CN118052425A - Production report management method and system - Google Patents
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Abstract
The invention discloses a production report management method and a system, which relate to the technical field of report management and comprise the steps of selecting a production task in a system and automatically recommending a procedure scheme by the system; setting a reporting node according to the production flow, and setting a reporting step aiming at a key procedure to form a reporting rule; determining a scheme comprising key working procedures according to the report rule, and intelligently selecting the working procedure scheme with the highest grading; the system calls standard technological parameters in a knowledge base and automatically generates a procedure card and a procedure file; when a worker starts to execute the operation, the system starts to monitor and record in real time and performs predictive maintenance; after the operation is finished, the system automatically checks the report data and gives out report effect evaluation. The invention has important significance for optimizing the whole production process, ensuring the product quality, reducing the cost and enhancing the efficiency and improving the production intelligence level.
Description
Technical Field
The invention relates to the technical field of newspaper work management, in particular to a production newspaper work management method and system.
Background
In the field of modern production management, accurate and efficient labor management is a key factor for improving production efficiency and quality. In recent years, with the rapid development of information technology, especially the progress of data processing and machine learning technologies, production report management methods have gradually changed from traditional manual recording to automated and intelligent systems. The system can monitor the production flow in real time, automatically record the key procedure information and provide support for decision making based on big data analysis. However, despite the significant advances in automation and data analysis of the prior art, there are some shortcomings.
First, existing report management methods still face challenges when dealing with complex production tasks. For example, in a diverse production environment, how to quickly and accurately select the most suitable process scheme and how to flexibly adjust the production flow to cope with the emergency is still a problem that is difficult to solve in the prior art. In addition, existing systems often lack sufficient intelligence in terms of predictive maintenance to effectively predict equipment failure and production breaks, thereby failing to maximize production efficiency and reduce downtime. These deficiencies limit the optimization and efficiency improvement of the production process.
Disclosure of Invention
The present invention has been made in view of the above-described problems occurring in the conventional report management method.
The problem underlying the present invention is therefore to provide a method for increasing the production efficiency and reducing the risk of production interruptions due to equipment malfunctions or operational errors.
In order to solve the technical problems, the invention provides the following technical scheme:
In a first aspect, an embodiment of the present invention provides a method for managing production report, including selecting a production task in a system, and automatically recommending a process scheme by the system; setting a reporting node according to the production flow, and setting a reporting step aiming at a key procedure to form a reporting rule; determining a scheme comprising key working procedures according to the report rule, and intelligently selecting the working procedure scheme with the highest grading; the system calls standard technological parameters in a knowledge base and automatically generates a procedure card and a procedure file; when a worker starts to execute the operation, the system starts to monitor and record in real time and performs predictive maintenance; after the operation is finished, the system automatically checks the report data and gives out report effect evaluation.
As a preferable scheme of the production report management method, the invention comprises the following steps: the production task is selected in the system, and the automatic recommended procedure scheme of the system comprises the following steps: integrating a cost prediction model, a productivity assessment model and an equipment compatibility model, and carrying out multidimensional analysis and scoring on each process scheme;
The cost prediction model includes:
；
Wherein, C is the total cost, Unit cost and quantity of raw materials, manpower and energy respectively,/>Is a risk factor,/>Is an environmental impact factor; the productivity assessment model includes:
；
wherein P is the productivity, O and T are the output and the production period respectively, For the efficiency of the device,/>Is a possible delay factor; the device compatibility model includes:
；
wherein D is the adjusted equipment compatibility, Weights and scores for each criterion,/>Factors are updated for the technology.
As a preferable scheme of the production report management method, the invention comprises the following steps: the integrated cost prediction model, the productivity assessment model and the equipment compatibility model are used for obtaining a comprehensive scoring model as follows:
；
wherein, Weights for each model,/>As intercept term C, P, D is the score of the corresponding model,/>Weighted for environmental sustainability,/>Scoring environmental sustainability,/>And Q is the weight of the product quality, and Q is the product quality score.
As a preferable scheme of the production report management method, the invention comprises the following steps: the weight of each model is set as follows: collecting detailed historical data including cost C, capacity P, equipment compatibility D, environmental sustainability scorePerforming feature engineering on the product quality score Q and the comprehensive score S; the overfitting was controlled using a modified regression method with regularization term, as follows:
；
Wherein n is the number of samples; m is the number of features; For the actual value of the ith sample,/> Is an intercept term; /(I)Is the coefficient of the j-th feature,/>For the j-th eigenvalue of the i-th sample, λ is the regularization parameter; searching for an optimal lambda value using a random search; the composite score S is calculated using the optimized model parameter weights and lambda values.
As a preferable scheme of the production report management method, the invention comprises the following steps: the report rule comprises the steps of classifying product types into a plurality of types, wherein a first type of product comprises 3 types, namely a first type, a second type and a third type; the first model is required to be produced in a self-defining mode according to special requirements, and a special process flow A and a quality standard X are used; the second model and the third model are standardized production, using a general process flow B; the second class of products comprises a plurality of models; the first model and the second model belong to small-batch customized products, and a nonstandard process flow C and a quality standard Y are required to be used; the third model and the fourth model belong to a large number of standard products, and an optimized general process flow D is used; the third type of products belongs to an updated version of the old products, and needs to refer to the original process flow E, update part of working procedures, add special function tests and use the updated process flow F and quality standard Z; the fourth type of product comprises a plurality of models, the modular design and production are adopted, the modules use a general process module G, and finally the general assembly process flow H is used for assembly.
As a preferable scheme of the production report management method, the invention comprises the following steps: the report effect evaluation comprises the following steps: the report interval time is predicted and analyzed, and the formula is as follows:
；
where n is the moving average window size X i is the observed value at the ith time point, Is a weight; a weighted moving average for each time point is calculated using a formula, the trend of the moving average is analyzed, and the pattern or periodicity is identified: if the WMA value shows an ascending trend along with time, the reporting interval time is increased, which means that the working efficiency is reduced or a bottleneck exists in the production flow; if the WMA value shows a decreasing trend with time, it indicates that the reporting interval time is decreasing, which indicates an increase in production efficiency or workflow optimization; if the WMA value fluctuation exceeds the threshold value, the reporting interval time is unstable, and an uncertain factor or abnormal condition is generated; if the WMA value is relatively stable, i.e. the fluctuation does not exceed the threshold value, the reporting interval time is relatively stable, and the production process is more predictable and controllable.
In a second aspect, the present invention provides a production report management system, which further solves the problems existing in the existing report management method, and the embodiment includes: the production task selection module is used for selecting production tasks in the system and automatically recommending a process scheme; the report rule setting module is used for setting report nodes according to the production flow and defining specific report steps aiming at key procedures; the process scheme optimizing module is used for intelligently selecting the process scheme with the highest score according to the report rule and the comprehensive score; the process parameter management module is used for calling standard process parameters in the knowledge base and automatically generating a process card and a process file; the real-time monitoring and predictive maintenance module is used for starting real-time monitoring and recording when a worker starts to execute operation; and the report data checking and evaluating module is used for automatically checking the report data after the operation is finished and evaluating the report effect.
The invention has the beneficial effects that the invention realizes the real-time monitoring, prediction and optimization of the whole process, ensures that the production process is highly transparent, is beneficial to finding problems, early warning in advance, and actively optimizes, and keeps the production in a controllable state; the standardized report rule template and the automatically generated procedure file ensure the process consistency among different product batches and the stability of quality; the optimal working procedure scheme can be intelligently selected, the failure rate is reduced through predictive maintenance, and unnecessary production loss and cost expenditure are effectively reduced; the standard reporting process reduces reworking and debugging, the intelligent scheme selection shortens the manufacturing period, and the production efficiency is comprehensively improved; the invention has important significance for optimizing the whole production process, ensuring the product quality, reducing the cost and enhancing the efficiency and improving the production intelligence level.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of the production report management method in embodiment 1.
Fig. 2 is a flowchart of the report effect evaluation in embodiment 1.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 and 2, a first embodiment of the present invention provides a production report management method, which includes the following steps:
s1: and selecting a production task in the system, and automatically recommending a procedure scheme by the system.
Preferably, a plurality of process scheme templates and the analysis data of the advantages and disadvantages of different schemes are stored in a process knowledge base; the cost prediction model, the productivity assessment model and the equipment compatibility model are integrated, multidimensional analysis and grading are carried out on each process scheme, the system can comprehensively consider the conditions of targets, resource constraints and the like of production tasks, and the process scheme with the greatest advantage is intelligently screened out, so that indexes such as raw material cost, production period, product quality and the like of the scheme are all optimal solutions.
Further, the cost prediction model, the productivity assessment model and the equipment compatibility model are integrated, and multidimensional analysis and scoring are performed on each process scheme as follows: the cost prediction model includes:
；
Wherein, C is the total cost, Unit cost and quantity of raw materials, manpower and energy respectively,/>Is a risk factor,/>Is an environmental impact factor.
The capacity assessment model includes:
；
wherein P is the productivity, O and T are the output and the production period respectively, For the efficiency of the device,/>Is a possible delay factor.
The device compatibility model includes:
；
wherein D is the adjusted equipment compatibility, Weights and scores for each criterion,/>Factors are updated for the technology.
Comprehensive scoring model:
；
wherein, Weights for each model,/>As intercept term C, P, D is the score of the corresponding model,/>Weighted for environmental sustainability,/>Scoring environmental sustainability,/>And Q is the weight of the product quality, and Q is the product quality score.
Further, the weight setting process of each model is as follows:
collecting detailed historical data including cost C, capacity P, equipment compatibility D, environmental sustainability score Product quality score Q and comprehensive score S; performing feature engineering, including standardization, normalization, missing value processing and the like; the overfitting was controlled using a modified regression method with regularization term, as follows:
；
Wherein n is the number of samples; m is the number of features; For the actual value of the ith sample,/> Is an intercept term; /(I)Is the coefficient of the j-th feature,/>For the j-th eigenvalue of the i-th sample, λ is the regularization parameter.
And (3) performing super-parameter optimization: searching for an optimal lambda value using a random search; the composite score S is calculated using the optimized model parameter weights and lambda values.
S2: and setting a reporting node according to the production flow, and setting a reporting step aiming at the key procedure to form a reporting rule.
S2.1: and setting a reporting step aiming at the key working procedure to form a reporting rule.
Combining the production flow chart and time dynamic analysis, deeply analyzing the operation content and time efficiency of each process, and determining a key process and a secondary process; setting reporting nodes in key working procedures such as feeding, processing, measuring, packaging and the like, and simultaneously, setting simplified reporting nodes in secondary working procedures to reduce operation interruption; determining which processes are more likely to be problematic or delayed based on historical data analysis, and increasing the monitoring and reporting requirements of the processes; and setting detailed report contents at the determined report nodes, wherein the report contents comprise report time, report personnel, product information, process information, equipment state, material consumption and the like.
Generating a report rule template according to the report nodes and report contents, and defining report information which needs to be filled in by each report node in the template; generating corresponding report rules according to different product models and process routes: the product types are divided into a plurality of types, and for the first type of product, 3 types are included, namely a first type, a second type and a third type; the first model is required to be produced in a self-defining mode according to special requirements, and a special technological process A and a quality standard X are used; the second model and the third model are standardized production, using general process flow B.
The second class of products contains 4 models, namely a first model to a fourth model; the first model and the second model belong to small-batch customized products, and a nonstandard process flow C and a quality standard Y are required to be used; the third model and the fourth model belong to a large-batch standard product, and an optimized general process flow D is used.
The third type of products only has one model, belongs to the updated version of the old products, needs to refer to the original process flow E, updates part of working procedures, adds special function tests, and uses the updated process flow F and the quality standard Z.
The fourth class of products comprises 4 models, all adopt modularized design and production, the modules use a general process module G, and finally the modules are assembled and use a general assembly process flow H.
S2.2: the reporting rule eliminates unnecessary repeated reporting points and optimizes the reporting flow.
Specifically, the process of eliminating unnecessary repeated reporting points is as follows: collecting all the original report data in the past year, including report time, place, content and other information; preprocessing the collected report original data, such as cleaning error data, standardizing formats, complementing missing values and the like, and processing the data into a model usable format; extracting features by analyzing the report data, and marking whether each report point is repeated or not; training a repeated report detection model by using an SVM model; aiming at the repeated report detection effect of the model, parameter adjustment is optimized to improve the accuracy, and the model with the lowest repetition rate is the final model; and integrating the trained repeated report point detection model into a report management system.
S3: and determining a scheme comprising key working procedures according to the report rule, and intelligently selecting the working procedure scheme with the highest grading.
Preferably, the report rule of the product is collected, and the raw material costs m1 and m2, the labor costs l1 and l2 and the energy costs e1 and e2 of the scheme 1 and the scheme 2 are collected; calculating a risk factor r1 of the scheme 1 to obtain an evaluation result 1; the risk factor r2 of the scheme 2 obtains an evaluation result 2; calculating an environmental impact factor v1 of the scheme 1 to obtain an analysis result 1; calculating an environmental impact factor v2 of the scheme 2 to obtain an analysis result 2; substituting each factor into the cost prediction model C to calculate the comprehensive cost, and obtaining C (scheme 1) and C (scheme 2).
Capacity assessment model: analyzing the output o1 and o2 and the production periods t1 and t2 of the two schemes; the equipment efficiency u1 of different working procedures is evaluated to obtain a detection result 1, u2 is evaluated to obtain a detection result 2, the possible delay probability P1 of two schemes is evaluated to obtain a statistical result 1, P2 is evaluated to obtain a statistical result 2, and data are substituted into the productivity evaluation model P to calculate productivity.
And sequentially calculating, and finally synthesizing a plurality of model quantitative calculation results to obtain a scoring magnitude relation.
S4: and (5) automatically generating a procedure card and a procedure file by using standard process parameters in a system call knowledge base.
S4.1: and constructing a standard technological parameter knowledge base.
Specifically, collecting process documents, historical procedure records and other data sources inside and outside industries; extracting process parameter information such as processing speed, feeding amount, clamp specification and the like; configuring a parameter structured template, and unifying data formats of different sources; and warehousing process parameters, and establishing an extensible standard process knowledge base.
S4.2: and (5) automatically generating a system in the design process.
Designing a process card and a process file data structure, wherein the process card and the process file data structure comprise the contents of process names, process parameters, quality requirements and the like; calling a knowledge base API to acquire standard technological parameters, organizing parameter contents, and rendering and generating a procedure card and a procedure file; and the configurable business rule is realized, and the process generation template and the process are controlled.
S4.3: using the identification code to associate the product with the corresponding process route scanning code or the input code in the knowledge base, and automatically extracting the process parameters from the knowledge base by the system; and one key automatically outputs matched procedure documents, supports process route version control and ensures that the latest process files are acquired.
S5: when a worker starts to execute the operation, the system starts to monitor and record in real time, and performs predictive maintenance.
S5.1: the real-time execution data structure is designed.
Determining a monitoring device: video, sensor, etc., defining a real-time recording data format: time, operator, process, equipment status, and yield; a storage database is constructed to accommodate the streaming data.
S4.2: real-time monitoring is carried out, a rule engine is set, abnormal states are identified, and early warning prompts are provided.
Further, the monitoring equipment is connected, multi-source data such as real-time images, audio frequency, sensing signals and the like are obtained, the data are analyzed, the execution state information is extracted, the procedure standard is compared, the execution state is packed, and the real-time record database is written.
S5.3: and integrating a prediction model and implementing predictive maintenance.
Training tool equipment based on a deep learning fault prediction model, inputting a real-time state, outputting fault probability, calling the model to predict equipment fault risks of subsequent operation, prompting workers to timely treat according to the risks, avoiding faults, continuously learning the prediction model, and improving prediction accuracy.
S6: after the operation is finished, the system automatically checks the report data and gives out report effect evaluation.
Specifically, all the report data of the current shift are extracted from the report system: the information comprises the time of reporting work, personnel, equipment, products, procedures and the like; checking whether the working procedure sequence and the time progress of the reporting work are correct or not by referring to the standard process flow, checking whether the reporting worker is matched with the corresponding operation post or not, and checking whether the product and the equipment information are matched or not; the formula for predicting and analyzing the reporting interval time is as follows:
；
Where e is the moving average window size X i is the observed value at the ith time point, Is a weight; the window size e of the moving average model is determined, a larger window may smooth out too much data, while a smaller window may result in the analysis result being too sensitive.
A weighted moving average for each time point is calculated using a formula, the trend of the moving average is analyzed, and possible patterns or periodicity are identified: if WMA t values show an upward trend over time, this may indicate that the reporting interval time is increasing, which may mean that the work efficiency is decreasing or that there is a bottleneck in the production flow; if WMA t values show a decreasing trend over time, this may indicate that the reporting interval time is decreasing, which is often an indication of increased production efficiency or workflow optimization; if the WMA t value fluctuates beyond the threshold, this may indicate that the reporting interval is unstable, possibly due to uncertainty factors or anomalies in the production process; if the WMA t value is relatively smooth, i.e. the fluctuation does not exceed the threshold, this generally means that the reporting interval is relatively stable and the production process may be more predictable and controllable.
Preferably, the result uses a chart to present the statistical result of the report data, indicates the evaluation result of the report effect and the proposal of the countermeasures, and outputs a report for reference of operators and a management layer.
The embodiment also provides a production report management system, which comprises a production task selection module, a production report management module and a production report management module, wherein the production task selection module is used for selecting production tasks in the system and automatically recommending a procedure scheme; the report rule setting module is used for setting report nodes according to the production flow and defining specific report steps aiming at key procedures; the process scheme optimizing module is used for intelligently selecting the process scheme with the highest score according to the report rule and the comprehensive score; the process parameter management module is used for calling standard process parameters in the knowledge base and automatically generating a process card and a process file; the real-time monitoring and predictive maintenance module is used for starting real-time monitoring and recording when a worker starts to execute operation; and the report data checking and evaluating module is used for automatically checking the report data after the operation is finished and evaluating the report effect.
The embodiment also provides a computer device, which is suitable for the situation of the production report management method, and includes: a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to implement the production report management method as proposed in the above embodiment.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the production report management method as set forth in the above embodiments; the storage medium may be implemented by any type or combination of volatile or nonvolatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In conclusion, the invention realizes the real-time monitoring, prediction and optimization of the whole process, ensures that the production process is highly transparent, is beneficial to finding problems, early warning and active optimization in advance, and keeps the production in a controllable state; the standardized report rule template and the automatically generated procedure file ensure the process consistency among different product batches and the stability of quality; the optimal working procedure scheme can be intelligently selected, the failure rate is reduced through predictive maintenance, and unnecessary production loss and cost expenditure are effectively reduced; the standard reporting process reduces reworking and debugging, the intelligent scheme selection shortens the manufacturing period, and the production efficiency is comprehensively improved; the invention has important significance for optimizing the whole production process, ensuring the product quality, reducing the cost and enhancing the efficiency and improving the production intelligence level.
Example 2
Referring to table 1, for the second embodiment of the present invention, comparative data of the production report management method and the prior art are presented for further verification of the advancement of the present invention.
In a machining workshop of an automobile part production enterprise, a plurality of working procedures of reporting work are required to be managed so as to improve the production efficiency, and the workshop mainly produces key parts such as an engine belt pulley and a shock absorber, and has two modes of standardized mass production and small-batch customized production.
The technical staff designs 4 types of report rule templates in the report management system according to different product models; the business personnel selects the current production task in the system, and the system can call a cost prediction model, a productivity evaluation model and the like to evaluate a plurality of optional process schemes and recommend the scheme with the highest score.
The system extracts a report rule template corresponding to the product type, a specific report scheme is automatically generated, when a worker executes each report node, report work is performed through the system, data are written into a database, the system performs predictive maintenance on tooling equipment to avoid machine failure, after production is finished, the system automatically checks the report work data, and the report work effect is evaluated, wherein the comparison experiment data of the invention and the prior art are as follows:
table 1 comparative data of the present invention with the prior art
It can be seen that the report management method of the invention is superior to the prior art method in terms of improving report accuracy, reducing failure rate, improving manufacturing efficiency, and the like.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (6)
1. A production report management method is characterized in that: comprising the following steps:
Selecting a production task in a system, and automatically recommending a procedure scheme by the system;
setting a reporting node according to the production flow, and setting a reporting step aiming at a key procedure to form a reporting rule;
Determining a scheme comprising key working procedures according to the report rule, and intelligently selecting the working procedure scheme with the highest grading;
The system calls standard technological parameters in a knowledge base and automatically generates a procedure card and a procedure file;
When a worker starts to execute the operation, the system starts to monitor and record in real time and performs predictive maintenance;
After the operation is finished, the system automatically checks the report data and gives out report effect evaluation;
The production task is selected in the system, and the automatic recommended procedure scheme of the system comprises the following steps:
integrating a cost prediction model, a productivity assessment model and an equipment compatibility model, and carrying out multidimensional analysis and scoring on each process scheme;
The cost prediction model includes:
；
Wherein, C is the total cost, Unit cost and quantity of raw materials, manpower and energy respectively,/>Is a risk factor,/>Is an environmental impact factor;
the productivity assessment model includes:
；
wherein P is the productivity, O and T are the output and the production period respectively, For the efficiency of the device,/>Is a delay factor;
The device compatibility model includes:
；
wherein D is the adjusted equipment compatibility, Weights and scores for each criterion,/>Factors are updated for the technology.
2. The production report process management method according to claim 1, wherein: the integrated cost prediction model, the productivity assessment model and the equipment compatibility model are used for obtaining a comprehensive scoring model as follows:
；
wherein, Weights for each model,/>As intercept term C, P, D is the score of the corresponding model,/>Weighted for environmental sustainability,/>Scoring environmental sustainability,/>And Q is the weight of the product quality, and Q is the product quality score.
3. The production report process management method according to claim 2, wherein: the weight of each model is set as follows:
collecting detailed historical data including cost C, capacity P, equipment compatibility D, environmental sustainability score Performing feature engineering on the product quality score Q and the comprehensive score S;
the overfitting was controlled using a modified regression method with regularization term, as follows:
；
Wherein n is the number of samples; m is the number of features; For the actual value of the ith sample,/> Is an intercept term; /(I)Is the coefficient of the j-th feature,/>For the j-th eigenvalue of the i-th sample, λ is the regularization parameter;
Searching for an optimal lambda value using a random search;
The composite score S is calculated using the optimized model parameter weights and lambda values.
4. The production report process management method of claim 3, wherein: the reporting rules include that,
The product types are divided into a plurality of types, wherein the first type of products comprises 3 types, namely a first type, a second type and a third type;
the first model is required to be produced in a self-defining mode according to special requirements, and a special process flow A and a quality standard X are used; the second model and the third model are standardized production, using a general process flow B;
The second class of products comprises a plurality of models; the first model and the second model belong to small-batch customized products, and a nonstandard process flow C and a quality standard Y are required to be used; the third model and the fourth model belong to a large number of standard products, and an optimized general process flow D is used;
the third type of products belongs to an updated version of the old products, and needs to refer to the original process flow E, update part of working procedures, add special function tests and use the updated process flow F and quality standard Z;
the fourth type of product comprises a plurality of models, the modular design and production are adopted, the modules use a general process module G, and finally the general assembly process flow H is used for assembly.
5. The production report job management method as set forth in claim 4, wherein: the report effect evaluation comprises the following steps:
The report interval time is predicted and analyzed, and the formula is as follows:
；
Where e is the moving average window size X i is the observed value at the ith time point, Is a weight;
a weighted moving average for each time point is calculated using a formula, the trend of the moving average is analyzed, and the pattern or periodicity is identified:
If the WMA value shows an ascending trend along with time, the reporting interval time is increased, which means that the working efficiency is reduced or a bottleneck exists in the production flow; if the WMA value shows a decreasing trend with time, it indicates that the reporting interval time is decreasing, which indicates an increase in production efficiency or workflow optimization; if the WMA value fluctuation exceeds the threshold value, the reporting interval time is unstable, and an uncertain factor or abnormal condition is generated; if the WMA value is relatively stable, i.e. the fluctuation does not exceed the threshold value, the reporting interval time is proved to be relatively stable.
6. A production report management system, based on the production report management method of any one of claims 1 to 5, characterized in that: comprising the steps of (a) a step of,
The production task selection module is used for selecting production tasks in the system and automatically recommending a process scheme;
the report rule setting module is used for setting report nodes according to the production flow and defining specific report steps aiming at key procedures;
The process scheme optimizing module is used for intelligently selecting the process scheme with the highest score according to the report rule and the comprehensive score;
the process parameter management module is used for calling standard process parameters in the knowledge base and automatically generating a process card and a process file;
The real-time monitoring and predictive maintenance module is used for starting real-time monitoring and recording when a worker starts to execute operation;
And the report data checking and evaluating module is used for automatically checking the report data after the operation is finished and evaluating the report effect.
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