CN117311295B - Production quality improving method and system based on wireless network equipment - Google Patents
Production quality improving method and system based on wireless network equipment Download PDFInfo
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Abstract
The invention relates to the technical field of artificial intelligence, and provides a production quality improving method and system based on wireless network equipment, wherein the method comprises the following steps: acquiring machine data of a production machine in a target factory by using preset wireless network equipment, and generating the machine stability of the production machine according to the machine data; acquiring product data of a target factory, and generating a worker level value of the target factory according to the product data; generating the production efficiency of the target factory according to the product data; generating a production quality value of a target factory by using a preset production quality algorithm, machine stability, worker level value and production efficiency; optimizing the quality parameter combination of the target factory according to the production quality value and a preset quality threshold value to obtain an optimal parameter combination of the target factory, and improving the production quality of the target factory according to the optimal parameter combination. The invention can improve the efficiency of production quality improvement based on the wireless network equipment.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a production quality improving method and system based on wireless network equipment.
Background
In a strong market competition, product quality is one of the key factors for consumer appeal. Through promoting mill production quality, can ensure that the product accords with standard and requirement to increase the competitiveness of product, and gain consumer's favor, simultaneously, the product safety and the reliability of certain trade (such as car, medical instrument etc.) have important meaning to people's life safety. The improvement of the production quality of factories can ensure that the products meet relevant safety standards and regulations, reduce the risks of faults, accidents and the like of the products, and protect the safety and rights and interests of users.
At present, if a scientific data analysis and feedback mechanism is not established in a factory, the problem of product quality and the bottleneck position cannot be effectively known, and targeted improvement measures are difficult to take without accurate data support and analysis, so that quality improvement is slow, and therefore, how to improve the production quality based on wireless network equipment becomes a problem to be solved urgently.
Disclosure of Invention
The invention provides a production quality improving method and system based on wireless network equipment, and mainly aims to solve the problem of lower efficiency in improving the production quality based on the wireless network equipment.
In order to achieve the above object, the present invention provides a method for improving production quality based on wireless network equipment, comprising:
acquiring machine data of a production machine in a target factory by using preset wireless network equipment, and generating the machine stability of the production machine according to the machine data;
acquiring product data of the target factory, and generating a worker level value of the target factory according to the product data;
generating the production efficiency of the target factory according to the product data;
generating a production quality value of the target factory by using a preset production quality algorithm, the machine stability, the worker level value and the production efficiency, wherein the preset production quality algorithm is as follows:
wherein,is the production quality value of the target plant, < >>Indicating the extent of influence of the worker level value on the production quality, < ->Is the production efficiency of the target plant, +.>Is the machine stability of the production machine, +.>Is indicative of production efficiency->And machine stability->Degree of influence on production quality, +.>Is the production cost of the target plant, +.>Is a worker level value for the target plant;
optimizing the quality parameter combination of the target plant according to the production quality value and a preset quality threshold value to obtain an optimal parameter combination of the target plant, and improving the production quality of the target plant according to the optimal parameter combination.
Optionally, the collecting, by using a preset wireless network device, machine data of a production machine in the target factory includes:
acquiring temperature data of a production machine in a target factory by using a preset temperature sensor;
collecting current data of the production machine by using a preset current sensor;
and collecting voltage data of the production machine by using a preset voltage sensor, and collecting the temperature data, the current data and the voltage data as machine data of the production machine.
Optionally, the generating the machine stability of the production machine according to the machine data includes:
extracting statistical features of the machine data, and generating a machine stabilizing function of the production machine according to the statistical features;
and generating the machine stability of the production machine by using the machine stability function.
Optionally, the extracting statistical features of the machine data is:
performing data cleaning on the machine data to obtain cleaning data of the machine data;
performing data classification on the cleaning data to obtain classification data of the cleaning data;
and carrying out vector transformation on the classified data to obtain a transformation vector of the classified data, and generating statistical characteristics of the machine data by using the transformation vector.
Optionally, the machine stabilization function includes:
wherein,is->-machine stability of the production machine, < >>Is->Each of the production machines is atAverage production value in each cycle, +.>Is the total number of observation periods, +.>Is->The production machine is in observation period +.>Second production record,/->Is->The production machine is->Actual production quantity in cycle, +.>Is a machine identification of the production machine.
Optionally, the acquiring product data of the target factory includes:
acquiring production time data of a factory product in the target factory, defect product data corresponding to the factory product, qualified product data corresponding to the factory product and worker data corresponding to the factory product;
and collecting the production time data, the defective product data, the qualified product data and the worker data as product data of the target factory.
Optionally, the generating the worker level value of the target factory according to the product data includes:
generating a worker-level value for the target plant using the worker-level algorithm and the product data as follows:
wherein,is a worker level value of the target plant, < > >Is the worker identification in the target plant, < >>Is the total number of workers in the target plant, < >>Is->The number of defective products detected by the individual workers in the products produced during a specific period of time, is->Is->The number of products produced by the individual workers during a specific time period,/->Is->Horizontal weight of individual workers,/>Is an identification of the worker.
Optionally, the generating the production efficiency of the target factory according to the product data includes:
determining a total amount of production product of the target plant from the product data;
determining the total production duration of the target factory according to the production products corresponding to the total production products;
and generating the production efficiency of the target factory according to the total amount of the produced products and the total production duration.
Optionally, the optimizing the quality parameter combination of the target plant according to the production quality value and a preset quality threshold value to obtain an optimal parameter combination of the target plant includes:
comparing the production quality value with a preset quality threshold, and determining that the parameter combination corresponding to the production quality value is the optimal parameter combination of the target factory when the production quality value is larger than or equal to the preset quality threshold;
When the production quality value is smaller than a preset quality threshold, determining that the parameter combination corresponding to the production quality value is the parameter combination to be optimized of the target plant, carrying out combination optimization on the parameter combination to be optimized to obtain an optimized parameter combination of the parameter combination to be optimized, and determining that the optimized parameter combination is the optimal parameter combination of the target plant.
In order to solve the above problems, the present invention further provides a production quality improving system based on a wireless network device, the system comprising:
the machine stability generation module is used for acquiring machine data of a production machine in a target factory by using preset wireless network equipment and generating the machine stability of the production machine according to the machine data;
the worker level value generation module is used for acquiring the product data of the target factory and generating a worker level value of the target factory according to the product data;
the production efficiency generation module is used for generating the production efficiency of the target factory according to the product data;
the production quality value generating module is configured to generate a production quality value of the target factory by using a preset production quality algorithm, the machine stability, the worker level value and the production efficiency, where the preset production quality algorithm is:
Wherein,is the production quality value of the target plant, < >>Indicating the extent of influence of the worker level value on the production quality, < ->Is the production efficiency of the target plant, +.>Is the machine stability of the production machine, +.>Is indicative of production efficiency->And machine stability->Degree of influence on production quality, +.>Is the production cost of the target plant, +.>Is a worker level value for the target plant;
and the production quality improvement module is used for optimizing the quality parameter combination of the target factory according to the production quality value and a preset quality threshold value to obtain an optimal parameter combination of the target factory, and improving the production quality of the target factory according to the optimal parameter combination.
The embodiment of the invention can realize real-time acquisition of production machine data and product data by using preset wireless network equipment, eliminates the time and labor cost of traditional manual data collection, improves the efficiency of data acquisition, considers the influence of a plurality of factors on the production quality, including machine stability, worker level, production efficiency and the like, evaluates by using a preset production quality algorithm, can evaluate the production quality of a target factory more comprehensively and objectively, optimizes and adjusts the quality parameters of the target factory according to the quality value and a preset quality threshold value to obtain the optimal parameter combination, adjusts and improves different factors by parameter optimization, and effectively improves the judgment and improvement efficiency of the production quality.
Drawings
Fig. 1 is a flow chart of a method for improving production quality based on a wireless network device according to an embodiment of the present invention;
FIG. 2 is a flow chart of a process for collecting machine data of a production machine according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating the production efficiency of a production target factory according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a system for improving quality of production based on a wireless network device according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a production quality improving method based on wireless network equipment. The execution subject of the wireless network device-based production quality improvement method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the wireless network device-based production quality improvement method may be performed by software or hardware installed in a terminal device or a server device. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for improving production quality based on a wireless network device according to an embodiment of the invention is shown. In this embodiment, the method for improving production quality based on wireless network equipment includes:
s1, acquiring machine data of a production machine in a target factory by using preset wireless network equipment, and generating the machine stability of the production machine according to the machine data.
In an embodiment of the present invention, referring to fig. 2, the collecting machine data of a production machine in a target factory by using a preset wireless network device includes:
s21, acquiring temperature data of a production machine in a target factory by using a preset temperature sensor;
s22, collecting current data of the production machine by using a preset current sensor;
s23, collecting voltage data of the production machine by using a preset voltage sensor, and collecting the temperature data, the current data and the voltage data as machine data of the production machine.
In detail, the target plant refers to a specific plant in which improvement of production quality is required; production machines refer to various machine equipment in a target plant that participates in a production process; wireless network devices refer to devices for wireless communication and data transmission, such as wireless sensors, routers, etc.; the temperature sensor is a sensor capable of sensing ambient temperature and converting it into an electrical signal; the current sensor is a sensor capable of sensing the current intensity and converting the current intensity into an electric signal; the voltage sensor is a sensor capable of sensing voltage and converting the voltage into an electric signal; machine data refers to various data collected about the state or performance of the production machine, such as temperature, current, voltage, etc.
In an embodiment of the present invention, the machine data of the production machine in the target factory is collected by using a preset wireless network device, including collecting temperature, current and voltage data of the production machine by using a preset temperature sensor, a current sensor and a voltage sensor, and collecting the data to form machine data about the production machine.
In detail, the step of acquiring temperature data of production machines in a target factory by using a preset temperature sensor means that the preset temperature sensor is installed on each production machine, and the temperature data is transmitted to a data center or a monitoring system through wireless network equipment, for example, the temperature sensor can be installed on a welding machine on an automobile assembly line to monitor temperature change in a welding process, and the running state of the production machine can be evaluated by monitoring the temperature data, so that abnormal conditions such as overheat or supercooling can be timely detected, and the stability of the production process and the product quality can be ensured.
In detail, the current data of the production machine are collected by using a preset current sensor: a preset current sensor is connected to each production machine and current data is transmitted to the corresponding system using a wireless network device. For example, a current sensor is installed on a stirring machine on a food processing line to monitor the power consumption condition of the machine, and by monitoring the current data, the energy consumption condition and the power consumption level of the production machine can be known, so that the working state and the efficiency of the machine can be effectively evaluated.
In detail, voltage data of the production machine are collected by using a preset voltage sensor: a preset voltage sensor is connected to each production machine and voltage data is transmitted to a data center or control system via a wireless network device. For example, a voltage sensor is installed on a machine tool on a manufacturing assembly line to monitor the power supply voltage condition of the machine tool, and by monitoring voltage data, the stability and quality of power supply can be identified, so that the production machine can be ensured to operate in a proper voltage range, and the stability and product quality of the production process can be improved.
Further, the temperature data, the current data, and the voltage data are collected as machine data of the production machine: and integrating and summarizing the acquired temperature, current and voltage data to form a complete machine data set about the production machine. For example, all machine data is stored in a database for subsequent analysis and processing.
Specifically, assuming that the target factory is a home electronics company, there are a plurality of machines on the production line that are responsible for soldering electronic components, and a preset temperature sensor is installed on each soldering machine for collecting temperature data of the machine. Meanwhile, preset current sensors and voltage sensors are also installed on each machine for collecting current and voltage data. Temperature, current, and voltage data are transmitted to a data center via a wireless network device. Finally, the data are collected to form machine data about the welding machine. These data can be used to monitor the machine's operating conditions, evaluate the weld quality, and help optimize the welding process and improve product quality.
In an embodiment of the present invention, the generating the machine stability of the production machine according to the machine data includes:
extracting statistical features of the machine data, and generating a machine stabilizing function of the production machine according to the statistical features;
and generating the machine stability of the production machine by using the machine stability function.
In detail, the machine stability refers to the stability or reliability of the operating state of the production machine over a certain period of time; statistical features refer to statistics, such as mean, standard deviation, maximum, etc., used to describe a data set.
In detail, the statistical features of the machine data are extracted by cleaning the collected machine data to remove abnormal values and error information therein, thereby obtaining cleaning data. And classifying the cleaning data according to a certain characteristic attribute to obtain classified data. Finally, the classified data are converted into vector forms by using a vector conversion method, the statistical characteristics of the machine data are obtained, the running state and the performance index of the production machine can be known on the whole by extracting the statistical characteristics of the machine data, and a data basis is provided for the subsequent stability calculation.
In detail, by generating a machine stabilization function, machine data can be converted into a mathematical model to calculate the machine stability of the production machine.
Further, the generated machine stability function is used for calculating the machine stability of each production machine, namely, the machine stability is calculated according to the production record and the average value of the production machine, the stability and the reliability of the production machine can be evaluated through calculating the machine stability, potential problems are found and solved, and the production efficiency is improved.
In detail, the statistical features of the extracted machine data are:
performing data cleaning on the machine data to obtain cleaning data of the machine data;
performing data classification on the cleaning data to obtain classification data of the cleaning data;
and carrying out vector transformation on the classified data to obtain a transformation vector of the classified data, and generating statistical characteristics of the machine data by using the transformation vector.
In detail, the data cleaning refers to processing data, and removing interference factors such as abnormal values, missing values, repeated values, inconsistent values and the like in the data to obtain more standardized and more accurate data, wherein the data cleaning is one of important links of data preprocessing; data classification refers to the process of grouping data according to certain attributes or rules. By classifying the data, the organization and management of the data can be realized, and the internal rule among the data can be better found; vector transformation is the process of converting data from an original form into vectors. Representing the data set with vectors not only improves the efficiency of data processing, but also better describes the similarity and difference between the data.
In detail, the purpose of data cleaning is to remove interference factors and improve data quality and accuracy. Data cleansing is typically accomplished by the following steps: removing abnormal values: unreasonable, extreme or outlier data is eliminated, so that the data is more real and effective; filling up missing values: for the data with missing information, filling the missing values by adopting methods such as mean value, median and the like, so as to ensure the data integrity; and (5) de-duplication: if repeated records exist in the data, the repeated data need to be deleted, and the data redundancy is avoided; format conversion: and converting the data which does not meet the data format requirement into a correct format, and avoiding subsequent processing errors. For example, in factory machine data, there may be sensor damage or reading errors, etc., and these problems can be eliminated by data cleaning, ensuring the accuracy and authenticity of the machine data.
In detail, by classifying machine data, the data can be better organized and managed, and intrinsic laws between the data can be more effectively discovered. The specific implementation mode of data classification is as follows: classifying according to the attribute: data is divided into categories according to a certain attribute. For example, machine data is classified into two types of high and low temperature according to temperature size; classifying according to rules: the data are classified into different categories according to a rule set in advance. For example, machines whose machine yield is lower than a certain value for a certain period of time are classified as abnormal machines or damaged machines, or the like. For example, in factory machine data, machines may be categorized according to attributes such as production time, model, operating status, etc., to facilitate subsequent analysis.
In detail, the machine data is transformed into a vector form by means of vector transformation in order to better describe its statistical characteristics. The specific implementation mode is as follows: word bag model: representing each word in the text data as a vector, using the number of occurrences of each word in the text as a value for the corresponding position in the word vector; TF-IDF model: representing words in the text data as a vector, and multiplying the frequency of each word in the text by the frequency of the word in all the texts as a value of the corresponding position in the word vector; one-hot encoding: each discrete attribute value is represented as a vector in which the corresponding attribute value is 1 in position and 0 in other positions. For example, in plant machine data, various data information collected by the machine may be converted into a vector form, e.g., current and temperature may be converted into a two-dimensional vector, where the two components represent the values of current and temperature, respectively. In this way, various statistical features in the vector may be used to describe the machine's operating state and performance metrics.
Further, assuming multiple machines in a plant, each machine collects different data information including machine run time, current, temperature, etc. First, machine data is subjected to data cleaning and sorting, for example, operations of removing outliers, filling missing values, sorting by model, and the like. The classified machine data is then converted into a vector form, e.g., machine run time and current are converted into a two-dimensional vector, where the first component represents machine run time and the second component represents machine current. Through the statistical characteristics in the vector, performance indexes such as average running time, current variance and the like of each machine can be obtained, and the machine stability of each machine can be calculated according to the performance indexes. Through calculation and analysis of the machine stability, the reliability and stability of each machine can be evaluated.
In detail, the machine stabilization function comprises:
wherein,is->-machine stability of the production machine, < >>Is->Each of the production machines is atAverage production value in each cycle, +.>Is the total number of observation periods, +.>Is->The production machine is in observation period +.>Second production record,/->Is->The production machine is->Actual production quantity in cycle, +.>Is a machine identification of the production machine.
In detail, the machine stabilization function represents, for each machineFirst, calculate its average production value +.>Then, for each observation period, +.>The actual production quantity of the bench machine in the cycle +.>Next, the difference between the actual production quantity and the average production value of the machine in the observation period is calculated>For each observation period, the square of the difference is +.>Summing to obtain a sum of squares of the differences over the observation period, and finally dividing the sum of squares of the differences over the observation period by +.>Obtaining the machine stability of the machine>。
In detail, the machine stabilization function means by comparing the difference between the actual production quantity and the average production value in each observation periodTo evaluate the stability of the machine. If the number of machines produced is close to the average value per cycle, the sum of squares of the differences is small, and the machine stability is low Will be higher; conversely, if the production quantity of the machine fluctuates greatly, the sum of squares of the differences will be large, the machine stability +.>Will be lower.
S2, acquiring product data of the target factory, and generating a worker level value of the target factory according to the product data.
In an embodiment of the present invention, the obtaining product data of the target plant includes:
acquiring production time data of a factory product in the target factory, defect product data corresponding to the factory product, qualified product data corresponding to the factory product and worker data corresponding to the factory product;
and collecting the production time data, the defective product data, the qualified product data and the worker data as product data of the target factory.
In detail, the product data includes information such as production time data of the factory product, defect product data, qualified product data, worker data, etc., wherein the production time data refers to the time spent by the factory product in the production process, and can be used for comparing the production efficiency between different products; defective product data refers to the number and type of products detected to have defects during the production process, and can be used to evaluate the quality level of the factory product; the qualified product data refers to the quantity and the category of products which are detected to meet the requirements in the production process, and can be used for evaluating the production efficiency and the quality level of factory products; worker data refers to worker information involved in the production of a factory product, including the number of workers, skill level, etc.
In detail, the relevant data is collected, consolidated and processed in the target plant by using a computer or other corresponding technical means, resulting in a complete product data set.
In detail, by acquiring and collecting the product data, a reference basis can be provided for production management and optimization of the factory, and the factory is helped to improve the production efficiency and the product quality.
Further, assuming that a factory needs production data analysis, a complete product data set may be formed by collecting production time data, defective product data, acceptable product data, and worker data for the factory over a period of time, and integrating the data together. By analysis of this dataset, the production efficiency and quality level of the plant can be evaluated for optimization and improvement.
In an embodiment of the present invention, the generating the worker level value of the target factory according to the product data includes:
generating a worker-level value for the target plant using the worker-level algorithm and the product data as follows:
wherein,is a worker level value of the target plant, < >>Is the worker identification in the target plant, < >>Is the total number of workers in the target plant, < > >Is->The number of defective products detected by the individual workers in the products produced during a specific period of time, is->Is->The number of products produced by the individual workers during a specific time period,/->Is->Horizontal weight of individual workers,/>Is an identification of the worker.
In detail, the worker level algorithm calculates the ratio of the number of defective products to the total number of products by counting the products produced by each worker in a specific period of time, multiplies the product by the corresponding level weight, and then accumulates the results of all workers to obtain the worker level value of the target factory.
In detail, proportional terms in the worker level algorithm formulaTo indicate how many defective products are in the products produced by each worker, in general, the fewer defective products, the higher the work quality of the worker.
In detail, in the worker level algorithm formulaThe term represents a horizontal weight of each worker, which may be determined according to factors such as skill level, experience, training level, etc. of the worker. A higher level weight means that the corresponding worker contributes more to the overall worker level value.
In detail, by adding the results of each worker, a worker level value of the target factory is obtained, so that contributions of different workers can be comprehensively considered to obtain an overall evaluation index.
S3, generating the production efficiency of the target factory according to the product data.
In an embodiment of the present invention, referring to fig. 3, the generating the production efficiency of the target plant according to the product data includes:
s31, determining the total amount of the production products of the target factory according to the product data;
s32, determining the total production duration of the target factory according to the production products corresponding to the total production products;
s33, generating the production efficiency of the target factory according to the total quantity of the produced products and the total production duration.
In detail, the production efficiency refers to the number of production tasks or outputs completed in a unit time.
In detail, determining the total amount of the product produced by the target plant from the product data refers to calculating the total amount of the product produced by the target plant for a specific period of time from the provided product data, for example: suppose that the target plant produces 1000 products in a week.
In detail, determining the total production duration of the target plant according to the produced products corresponding to the total produced products refers to calculating the total time required for the target plant to complete the products according to the total produced products, for example: suppose that 1000 products produced by the target plant take 3 days.
In detail, generating the production efficiency of the target plant from the total amount of the produced products and the total production time period refers to calculating the production efficiency of the target plant by dividing the total amount of the produced products by the total production time period, for example: the production efficiency of the target plant was 1000 products/3 days = 333.33 products/day.
S4, generating a production quality value of the target factory by using a preset production quality algorithm, the machine stability, the worker level value and the production efficiency.
In the embodiment of the invention, the preset production quality algorithm is as follows:
wherein,is the production quality value of the target plant, < >>Indicating the extent of influence of the worker level value on the production quality, < ->Is the production efficiency of the target plant, +.>Is the machine stability of the production machine, +.>Is indicative of production efficiency->And machine stability->Degree of influence on production quality, +.>Is the production cost of the target plant, +.>Is the worker level value for the target plant.
In detail, the preset quality algorithm is used to calculate the quality value of the target plant, and it includes a plurality of parameters: production efficiencyMachine stability->Production cost->And worker level value- >Etc.
Further, the method comprises the steps of,is indicative of production efficiency->And machine stability->Degree of influence on production quality, +.>The larger the impact of production efficiency and machine stability on production quality is explained; production efficiency->Representing the number of production tasks or productions completed per unit time, < >>The higher the production efficiency of the target factory is, the better the production efficiency is; machine stability->Refers to performance indexes such as failure rate of the machine, < >>The higher the production machine is, the more stable the production machine is, and the lower the probability of failure is; production cost->The lower the production cost of the target factory is, and the production can be realized more cost-effectively; worker level valueIndicating the extent of worker contribution to the quality of production, +.>The higher the level of the worker, the birth to the birthThe greater the impact of product quality; />Indicating the extent of influence of the worker level value on the production quality, < ->The larger the explanation the greater the impact of the worker level value on the quality of production.
In detail, the preset production quality algorithm calculates a production quality value of the target factory taking into consideration factors such as production efficiency, machine stability, production cost and worker level value. In particular, when the production efficiency, machine stability and worker level values of the factory are all high, the quality of the produced product should be relatively high; when the production cost is lower, the factory can realize the production more cost-effectively on the premise of ensuring the quality.
And S5, optimizing the quality parameter combination of the target factory according to the production quality value and a preset quality threshold value to obtain an optimal parameter combination of the target factory, and improving the production quality of the target factory according to the optimal parameter combination.
In an embodiment of the present invention, the optimizing the quality parameter combination of the target plant according to the production quality value and a preset quality threshold value to obtain an optimal parameter combination of the target plant includes:
comparing the production quality value with a preset quality threshold, and determining that the parameter combination corresponding to the production quality value is the optimal parameter combination of the target factory when the production quality value is larger than or equal to the preset quality threshold;
when the production quality value is smaller than a preset quality threshold, determining that the parameter combination corresponding to the production quality value is the parameter combination to be optimized of the target plant, carrying out combination optimization on the parameter combination to be optimized to obtain an optimized parameter combination of the parameter combination to be optimized, and determining that the optimized parameter combination is the optimal parameter combination of the target plant.
In detail, the production quality value refers to an evaluation value of the quality of the product produced by the target plant; the preset quality threshold value refers to a criterion determined for comparing the reference value of the production quality value.
In detail, the production quality value is compared with a preset quality threshold, and if the production quality value is greater than or equal to the preset quality threshold, the parameter combination corresponding to the production quality value is determined as the optimal parameter combination of the target plant. This means that the parameter combination is able to meet a preset quality criterion; if the production quality value is smaller than the preset quality threshold, determining the parameter combination corresponding to the production quality value as the parameter combination to be optimized of the target factory, performing combination optimization on the parameter combination to be optimized, searching for a better parameter combination, and obtaining an optimized parameter combination of the parameter combination to be optimized through searching and an optimization algorithm.
In detail, comparing the production quality value with a preset quality threshold value means judging whether the production quality of the target plant meets the standard or not through numerical comparison.
In detail, the parameter combination to be optimized is subjected to combined optimization, the optimized parameter combination for obtaining the parameter combination to be optimized is a pointer parameter combination to be optimized, an optimization algorithm (such as a genetic algorithm, simulated annealing and the like) is used for searching and optimizing, so that a better parameter combination is obtained, and the combination can achieve the best effect on the premise of quality assurance.
Specifically, assume that a target plant is to optimize three parameters on the production line: production efficiency, machine stability, and worker level values. The preset quality threshold is 80. When the production quality value of the production line is 85, the production quality value is larger than the preset quality threshold value, and the parameter combination is determined to be the optimal parameter combination. And when the production quality value of the production line is 75, the production quality value is smaller than the preset quality threshold value, and the parameter combination is determined to be the parameter combination to be optimized. Searching and optimizing the parameter combination to be optimized through an optimization algorithm to obtain the optimized parameter combination (increasing production efficiency, improving machine stability and improving worker level value), and further determining the optimal parameter combination. Thus, the production quality value of the production line can reach above a preset quality threshold.
In the embodiments of the present invention, the optimal parameter combination refers to a combination of constitutively based on production efficiency, machine stability and worker level values.
In detail, the production quality improvement of the target plant according to the optimal parameter combination comprises: adjusting the machine equipment according to the optimal parameter combination, optimizing the process flow and the control parameters, and training and managing reinforcing personnel, wherein the adjustment of the machine equipment according to the optimal parameter combination refers to the parameter adjustment according to the optimal parameter combination, and the relevant adjustment is carried out on the machine equipment of a target factory so as to improve the stability and the energy efficiency of the machine, reduce the mechanical faults and damages and finally improve the production efficiency and the product quality; optimizing the process flow and the control parameters refers to optimizing the process flow and the control parameters according to the optimal parameter combination, and producing according to scientific and reasonable production flow and parameter setting, so that the waste and the defective product quantity in the production are reduced, and the quality stability and the repeatability of the production process are ensured; the reinforced personnel training and management means that the reinforced personnel training and management is carried out according to the optimal parameter combination, the skill level of workers is improved, the occurrence of misoperation and accidents is reduced, and therefore the safety and the quality stability of the production process are ensured.
The embodiment of the invention can realize real-time acquisition of production machine data and product data by using preset wireless network equipment, eliminates the time and labor cost of traditional manual data collection, improves the efficiency of data acquisition, considers the influence of a plurality of factors on the production quality, including machine stability, worker level, production efficiency and the like, evaluates by using a preset production quality algorithm, can evaluate the production quality of a target factory more comprehensively and objectively, optimizes and adjusts the quality parameters of the target factory according to the quality value and a preset quality threshold value to obtain the optimal parameter combination, adjusts and improves different factors by parameter optimization, and effectively improves the judgment and improvement efficiency of the production quality.
Fig. 4 is a functional block diagram of a production quality improving system based on a wireless network device according to an embodiment of the present invention.
The wireless network device-based production quality improvement system 100 of the present invention may be installed in an electronic device. Depending on the functions implemented, the wireless network device-based production quality enhancement system 100 may include a machine stability generation module 101, a worker level value generation module 102, a production efficiency generation module 103, a production quality value generation module 104, and a production quality enhancement module 105. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the machine stability generation module 101 is configured to collect machine data of a production machine in a target factory by using a preset wireless network device, and generate machine stability of the production machine according to the machine data;
the worker level value generation module 102 is configured to obtain product data of the target plant, and generate a worker level value of the target plant according to the product data;
the production efficiency generating module 103 is configured to generate the production efficiency of the target factory according to the product data;
the production quality value generating module 104 is configured to generate a production quality value of the target factory using a preset production quality algorithm, the machine stability, the worker level value, and the production efficiency, where the preset production quality algorithm is:
wherein,is the raw of the target plantYield value (I/O)>Indicating the extent of influence of the worker level value on the production quality, < ->Is the production efficiency of the target plant, +.>Is the machine stability of the production machine, +.>Is indicative of production efficiency->And machine stability->Degree of influence on production quality, +. >Is the production cost of the target plant, +.>Is a worker level value for the target plant; />
The production quality improving module 105 is configured to optimize a quality parameter combination of the target plant according to the production quality value and a preset quality threshold, obtain an optimal parameter combination of the target plant, and improve production quality of the target plant according to the optimal parameter combination.
In the several embodiments provided in the present invention, it should be understood that the disclosed method and system may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module 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 can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely 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 modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for improving production quality based on wireless network equipment, the method comprising:
acquiring machine data of a production machine in a target factory by using preset wireless network equipment, and generating the machine stability of the production machine according to the machine data;
acquiring product data of the target factory, and generating a worker level value of the target factory according to the product data;
generating the production efficiency of the target factory according to the product data;
generating a production quality value of the target factory by using a preset production quality algorithm, the machine stability, the worker level value and the production efficiency, wherein the preset production quality algorithm is as follows:
wherein (1)>Is the production quality value of the target plant, < >>Indicating the extent of influence of the worker level value on the production quality, < - >Is the production efficiency of the target plant, +.>Is the machine stability of the production machine, +.>Is indicative of production efficiency->And machine stability->The degree of influence on the production quality,is the production cost of the target plant, +.>Is a worker level value for the target plant;
optimizing the quality parameter combination of the target plant according to the production quality value and a preset quality threshold value to obtain an optimal parameter combination of the target plant, and improving the production quality of the target plant according to the optimal parameter combination.
2. The method for improving production quality based on wireless network equipment according to claim 1, wherein the step of acquiring machine data of a production machine in a target plant by using a preset wireless network equipment comprises the steps of:
acquiring temperature data of a production machine in a target factory by using a preset temperature sensor;
collecting current data of the production machine by using a preset current sensor;
and collecting voltage data of the production machine by using a preset voltage sensor, and collecting the temperature data, the current data and the voltage data as machine data of the production machine.
3. The method for improving production quality based on wireless network equipment according to claim 1, wherein the generating the machine stability of the production machine from the machine data comprises:
Extracting statistical features of the machine data, and generating a machine stabilizing function of the production machine according to the statistical features;
and generating the machine stability of the production machine by using the machine stability function.
4. The method for improving production quality of wireless network equipment according to claim 3, wherein the extracting statistical features of the machine data is:
performing data cleaning on the machine data to obtain cleaning data of the machine data;
performing data classification on the cleaning data to obtain classification data of the cleaning data;
and carrying out vector transformation on the classified data to obtain a transformation vector of the classified data, and generating statistical characteristics of the machine data by using the transformation vector.
5. The wireless network device-based production quality enhancement method of claim 3, wherein the machine stabilization function comprises:
wherein (1)>Is->The machine stability of each of said production machines,is->The production machine is->Average production value in each cycle, +.>Is the total number of observation periods, +.>Is the firstThe production machine is in observation period +.>Second production record,/- >Is->The production machine is->Actual production quantity in cycle, +.>Is a machine identification of the production machine.
6. The method for improving production quality of a wireless network device according to claim 1, wherein the acquiring the product data of the target plant comprises:
acquiring production time data of a factory product in the target factory, defect product data corresponding to the factory product, qualified product data corresponding to the factory product and worker data corresponding to the factory product;
and collecting the production time data, the defective product data, the qualified product data and the worker data as product data of the target factory.
7. The wireless network device-based production quality improvement method of claim 1, wherein the generating the worker level value of the target plant from the product data comprises:
generating a worker-level value for the target plant using the worker-level algorithm and the product data as follows:
wherein (1)>Is a worker level value of the target plant, < >>Is the worker identification in the target plant, < >>Is the total number of workers in the target plant, < > >Is->The number of defective products detected by the individual workers in the products produced during a specific period of time, is->Is->The number of products produced by the individual workers during a specific time period,/->Is->Of individual workersHorizontal weight (L)>Is an identification of the worker.
8. The method for improving production quality of a wireless network device according to claim 1, wherein the generating the production efficiency of the target plant from the product data comprises:
determining a total amount of production product of the target plant from the product data;
determining the total production duration of the target factory according to the production products corresponding to the total production products;
and generating the production efficiency of the target factory according to the total amount of the produced products and the total production duration.
9. The method for improving production quality based on wireless network equipment according to any one of claims 1 to 8, wherein optimizing the quality parameter combination of the target plant according to the production quality value and a preset quality threshold value to obtain the optimal parameter combination of the target plant comprises:
comparing the production quality value with a preset quality threshold, and determining that the parameter combination corresponding to the production quality value is the optimal parameter combination of the target factory when the production quality value is larger than or equal to the preset quality threshold;
When the production quality value is smaller than a preset quality threshold, determining that the parameter combination corresponding to the production quality value is the parameter combination to be optimized of the target plant, carrying out combination optimization on the parameter combination to be optimized to obtain an optimized parameter combination of the parameter combination to be optimized, and determining that the optimized parameter combination is the optimal parameter combination of the target plant.
10. A wireless network device-based production quality enhancement system, the system comprising:
the machine stability generation module is used for acquiring machine data of a production machine in a target factory by using preset wireless network equipment and generating the machine stability of the production machine according to the machine data;
the worker level value generation module is used for acquiring the product data of the target factory and generating a worker level value of the target factory according to the product data;
the production efficiency generation module is used for generating the production efficiency of the target factory according to the product data;
the production quality value generating module is configured to generate a production quality value of the target factory by using a preset production quality algorithm, the machine stability, the worker level value and the production efficiency, where the preset production quality algorithm is:
Wherein (1)>Is the production quality value of the target plant, < >>Indicating the extent of influence of the worker level value on the production quality, < ->Is the production efficiency of the target plant, +.>Is the machine stability of the production machine, +.>Is indicative of production efficiency->And machine stability->The degree of influence on the production quality,is the production cost of the target plant, +.>Is a worker level value for the target plant;
and the production quality improvement module is used for optimizing the quality parameter combination of the target factory according to the production quality value and a preset quality threshold value to obtain an optimal parameter combination of the target factory, and improving the production quality of the target factory according to the optimal parameter combination.
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