionally, the evaluation result includes any one of scores of production indexes, and the evaluation of the multi-level production indexes based on the whole-flow production data and the evaluation algorithm is performed to obtain the evaluation result including: analyzing any production index by using an evaluation algorithm to obtain a first analysis result, wherein the first analysis result at least comprises: an evaluation index of any one production index, an industrial production flow associated with the evaluation index, and a weight of any one evaluation index; extracting production data of an industrial production process from the whole-process production data; scoring any one evaluation index according to the production data and a preset scoring mechanism to obtain a first score, wherein the first score is the score of any one evaluation index; and calculating the first scores and the weights to obtain second scores, and determining a set of the second scores as an evaluation result, wherein the second scores are scores of any production index.
Optionally, the analysis and prediction model includes an analysis model and a prediction model, and the analyzing and predicting the evaluation result and the whole-process production data by using the analysis and prediction model, and obtaining the target production strategy of the production process to be adjusted includes: responding to the evaluation result to determine that any one of the first scores is lower than a preset score threshold value, and determining that the industrial production process corresponding to any one of the first scores is the production process to be adjusted; extracting current production data of a production process to be adjusted from the whole-process production data; analyzing the current production data by utilizing an analysis model to obtain a second analysis result, wherein the second analysis result at least comprises a plurality of candidate production strategies of the production flow to be adjusted; calculating a plurality of candidate production strategies by using a prediction model to obtain a calculation result; based on the calculation result, a target production strategy is determined from the plurality of candidate production strategies.
Optionally, the second analysis result further includes basic data of the production process to be adjusted, an abnormal phenomenon, a cause of formation of the abnormal phenomenon, and a potential risk of the abnormal phenomenon.
Optionally, the above industrial production process adjustment method further includes: a scheduling table is formulated according to a target production strategy, wherein the scheduling table is used for determining the operation time of any group; and controlling the plurality of teams to perform the operation according to the scheduling table so as to execute the target production strategy.
Optionally, the above industrial production process adjustment method further includes: recording operation data of the operation device in the process of a plurality of teams; and generating a feedback report of the target production strategy based on the operation data, wherein the feedback report is used for determining the execution condition of the target production strategy.
According to another aspect of the embodiment of the present invention, there is also provided an industrial production process adjustment apparatus, including:
The acquisition module is used for acquiring the whole-flow production data of the industrial production process; the evaluation module is used for evaluating the multi-level production indexes based on the whole-process production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining the production process to be adjusted; the processing module is used for analyzing and predicting the evaluation result and the whole-process production data by utilizing the analysis and prediction model to obtain a target production strategy of the production process to be adjusted; and the adjusting module is used for adjusting the production flow to be adjusted according to the target production strategy.
Optionally, the industrial production process adjusting device further includes: the construction module is used for constructing a top-level production index of the industrial production process; and decomposing the top-layer production index step by step from top to bottom according to the execution sequence of the production flow in the industrial production process and the images of the multi-level manager to obtain the multi-level production index.
Optionally, the evaluation module is further configured to: the evaluation result comprises any production index score, the multi-level production index is evaluated based on the whole-flow production data and an evaluation algorithm, and the evaluation result comprises the following steps: analyzing any production index by using an evaluation algorithm to obtain a first analysis result, wherein the first analysis result at least comprises: an evaluation index of any one production index, an industrial production flow associated with the evaluation index, and a weight of any one evaluation index; extracting production data of an industrial production process from the whole-process production data; scoring any one evaluation index according to the production data and a preset scoring mechanism to obtain a first score, wherein the first score is the score of any one evaluation index; and calculating the first scores and the weights to obtain second scores, and determining a set of the second scores as an evaluation result, wherein the second scores are scores of any production index.
Optionally, the processing module is further configured to: the analysis and prediction model comprises an analysis model and a prediction model, the analysis and prediction model is used for carrying out analysis and prediction on the evaluation result and the whole-process production data, and the obtaining of the target production strategy of the production process to be adjusted comprises the following steps: responding to the evaluation result to determine that any one of the first scores is lower than a preset score threshold value, and determining that the industrial production process corresponding to any one of the first scores is the production process to be adjusted; extracting current production data of a production process to be adjusted from the whole-process production data; analyzing the current production data by utilizing an analysis model to obtain a second analysis result, wherein the second analysis result at least comprises a plurality of candidate production strategies of the production flow to be adjusted; calculating a plurality of candidate production strategies by using a prediction model to obtain a calculation result; based on the calculation result, a target production strategy is determined from the plurality of candidate production strategies.
Optionally, the processing module is further configured to: the second analysis result also comprises basic data of the production flow to be adjusted, abnormal phenomena, formation reasons of the abnormal phenomena and potential risks of the abnormal phenomena.
Optionally, the industrial production process adjusting device further includes: the execution module is used for making a scheduling list according to the target production strategy, wherein the scheduling list is used for determining the operation time of any group; and controlling the plurality of teams to perform the operation according to the scheduling table so as to execute the target production strategy.
Optionally, the industrial production process adjusting device further includes: the feedback module is used for recording the operation data of the operation device in the operation process of a plurality of teams; and generating a feedback report of the target production strategy based on the operation data, wherein the feedback report is used for determining the execution condition of the target production strategy.
According to still another aspect of the embodiments of the present invention, there is further provided a computer-readable storage medium, the computer-readable storage medium including a stored executable program, wherein the computer-readable storage medium is controlled to execute the industrial production process adjustment method of any one of the foregoing when the executable program runs.
According to still another aspect of the embodiment of the present invention, there is also provided an electronic device including: a memory storing an executable program; and the processor is used for running an executable program, wherein the executable program executes the industrial production flow adjustment method of any one of the above steps.
In the embodiment of the invention, the whole-flow production data of the industrial production process is firstly obtained, then the multi-level production index is evaluated based on the whole-flow production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining the production flow to be regulated, the analysis and prediction model is used for analyzing and predicting the evaluation result and the whole-flow production data to obtain a target production strategy of the production flow to be regulated, and finally the production flow to be regulated is regulated according to the target production strategy. The industrial production process is evaluated based on the multi-level production indexes constructed in a grading manner, the production flow to be adjusted is determined based on the evaluation result, and the target production strategy of the production flow to be adjusted is predicted by utilizing the analysis prediction model, so that the purpose of comprehensively evaluating and optimizing the industrial production process is achieved, the technical effects of improving the accuracy of the evaluation result of the industrial production flow and the industrial production efficiency are achieved, and the technical problems that the evaluation result accuracy is low due to the fact that the industrial production flow is evaluated based on the manual production experience in the prior art, and the production efficiency is low due to the fact that the industrial production flow is not adjusted timely are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a block diagram of a hardware architecture of an alternative mobile terminal for an industrial process flow adjustment method according to an embodiment of the present invention;
FIG. 2 is a flow chart of an industrial process flow adjustment method according to an embodiment of the present invention;
FIG. 3 is a flow chart of a production facility image evaluation system according to an embodiment of the present invention;
FIG. 4 is a flow chart of an industrial process flow adjustment process according to an embodiment of the present invention;
FIG. 5 is a block diagram of an alternative industrial process flow adjustment device according to an embodiment of the present invention;
FIG. 6 is a block diagram of an alternative industrial process flow adjustment device according to an embodiment of the present invention;
FIG. 7 is a block diagram of yet another alternative industrial process flow adjustment device according to an embodiment of the present invention;
Fig. 8 is a block diagram of still another alternative industrial process flow adjustment device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present invention, there is provided a method embodiment of an industrial process flow adjustment method, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
Fig. 1 is a block diagram of the hardware architecture of an alternative mobile terminal for an industrial process flow adjustment method according to an embodiment of the present invention, as shown in fig. 1, the mobile terminal 10 (or mobile device 10) may include one or more processors 102 (the processors 102 may include, but are not limited to, a microprocessor (Microcontroller Unit, MCU) or a processing device such as a programmable logic device (Field Programmable GATE ARRAY, FPGA)), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, the method may further include: display device 110, input/output device 108 (i.e., I/O device), universal serial bus (Universal Serial Bus, USB) port (which may be included as one of the ports of a computer bus, not shown), network interface (not shown), power supply (not shown), and/or camera (not shown). It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the mobile terminal 10 described above. For example, the mobile terminal 10 may also include more or fewer components than shown in FIG. 1 or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors 102 and/or other data processing circuits described above may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single stand-alone processing module or incorporated, in whole or in part, into any of the other elements in the mobile terminal 10 (or mobile device).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the industrial process adjustment method in the embodiment of the present invention, and the processor 102 executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, that is, implement the industrial process adjustment method described above. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. The specific examples of networks described above may include wireless networks provided by the communication provider of the mobile terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
In the above-mentioned operating environment, the embodiment of the present invention provides an industrial process flow adjustment method as shown in fig. 2, and fig. 2 is a flowchart of an industrial process flow adjustment method according to an embodiment of the present invention, as shown in fig. 2, and the method includes the following implementation steps:
step S201, acquiring whole-flow production data of an industrial production process;
step S202, evaluating the multi-level production indexes based on the whole-process production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining the production process to be adjusted;
Step S203, analyzing and predicting the evaluation result and the whole-process production data by using an analysis and prediction model to obtain a target production strategy of the production process to be adjusted;
Step S204, the production flow to be adjusted is adjusted according to the target production strategy.
As an alternative embodiment, the industrial process may be an automotive process, and the process flow involved in the automotive process may include, but is not limited to: sheet metal machining, engine manufacturing, automotive assembly, paint spraying, quality inspection, full process production data may include, but is not limited to: component data (types of raw materials, lot numbers, etc.), assembly data (component mounting records, tightening torque data, etc.), quality inspection data (quality inspection records, yield, etc.), environmental data (temperature, humidity, etc.).
The evaluation algorithm may include, but is not limited to: analytic hierarchy process, superior-inferior solution distance method (Technique for Order of Preference by Similarity to an Ideal Solution, TOPSIS), entropy weight method, coefficient of variation method, principal component analysis method, and weighted average. The multi-level production index can be a production index of a plurality of levels constructed from top to bottom based on the images of the multi-level manager and the whole-flow production sequence, and the production index of each level can be used for representing the production target of the level manager.
The analysis prediction model may include an analysis model and a prediction model, wherein the analysis model may be used to analyze the evaluation result and the whole-process production data to determine candidate production strategies of the production process to be adjusted, and the prediction model may be used to predict an execution result of each candidate production strategy, so that a target production strategy conforming to the multi-level production target may be selected from the plurality of candidate production strategies by comparing the execution results of the plurality of candidate strategies.
In the embodiment of the invention, the whole-flow production data of the industrial production process is firstly obtained, then the multi-level production index is evaluated based on the whole-flow production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining the production flow to be regulated, the analysis and prediction model is used for analyzing and predicting the evaluation result and the whole-flow production data to obtain a target production strategy of the production flow to be regulated, and finally the production flow to be regulated is regulated according to the target production strategy. The industrial production process is evaluated based on the multi-level production indexes constructed in a grading manner, the production flow to be adjusted is determined based on the evaluation result, and the target production strategy of the production flow to be adjusted is predicted by utilizing the analysis prediction model, so that the purpose of comprehensively evaluating and optimizing the industrial production process is achieved, the technical effects of improving the accuracy of the evaluation result of the industrial production flow and the industrial production efficiency are achieved, and the technical problems that the evaluation result accuracy is low due to the fact that the industrial production flow is evaluated based on the manual production experience in the prior art, and the production efficiency is low due to the fact that the industrial production flow is not adjusted timely are solved.
The above-described methods of embodiments of the present invention are further described below.
In an alternative embodiment, the industrial process adjustment method further includes:
step S251, constructing a top-level production index of an industrial production process;
Step S252, the top production index is decomposed step by step from top to bottom according to the execution sequence of the production flow in the industrial production process and the images of the multi-level manager, so as to obtain the multi-level production index.
As an alternative embodiment, the multi-level production index may be constructed according to the attention of the multi-level manager of the enterprise to the production device, specifically, the construction of the top-level production index based on the operation condition of the production device of the enterprise leader attention group level includes: safety environmental protection index, automation index and industry competitiveness index; based on the production device operation condition of the whole factory area and/or the workshop section, the middle layer production index is constructed by the middle layer manager, which comprises the following steps: health index, efficacy index, environmental protection index; based on the enterprise low-level manager paying attention to the operation condition of a certain production device, constructing a low-level production index includes: production device operation stability, alarm information, cooperative information and operation optimization information.
In the above alternative embodiment, the production index of the whole enterprise is decomposed step by step from top to bottom, and is refined to the production elements such as the production device, the operation post and the like, so that the accuracy of the production index is improved, the hierarchical management of the industrial production flow is facilitated, and the industrial production efficiency is improved.
In an alternative embodiment, in step S202, the evaluation result includes any score of the production index, and the evaluating the multi-level production index based on the full-process production data and the evaluation algorithm, to obtain the evaluation result includes:
Step S221, analyzing any production index by using an evaluation algorithm to obtain a first analysis result, wherein the first analysis result at least comprises: an evaluation index of any one production index, an industrial production flow associated with the evaluation index, and a weight of any one evaluation index;
Step S222, extracting production data of an industrial production process from the whole process production data;
step S223, scoring any one evaluation index according to the production data and a preset scoring mechanism to obtain a first score, wherein the first score is the score of any one evaluation index;
Step S224, calculating a plurality of first scores and weights to obtain second scores, and determining a set of the second scores as evaluation results, wherein the second scores are scores of any production index.
The above method is further described below in conjunction with fig. 3.
FIG. 3 is a flowchart of a production device image evaluation system according to an embodiment of the present invention, and as shown in FIG. 3, a production data fusion platform is established based on multidimensional, full-flow production data, and the production data fusion platform includes: the device portrait evaluation system is constructed based on multi-level production indexes determined by production targets of multi-level manager portraits and based on industrial production processes (working condition monitoring, operation guiding, abnormality early warning and other production processes), when the scores of any one production index are determined, target monitoring data can be obtained, the target monitoring data comprise production data obtained by monitoring all production devices in the industrial production process from bottom to top, the managers of different levels can score the evaluation indexes according to the production data related to the evaluation indexes of the production indexes of the target levels, and then the scores (namely, the first scores) of a plurality of evaluation indexes of one production index and the corresponding weights are weighted and averaged to obtain the scores (namely, the second scores) of the production indexes.
As an alternative embodiment, assuming that a set of multiple evaluation indexes of the safety environmental protection index is { a 1、a2、a3、…、an }, and a set of weights is { k 1、k2、k3、…、kn }, performing weighted average calculation on the multiple evaluation indexes and the corresponding weights to obtain a score s of the safety environmental protection index may be shown in the following formula (1):
The optional embodiment realizes the bottom-up monitoring of the production device, and realizes the top-down tracing of the production data through the multi-level manager and the multi-level generation index, thereby realizing the top-down and hierarchical management of the industrial production process and improving the management efficiency of the industrial production process.
In an alternative embodiment, in step S203, the analyzing and predicting model includes an analyzing and predicting model, and using the analyzing and predicting model to analyze and predict the evaluation result and the whole-process production data, the obtaining the target production strategy of the production process to be adjusted includes:
Step S231, determining that any one of the first scores is lower than a preset score threshold value in response to the evaluation result, and determining that the industrial production process corresponding to any one of the first scores is a production process to be adjusted;
Step S232, extracting current production data of a production process to be adjusted from the whole process production data;
Step S233, analyzing the current production data by utilizing an analysis model to obtain a second analysis result, wherein the second analysis result at least comprises a plurality of candidate production strategies of the production flow to be adjusted;
step S234, calculating a plurality of candidate production strategies by using a prediction model to obtain a calculation result;
step S235, determining a target production strategy from a plurality of candidate production strategies based on the calculation result.
As an optional implementation manner, one of the evaluation indexes of the safety environmental protection index is temperature, and when the temperature value in the acquired whole-process production data exceeds a preset temperature threshold value, the production process related to the safety environmental protection index in the industrial production process of the enterprise is determined to be the production process to be adjusted. It should be noted that, the multi-level production index and the evaluation index of each production index may be displayed on the target display device, and when it is determined that a certain evaluation index is abnormal, the evaluation index and the production index related to the evaluation index may be marked with different colors, where different colors may be used to characterize different risk levels of abnormal data, for example: the red mark represents high risk level, the yellow mark represents low risk level, and the green mark represents normal data and no risk.
Analyzing the current production data by using an analysis model to obtain a second analysis result, wherein the specific method comprises the following steps: analyzing the collected whole-flow production data by using an analysis algorithm to realize the analysis processes of the operation condition analysis, the early warning analysis, the operation guidance analysis and the like of the production device, and presetting a production experience knowledge base constructed based on a knowledge graph in an analysis model, wherein the production experience knowledge base can comprise abnormal production flows in the historical production process and abnormal solving measures adopted for the abnormal production flows, so that the solving measures corresponding to the historical abnormal phenomena of the production flows to be adjusted in the production experience knowledge base can be determined as candidate production strategies.
As another alternative, the analysis model may be trained based on an inference model, which may be a neural network model or a model containing a target inference algorithm (PATHRANKING algorithm), to promote the knowledge inference and update capabilities of the analysis model.
In the technical scheme provided by the invention, the prediction model can be a combined model of a high-dimensional dimension reduction model and a time sequence prediction model, and a plurality of candidate production strategies are calculated by using the prediction model to obtain a calculation result, and the specific method can be as follows: and (3) pre-constructing an optimization target and constraint conditions of the production strategy, and adjusting parameter values of production parameters in the candidate production strategy in the calculation process to obtain the optimal target production strategy. For example: the optimization target is that the total energy consumption in the production process is the lowest, the constraint condition is that the data such as the running efficiency, the efficiency and the product yield of each production device are in a corresponding preset range, and in the prediction calculation process, the parameter values of the production parameters of each production device are adjusted, so that a group of production parameters corresponding to the lowest total energy consumption obtained through calculation after multiple adjustment are determined as a target production strategy.
As yet another alternative embodiment, taking the example of predicting and determining the optimal load distribution of the electrolytic cells, determining the total production load based on the demand amount of the downstream chlorine in the production scheduling demand, calculating the total current according to the total production load, wherein the optimization target is that the total energy consumption of the production process is the lowest, and the constraint condition comprises the cell temperature and voltage of each electrolytic cell, and the process of optimally distributing the load of each electrolytic cell is as follows:
(1) When the downstream demand (for example, caustic soda amount, chlorine amount) changes, the total load of the production device is adjusted, and the load of each electrolytic cell is adjusted in turn, in the process, the optimal load distribution can be performed according to the current efficiency of each electrolytic cell based on the principle that the load is high due to high current efficiency, that is, the current value of each electrolytic cell is set.
(2) When the total load of the production device is unchanged, production parameters such as current efficiency, voltage and the like are monitored in real time, and when the performance of a plurality of electrolytic tanks is determined to be changed according to the change of the production parameters, the loads of the electrolytic tanks are optimally distributed based on the voltage and the current efficiency of the electrolytic tanks so as to reduce the total energy consumption in the production process.
In the above alternative embodiment, it should be noted that the current efficiency of the electrolytic cell is divided into the cathode current efficiency and the anode current efficiency, and the calculation of the anode current efficiency is complicated but the accuracy of the prototype current efficiency is higher than the cathode current efficiency. For a certain enterprise, the cathode current efficiency can be characterized by using the cathode caustic soda amount, and the calculation process of the cathode caustic soda amount can be shown in the following formula (2):
In the above formula (2), CE is the cathode caustic soda amount (in percent form); m NaOH represents the average production of caustic soda (in tons/day) within 24 hours of statistical time t, which can be measured by an on-line flow and density recorder of caustic soda; k is the electrochemical equivalent of caustic soda, and the numerical value is 1.4923; i represents average load running current (in kiloamperes) within 24 hours of statistical time t; n represents the total number of cells (in units of one) of the cell units.
In the above-mentioned alternative embodiment, the to-be-adjusted production flow corresponding to the abnormal production index is rapidly determined based on the evaluation result, and then the plurality of candidate production strategies are determined by using the analysis model including the production experience knowledge, so that the prediction model accurately determines the target production strategy from the plurality of candidate production strategies, thereby realizing the deep fusion of the production experience and the model, improving the accuracy of the target production strategy, and further being beneficial to improving the subsequent industrial production efficiency.
In an alternative embodiment, in step S233, the second analysis result further includes the basic data of the production process to be adjusted, the anomaly, the formation cause of the anomaly, and the potential risk of the anomaly.
The base data may include, but is not limited to: production device related to production flow to be adjusted, and current production data of the production device. As an alternative implementation mode, the basic data is that the cell voltage value exceeds 100 volts, the abnormal phenomenon is that the cell voltage is rapidly increased, the abnormal phenomenon is formed due to low concentration of catholyte, the potential risk of the abnormal phenomenon is that an ion membrane leaks, and the abnormal solving measure can be determined to improve the cathode and anode flow to a normal value by searching a production experience knowledge base.
In an alternative embodiment, the industrial process adjustment method further includes:
Step S261, a scheduling list is formulated according to a target production strategy, wherein the scheduling list is used for determining the operation time of any group;
In step S262, the plurality of teams are controlled to perform the job according to the scheduling table to execute the target production strategy.
After determining a target production strategy of a production flow to be adjusted, carrying out a scheduling decision of a production task based on the target production strategy, wherein the scheduling decision can be divided into an unmanned decision and a manned decision, and the unmanned decision can be a task scheduling instruction generated by a control system based on the scheduling decision, and then executing the task scheduling instruction to execute the target production strategy. The someone decision may be that the producer is scheduled with instructions according to a production task scheduling schedule to control the production device to produce.
The above method is further described below in conjunction with fig. 4.
Fig. 4 is a flowchart of an industrial process flow adjustment procedure according to an embodiment of the present invention, as shown in fig. 4, a scheduling table may be formulated based on a non-sensory evaluation of a producer, and when performing task scheduling, as shown in table 1 below, after a task list is acquired from a group of an early shift to perform task scheduling, a group of a late shift performs group switching before a job, and after the task list is acquired, each group performs daily group task switching according to the scheduling table, so as to complete a production task.
TABLE 1
In an alternative embodiment, the industrial process adjustment method further includes:
step S271, recording operation data of the operation device during a plurality of team operations;
step S272, generating a feedback report of the target production strategy based on the operation data, wherein the feedback report is used to determine the execution of the target production strategy.
In the above alternative embodiment, workshops, time, shift, types, procedures, categories, work content and corresponding scoring criteria are imported into a production task scheduling system in advance, the scheduling system performs production task scheduling once before the shift between the early shift and the late shift, so as to scan a scoring table (as shown in table 2 below) of the current shift into a task queue, the current shift performs a scoring record on the task execution condition of the previous shift, and a manager of the current working device or the shift on the basis of the operation data of the working device scores the task responsible members, and the scoring result is fed back to the target device.
As an alternative embodiment, the task categories may be divided in advance, for example, the task categories include: daily, periodic, temporary. For each-day type task, scanning the task into a task queue every day to ensure the task to be executed on time, thereby ensuring the continuity of the subsequent production flow; for periodic class tasks, scanning the tasks into a task queue on the day of assigned task execution; for a temporary class of tasks (urgent tasks), the task implementation is added before all tasks currently being performed to meet production requirements.
TABLE 2
In the execution process of the target production strategy, whether each production flow in the execution process runs normally or not and whether the running result of each production flow meets the production target of the corresponding level can be determined by analyzing and evaluating the execution condition of each scheduling task.
The industrial production flow adjustment method provided by the embodiment of the invention achieves the following technical effects:
(1) Pre-abnormality early warning: based on the evaluation result of the multi-level production index, early warning is carried out on the abnormal production flow;
(2) And (3) detecting working conditions in the process: in the execution process of the target production strategy, detecting the production working condition of the target production flow in real time, and determining the target production strategy for adjusting the abnormal production flow according to the analysis model and the prediction model;
(3) In-the-event post-operation guidance: and carrying out multi-level evaluation on the production tasks in the execution process and after the execution of the target production strategy is finished, and carrying out real-time optimized production on the production device based on the evaluation result, thereby improving the yield of the production device and reducing the energy consumption of a single production device.
In this embodiment, an industrial production process adjusting device is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, and is not described in detail. As used below, a combination of software and/or hardware that belongs to a "module" may implement a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
FIG. 5 is a block diagram of an alternative industrial process flow adjustment device, according to an embodiment of the present invention, as shown in FIG. 5, comprising:
an acquisition module 501, configured to acquire full-flow production data of an industrial production process;
The evaluation module 502 is configured to evaluate the multi-level production index based on the whole-process production data and an evaluation algorithm to obtain an evaluation result, where the evaluation result is used to determine a production process to be adjusted;
the processing module 503 is configured to perform analysis and prediction on the evaluation result and the overall process production data by using an analysis and prediction model, so as to obtain a target production strategy of the production process to be adjusted;
The adjustment module 504 is configured to adjust the production flow to be adjusted according to the target production policy.
Alternatively, fig. 6 is a block diagram of another alternative industrial process flow adjustment device according to an embodiment of the present invention, and as shown in fig. 6, the device further includes, in addition to the above modules: a construction module 505 for constructing a top-level production index of an industrial process; and decomposing the top-layer production index step by step from top to bottom according to the execution sequence of the production flow in the industrial production process and the images of the multi-level manager to obtain the multi-level production index.
Optionally, the evaluation module 502 is further configured to: the evaluation result comprises any production index score, the multi-level production index is evaluated based on the whole-flow production data and an evaluation algorithm, and the evaluation result comprises the following steps: analyzing any production index by using an evaluation algorithm to obtain a first analysis result, wherein the first analysis result at least comprises: an evaluation index of any one production index, an industrial production flow associated with the evaluation index, and a weight of any one evaluation index; extracting production data of an industrial production process from the whole-process production data; scoring any one evaluation index according to the production data and a preset scoring mechanism to obtain a first score, wherein the first score is the score of any one evaluation index; and calculating the first scores and the weights to obtain second scores, and determining a set of the second scores as an evaluation result, wherein the second scores are scores of any production index.
Optionally, the processing module 503 is further configured to: the analysis and prediction model comprises an analysis model and a prediction model, the analysis and prediction model is used for carrying out analysis and prediction on the evaluation result and the whole-process production data, and the obtaining of the target production strategy of the production process to be adjusted comprises the following steps: responding to the evaluation result to determine that any one of the first scores is lower than a preset score threshold value, and determining that the industrial production process corresponding to any one of the first scores is the production process to be adjusted; extracting current production data of a production process to be adjusted from the whole-process production data; analyzing the current production data by utilizing an analysis model to obtain a second analysis result, wherein the second analysis result at least comprises a plurality of candidate production strategies of the production flow to be adjusted; calculating a plurality of candidate production strategies by using a prediction model to obtain a calculation result; based on the calculation result, a target production strategy is determined from the plurality of candidate production strategies.
Optionally, the processing module 503 is further configured to: the second analysis result also comprises basic data of the production flow to be adjusted, abnormal phenomena, formation reasons of the abnormal phenomena and potential risks of the abnormal phenomena.
Alternatively, fig. 7 is a block diagram of still another alternative industrial process flow adjustment device according to an embodiment of the present invention, and as shown in fig. 7, the device further includes, in addition to the above modules: an execution module 506, configured to formulate a scheduling table according to a target production policy, where the scheduling table is used to determine a job time of any one team; and controlling the plurality of teams to perform the operation according to the scheduling table so as to execute the target production strategy.
Optionally, fig. 8 is a block diagram of still another alternative industrial process flow adjustment device according to an embodiment of the present invention, and as shown in fig. 8, the device further includes, in addition to the above modules: the feedback module 507 is configured to record operation data of the operation device during the operation of the plurality of teams; and generating a feedback report of the target production strategy based on the operation data, wherein the feedback report is used for determining the execution condition of the target production strategy.
It should be noted that each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; or the above modules may be located in different processors in any combination.
According to still another aspect of the embodiments of the present invention, there is further provided a computer-readable storage medium, the computer-readable storage medium including a stored executable program, wherein the computer-readable storage medium is controlled to execute the industrial production process adjustment method of any one of the foregoing when the executable program runs.
Alternatively, in the present embodiment, the above-described computer-readable storage medium may be configured to store a computer program for performing the steps of:
Step S1, acquiring whole-flow production data of an industrial production process;
step S2, evaluating the multi-level production indexes based on the whole-process production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining the production process to be adjusted;
S3, analyzing and predicting the evaluation result and the whole-process production data by using an analysis and prediction model to obtain a target production strategy of the production process to be adjusted;
and S4, adjusting the production flow to be adjusted according to the target production strategy.
Alternatively, in the present embodiment, the above-described computer-readable storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media in which a computer program can be stored.
According to still another aspect of the embodiment of the present invention, there is also provided an electronic device including: a memory storing an executable program; and the processor is used for running an executable program, wherein the executable program executes the industrial production flow adjustment method of any one of the above steps.
Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
Step S1, acquiring whole-flow production data of an industrial production process;
step S2, evaluating the multi-level production indexes based on the whole-process production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining the production process to be adjusted;
S3, analyzing and predicting the evaluation result and the whole-process production data by using an analysis and prediction model to obtain a target production strategy of the production process to be adjusted;
and S4, adjusting the production flow to be adjusted according to the target production strategy.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations thereof, and this embodiment is not described herein.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present invention, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. An industrial process flow adjustment method, comprising:
Acquiring whole-flow production data of an industrial production process;
evaluating the multi-level production indexes based on the whole-process production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining a production process to be adjusted;
Analyzing and predicting the evaluation result and the whole-process production data by using an analysis and prediction model to obtain a target production strategy of the production process to be adjusted;
and adjusting the production flow to be adjusted according to the target production strategy.
2. The industrial process adjustment method according to claim 1, characterized in that the method further comprises:
Constructing a top-level production index of the industrial process;
and decomposing the top-layer production index step by step from top to bottom according to the execution sequence of the production flow in the industrial production process and the images of the multi-level manager to obtain the multi-level production index.
3. The industrial process adjustment method according to claim 1, wherein the evaluation result includes a score of any one production index, evaluating the multi-level production index based on the full-process production data and the evaluation algorithm, and obtaining the evaluation result includes:
Analyzing any production index by using the evaluation algorithm to obtain a first analysis result, wherein the first analysis result at least comprises: the evaluation index of any production index, the industrial production flow associated with the evaluation index, and the weight of any evaluation index;
Extracting production data of the industrial production process from the full-process production data;
scoring any one of the evaluation indexes according to the production data and a preset scoring mechanism to obtain a first score, wherein the first score is the score of any one of the evaluation indexes;
And calculating a plurality of first scores and the weights to obtain second scores, and determining a set of the second scores as the evaluation result, wherein the second scores are scores of any production index.
4. The industrial process adjustment method according to claim 3, wherein the analysis prediction model includes an analysis model and a prediction model, and the analyzing and predicting the evaluation result and the full-process production data by using the analysis prediction model to obtain the target production strategy of the process to be adjusted includes:
Responding to the evaluation result to determine that any one first score is lower than a preset score threshold value, and determining that the industrial production process corresponding to any one first score is the production process to be adjusted;
Extracting current production data of the production process to be adjusted from the whole-process production data;
analyzing the current production data by utilizing the analysis model to obtain a second analysis result, wherein the second analysis result at least comprises a plurality of candidate production strategies of the production flow to be adjusted;
Calculating the plurality of candidate production strategies by using the prediction model to obtain a calculation result;
based on the calculation result, the target production strategy is determined from the plurality of candidate production strategies.
5. The industrial process adjustment method according to claim 4, wherein the second analysis result further includes basic data of the process to be adjusted, an anomaly, a cause of formation of the anomaly, and a potential risk of the anomaly.
6. The industrial process adjustment method according to claim 1, characterized in that the method further comprises:
A scheduling table is formulated according to the target production strategy, wherein the scheduling table is used for determining the operation time of any group;
and controlling a plurality of teams to operate according to the scheduling table so as to execute the target production strategy.
7. The industrial process adjustment method according to claim 6, characterized in that the method further comprises:
Recording operation data of the operation device in the process of the plurality of teams;
and generating a feedback report of the target production strategy based on the operation data, wherein the feedback report is used for determining the execution condition of the target production strategy.
8. An industrial process flow adjustment device, comprising:
the acquisition module is used for acquiring the whole-flow production data of the industrial production process;
the evaluation module is used for evaluating the multi-level production indexes based on the whole-process production data and an evaluation algorithm to obtain an evaluation result, wherein the evaluation result is used for determining the production process to be adjusted;
the processing module is used for analyzing and predicting the evaluation result and the whole-flow production data by utilizing an analysis and prediction model to obtain a target production strategy of the production flow to be adjusted;
And the adjusting module is used for adjusting the production flow to be adjusted according to the target production strategy.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored executable program, wherein the executable program, when run, controls a device in which the computer-readable storage medium is located to perform the industrial production process adjustment method according to any one of claims 1 to 7.
10. An electronic device, comprising:
A memory storing an executable program;
A processor for executing the executable program, wherein the executable program executes the industrial process adjustment method according to any one of claims 1 to 7 when executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410194256.4A CN118052403A (en) | 2024-02-21 | 2024-02-21 | Industrial production flow adjustment method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410194256.4A CN118052403A (en) | 2024-02-21 | 2024-02-21 | Industrial production flow adjustment method and device, storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118052403A true CN118052403A (en) | 2024-05-17 |
Family
ID=91044178
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410194256.4A Pending CN118052403A (en) | 2024-02-21 | 2024-02-21 | Industrial production flow adjustment method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118052403A (en) |
-
2024
- 2024-02-21 CN CN202410194256.4A patent/CN118052403A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110320892B (en) | Sewage treatment equipment fault diagnosis system and method based on L asso regression | |
| US10223167B2 (en) | Discrete resource management | |
| CN111898873A (en) | Group company carbon emission early warning information system and early warning method thereof | |
| CN113708493B (en) | Cloud edge cooperation-based power distribution terminal operation and maintenance method and device and computer equipment | |
| CN105467946A (en) | Aluminum electrolytic MES system based on accurate perception and intelligent decision | |
| CN107122832A (en) | A kind of manufacture system preventative maintenance method of Quality Control And Reliability analysis | |
| CN117389236B (en) | Propylene oxide production process optimization method and system | |
| CN114169254A (en) | Abnormal energy consumption diagnosis method and system based on short-term building energy consumption prediction model | |
| CN105467945A (en) | Expert system based on electrolytic tank real-time production data | |
| CN117393076B (en) | Intelligent monitoring method and system for heat-resistant epoxy resin production process | |
| CN111522864B (en) | Enterprise production mode recognition and transfer production early warning method based on electricity consumption data | |
| CN117454093A (en) | Intelligent power module management method and system | |
| CN117411115A (en) | Detection control method and device for energy storage power supply state, electronic equipment and medium | |
| CN115407712A (en) | Intelligent maintenance system for hydraulic station of steel mill and working process | |
| Bao et al. | Wind turbine condition monitoring based on improved active learning strategy and KNN algorithm | |
| CN117114446A (en) | Device health assessment method based on data driving | |
| CN112418662A (en) | Power distribution network operation reliability analysis method using artificial neural network | |
| CN118052403A (en) | Industrial production flow adjustment method and device, storage medium and electronic equipment | |
| CN113537681A (en) | Method and system for refining enterprise equipment management informatization | |
| CN109978299B (en) | Data analysis method and device for offshore wind power business and storage medium | |
| Vermeer et al. | The application of health-and risk indices as a decision-support tool for utilities | |
| CN107955951B (en) | Method for managing full life cycle of electrolytic aluminum | |
| Sun et al. | Diagnosis and decision analysis system of high temperature molten aluminum cell based on neural network | |
| CN117932976B (en) | Method and device for acquiring process machine set energy data | |
| Shang et al. | Generation Mode Analysis of General Quality Characteristic of Ship Power System |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |