CN114356895A - Abnormal condition database management-based method, device, equipment and storage medium - Google Patents
Abnormal condition database management-based method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN114356895A CN114356895A CN202210256168.3A CN202210256168A CN114356895A CN 114356895 A CN114356895 A CN 114356895A CN 202210256168 A CN202210256168 A CN 202210256168A CN 114356895 A CN114356895 A CN 114356895A
- Authority
- CN
- China
- Prior art keywords
- database
- abnormal
- working condition
- data
- production
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application discloses a method, a device, equipment and a storage medium based on abnormal working condition database management, wherein the method comprises the following steps: when abnormal working conditions exist, generating nonstandard working condition codes, judging whether a first database corresponding to the nonstandard working condition codes exists or not, and when the first database does not exist, establishing a second database; acquiring process parameters corresponding to the nonstandard working condition codes, storing the process parameters into the second database, and recording production product data and production process data corresponding to the process parameters into the second database; and processing the abnormal working conditions based on the second database, and resetting the preset values of the process parameters. By adopting the method and the device, the adjustment efficiency of production under the non-standard working condition can be greatly improved.
Description
Technical Field
The application belongs to the field of general data processing of the Internet industry and mainly relates to a method, a device, equipment and a storage medium for managing a database based on abnormal working conditions.
Background
At present, a general benchmarking working condition database is suitable for simple production process parameters, and when non-standard working conditions occur in production, the process parameters of the corresponding database need to be adjusted to keep the production quality at a good level.
In the prior art, the process parameters of the production database are generally adjusted according to the accumulated experience of production personnel for many years so as to adapt to the change of non-standard working conditions, and the efficiency of the adjustment process is low.
Disclosure of Invention
An object of the present application is to provide a method, an apparatus, a device and a storage medium for managing a database based on abnormal operating conditions, which have the advantage of greatly improving the adjustment efficiency of production under non-standard operating conditions.
In order to achieve the above object, in a first aspect, an embodiment of the present application provides a method for managing a database based on abnormal operating conditions, where the method includes:
when abnormal working conditions exist, generating nonstandard working condition codes, judging whether a first database corresponding to the nonstandard working condition codes exists or not, and when the first database does not exist, establishing a second database;
acquiring process parameters corresponding to the nonstandard working condition codes, storing the process parameters into the second database, and recording production product data and production process data corresponding to the process parameters into the second database;
and processing the abnormal working conditions based on the second database, and resetting the preset values of the process parameters.
It can be understood that when abnormal conditions exist, non-standard condition codes are generated, whether a first database corresponding to the non-standard condition codes exists is judged, when the first database does not exist, a second database is newly established, process parameters corresponding to the non-standard condition codes are obtained, the process parameters are stored in the second database, production product data and production process data corresponding to the process parameters are recorded in the second database, the abnormal conditions are processed based on the second database, preset values of the process parameters are reset, and adjustment efficiency of production under the non-standard conditions can be improved.
In one possible example, the second database includes a parameter scoring card including the adjustable preset values, and the preset values are calculated using a prediction algorithm according to the abnormal operating conditions.
It can be understood that the accuracy of the preset value can be optimized by calculating the preset value using a prediction algorithm according to the abnormal working condition.
In one possible example, the step of recording the production product data and the production process data corresponding to the process parameters in the second database comprises the following steps:
storing the abnormal values of the process parameters into the second database;
and storing the production verification result product parameters into the second database.
It can be understood that storing the abnormal values of the process parameters into the second database and storing the production verification result product parameters into the second database can optimize the integrity of the production product data and the production process data.
In one possible example, the processing the abnormal operating condition based on the second database includes the following steps:
monitoring the abnormal working condition;
and updating the production process data.
It can be understood that the abnormal working conditions are monitored, the production process data are updated, and the coping efficiency of the abnormal working conditions can be optimized.
In one possible example, the monitoring of the abnormal operating condition includes the steps of:
and when the process parameters are abnormal, generating the process parameter data items.
It can be understood that when the process parameter is abnormal, the process parameter data item is generated, and the monitoring of the process parameter can be optimized.
In one possible example, when the process parameter is abnormal, generating the process parameter data item includes the steps of:
and generating the process parameter data item based on at least one of batch standard reaction time, post working condition reacted time, residual reaction time, parameter change times, change time period positions and process parameter mean values.
It can be understood that the process parameter data item is generated based on at least one of the batch standard reaction time, the post working condition reacted time, the residual reaction time, the parameter change times, the change time period position and the process parameter mean value, and the recording efficiency of the abnormal process parameters can be improved.
In one possible example, the processing the abnormal operating condition based on the second database, and resetting the preset value of the process parameter includes the following steps:
based on the batch number of the product, acquiring data corresponding to the batch number from the second database;
and analyzing the variation trend of the nonstandard parameters in the batch number based on the corresponding data, and resetting the preset value.
It can be understood that the data corresponding to the batch numbers are obtained from the second database, the variation trend of the nonstandard parameters in the batch numbers is analyzed, the preset values are reset, and the efficiency of processing abnormal working conditions can be optimized.
In a second aspect, an apparatus for abnormal situation database management includes means for performing the method provided in the first aspect or any of the embodiments of the first aspect.
In a third aspect, an apparatus based on abnormal situation database management is characterized by comprising a processor, a memory, and one or at least one program, wherein the one or at least one program is stored in the memory and configured to be executed by the processor, and the program comprises instructions for executing the method provided in the first aspect or any embodiment of the first aspect.
In a fourth aspect, a computer-readable storage medium is characterized by storing a computer program, which causes a computer to execute a method provided by the first aspect or any implementation manner of the first aspect.
The embodiment of the application has the following beneficial effects:
when an abnormal working condition exists, generating a nonstandard working condition code according to the abnormal working condition, and judging whether a first database corresponding to the nonstandard working condition code exists or not; when the first database does not exist, a second database is newly established, and nonstandard working condition data are stored in the second database; and processing the abnormal working conditions based on the non-standard working condition data of the second database, thereby greatly improving the adjustment efficiency of production under the non-standard working conditions.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative efforts. Wherein:
fig. 1 is an application scenario diagram based on abnormal condition database management according to an embodiment of the present application;
fig. 2 is a schematic flowchart of abnormal condition database management according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an abnormal condition database management device according to an embodiment of the present application;
fig. 4 is a structural diagram of an abnormal condition-based database management apparatus according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "1" and "2" and the like in this application are used to distinguish different objects, and are not used to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a diagram of an application scenario based on abnormal condition database management according to an embodiment of the present application. As shown in fig. 1, the application scenario diagram includes a user 101, an electronic device 102, and a server 103. It should be noted that the number of devices, the form of the devices, and the number of users in the system shown in fig. 1 are used for example, and do not limit the embodiments of the present application, and one user may use a plurality of electronic devices.
The user 101 is a user who actually operates the electronic device 102 to control the electronic device 102 to perform corresponding operations. The electronic device 102 may be a notebook computer shown in fig. 1, and may also be a Personal Computer (PC), a smart phone, an all-in-one machine, a palm computer, a tablet computer (pad), a smart television playing terminal, a portable device, and the like. The operating system of the PC-side electronic device, such as a kiosk or the like, may include, but is not limited to, operating systems such as Linux system, Unix system, Windows series system (e.g., Windows xp, Windows 7, etc.). The operating system of the electronic device at the mobile end, such as a smart phone, may include, but is not limited to, an operating system such as an android system, an IOS (operating system of an apple mobile phone), a Window system, and the like.
The method for managing the abnormal condition-based database provided by the embodiment of the present application is described below, and the method may be executed by an abnormal condition-based database management apparatus, which may be implemented by software and/or hardware, and may be generally integrated in an electronic device or a server.
Referring to fig. 2, fig. 2 is a schematic view illustrating a flow of abnormal condition database management according to an embodiment of the present application. The method is exemplified by being applied to an abnormal condition-based database management process, and the abnormal condition-based database management device can comprise a server or an electronic device. The method comprises the following steps S201-S203, wherein,
s201: and when abnormal working conditions exist, generating a nonstandard working condition code, judging whether a first database corresponding to the nonstandard working condition code exists, and when the first database does not exist, establishing a second database.
S202: and acquiring the process parameters corresponding to the nonstandard working condition codes, storing the process parameters into the second database, and recording the production product data and the production process data corresponding to the process parameters into the second database.
For example, the second database is built according to the following idea:
IF does not have the non-standard condition code THEN;
a second database is newly established;
the second database is updated.
It should be noted that the abnormal value X in the second database is fixed, the normal value X is the same as the design score card, the design score card includes the target value of each process parameter, and if the parameter is normal, the target value in the design score card is regarded as the parameter value of the parameter. And in the updating process of the second database, updating the database corresponding to the nonstandard working condition code of each production query.
Further, the recording of the production product data and the production process data corresponding to the process parameters in the second database may also be implemented by a block chain technique, specifically including fragmenting data generated by production equipment or a production process corresponding to the production product data and the production process data, encrypting data corresponding to at least one fragment to obtain an encryption key of the at least one fragment, and generating a unique hash, that is, an encrypted output character string of a fixed length, according to the encryption key or the data corresponding to the fragment. The hash will be added to the ledger and shard metadata to link the transaction to the shard of storage, eventually recording all transactions in the blockchain ledger through the storage system and synchronizing this information between all nodes. Particularly, the storage system may also copy the data of each segment according to the requirement to obtain multiple copies, and the number of copies and the storage place corresponding to each segment data may be determined according to the requirement of the user or the priority of the segment.
S203: and processing the abnormal working conditions based on the second database, and resetting the preset values of the process parameters.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a database management device based on abnormal operating conditions according to an embodiment of the present disclosure. Based on the above system architecture, the abnormal condition-based database management apparatus 300 may be a server or a module in the server. The apparatus 300, at least comprising: an acquisition module 301 and a processing module 302, wherein,
the acquisition module 301 is used for acquiring abnormal working conditions;
the processing module 302 is configured to generate an nonstandard operating condition code; judging whether a first database corresponding to the nonstandard working condition codes exists or not; a second database is newly established; acquiring process parameters corresponding to the nonstandard working condition codes; storing the process parameters into the second database, and recording production product data and production process data corresponding to the process parameters into the second database; and processing the abnormal working condition, and resetting the preset value of the process parameter.
In one possible example, the second database includes a parameter scorecard, the parameter scorecard includes the adjustable preset value, and the processing module 302 calculates the preset value using a prediction algorithm according to the abnormal operating condition.
For example, the production process parameters are index parameters of all production processes for controlling the production flow to ensure the quality and efficiency of the produced product, and include equipment control parameters (temperature, pressure and speed), production steps, production time and material amount, and the like. When production is carried out, multi-target machine learning is carried out according to production conditions, a machine learning training model of the production conditions is obtained, when abnormal process parameters occur, a prediction algorithm is carried out by using the training model, normal adjustable X process parameters are adjusted, and the optimal value (preset value) of the normal adjustable X is given, so that the indexes of the adjusted production products reach the optimal target. Among them, the machine learning algorithm can be roughly classified into the following three types:
and (3) supervision and learning: is a machine learning task that establishes a mathematical (mapping) relationship between input X and output Y variables, such pairs of X, Y constituting label data for modeling to learn how to predict output from input.
Unsupervised learning: is a machine learning task that only uses input X variables, which are unlabeled data, and the learning algorithm uses the inherent structure of the data when modeling.
Reinforcement learning: is a machine learning task that determines the next action plan, and it accomplishes this goal by trial and error learning in an effort to maximize the return.
In supervised learning, when a space is assumed by given data and models, an optimization problem can be constructed, namely how to determine parameters to optimize your optimization goal, which is called training. When the training is completed, the parameters are determined, and a certain hypothesis function in the hypothesis space determined by the parameters is a so-called model.
For adjusting the normal adjustable X process parameters, an optimal value (preset value) of the normal adjustable X is given, and the present embodiment is exemplified by a super parameter with optimized parameters. The hyper-parameters are essentially parameters of a machine learning algorithm, directly influence the learning process and the prediction performance, and can be generally applied to all data sets due to the fact that no hyper-parameter setting of 'one-time-to-one-time' exists, so that hyper-parameter optimization (also called hyper-parameter adjustment or model adjustment) is needed.
One popular machine learning algorithm is the support vector machine. The hyper-parameters to be optimized are the C-parameters and gamma-parameters of the Radial Basis Function (RBF) kernel (i.e., the linear kernel has only C-parameters; the C and exponent of the polynomial kernel). The C parameter is a penalty term that limits overfitting, while the gamma parameter controls the width of the RBF kernel. As mentioned above, tuning is usually done to derive the optimal set of values for the hyper-parameters, although some studies have been aimed at finding good starting values for the C-parameter and gamma-parameter.
In addition, the present embodiment does not limit the horizontal collaboration and the vertical collaboration of the machine learning, for example, a machine learning model trained based on data generated by the block chain market has a very strong artificial intelligence characteristic, and can combine strong original resources and advantages of the two. Machine learning allows training without revealing sensitive private data, and based on the incentive mechanisms with blockchains, these incentive mechanisms allow these systems to attract optimal data and models, making them more intelligent. By the development, an open market can be formed finally, any user can sell data and keep the privacy of the data, developers can use incentive measures to attract the optimal data to the user's algorithm, and the cooperation of the two enables the machine learning model to be more complete.
In one possible example, the processing module 302 stores process parameter outliers in the second database and stores production verification product parameters in the second database.
For example, the logging process may be implemented in the following specific manner:
executing an IF prediction algorithm to finish OR mass production and finish THEN;
presetting the process parameter X = (X)1,X2To Xm) Storing the data into a second database;
abnormal value X of the process parametern(the abnormal value of the process parameter) is stored in a second database;
the product parameter Y of the production verification result is obtained1-YnAnd storing the data into a second database.
In addition, in the companion environment in which the present embodiment operates,or by relying on a second database, a new training set is encoded according to the nonstandard working condition corresponding to the abnormal value of the process parameter, and the format of the training set is set as follows: the number, the nonstandard working condition code, the production batch, the whole-process working condition value, the preset value and the quality characteristic value of the corresponding product. When an abnormal condition is encountered, the training set of the nonstandard condition can be updated, specifically, if all abnormal X of the batch production of the abnormal condition are abnormalnIf the working condition codes are available, storing the data of the batch into a training set database; if the training samples do not meet the overall requirements or the data quality is abnormal, the batch of data may be deleted from the training set.
In one possible example, the abnormal operation condition is processed based on the second database, and the processing module 302 monitors the abnormal operation condition and updates the production process data.
For example, at the beginning of production, the processing module 302 monitors the hydrogen production pressure used by the equipment, and at this time, the hydrogen production pressure is a normal condition, and after the production is continued for 20 minutes, it is found that the hydrogen production pressure used by the equipment is insufficient and is far lower than the pressure of the normal condition, which is one of the abnormal conditions. When abnormal working conditions occur under the working conditions of mass production, the production flow PnParameter X ofnAnd if the standard range of the design parameters is exceeded, generating nonstandard working condition codes. Meanwhile, due to the occurrence of abnormal working conditions, the types of the process parameters need to be combined with XnThe trend is predicted if it is invariable XnGenerating a nonstandard working condition code according to the current parameter value, and predicting a preset value; if it is variable XnAnd according to the average value of the process parameters of the sampling points of the database for a period of time, giving a predicted average value in the production time.
In one possible example, the processing module 302 generates the process parameter data item when an anomaly in the process parameter occurs.
For example, when the production is performed each time, if X monitors an abnormality, a process parameter data item of the process parameter X is automatically generated, the name of the process parameter data item is described from the viewpoint of practicality, and the process parameter data item can also be referred to as an abnormal process parameter database from the viewpoint of data management.
In one possible example, the processing module 302 generates the process parameter data item based on at least one of a batch standard reaction time, a benchmarking elapsed reaction time, a remaining reaction time, a number of parameter changes, a change time period location, and a process parameter mean.
For example, the function of the process parameter data item is to record each item of data in detail when the working condition is abnormal, and in order to record the abnormal working condition in multiple angles, the following concept settings can be made in the process parameter data item:
a: the number of the database address corresponds to the database address of the fault code, the detailed information of the abnormal parameter is inquired or updated by using the address, the fault code is explained, and the code form can be (the number of the abnormal flow, the number of the abnormal parameter: the flow number, the process parameter fault code, the fault code available mark and the address number of the abnormal parameter data item);
b: lot number, and corresponding device P when abnormality occursnThe corresponding production batch number is convenient for quality tracing and data analysis;
c: the standard batch reaction time means the average time of the production reaction of the product in the device under the working condition of the marker post, and the time can be obtained by utilizing the working condition score card of the marker post;
d: when the working condition of the marker post is reacted for a certain time and X is abnormal, the device PnThe time of the produced reaction under the working condition of the marker post, if the time is more than 0, the reaction is already generated;
e: residual reaction time, the residual reaction time in standard working condition production = batch standard reaction time-post working condition production reaction time; in addition, a compensation algorithm for material parameters can also be processed using the time;
the use of the remaining reaction time is versatile, and for example, we can use it to make the following conceptual setup, so that the following formula relationship is used to obtainA certain total amount of gas Y. With H2As specific examples thereof:
P1is (working condition H of marker post)2Pressure);
t is standard batch reaction time under the working condition of the marker post;
T1the working condition of the marker post is the reaction time;
P2for abnormal working condition H2The average pressure;
T2is the reaction time;
Y（H2total dosage) is constant under the working condition of the marker post;
then through Y (H)2Total amount) = F (P)1,T)=F(P1,T1)+F(P2,T2) By the formula (I), we can obtain H2Total gas usage Y. From the above examples we can see that the residual reaction time is of varying use, which plays an important role in the process parameter data item.
We can also use it to calculate the reaction time T2With H2For purposes of illustration, H2The pressure substantially corresponds to H2The total reaction dosage of Y, the dosage of hydrogen is according to the Avogastron's law, the ideal gas state equation PV = nRT; at the same temperature and volume, the proportion of gas pressure is equal to the proportion of gas mass, assuming the hydrogen pressure P of the benchmarking working condition1The amount of hydrogen is n1(number of moles), hydrogen pressure P in abnormal conditions2Amount of hydrogen n2Then P1/P2=n1/n2If the total amount of hydrogen is required to be ensured, the reaction time needs to be compensated when the hydrogen pressure is low because the volume of the reaction kettle is fixed and the temperature is not changed. For its specific process, for example, Y = F (P, T), P being the pressure and T being H2Reaction time of (2), YSign board=F(P1,T1)=YNonstandard=F(P2,T2) If the pressure, standard reaction time, P of the working condition of the marker post are known1And T1By obtaining P2Can find T2。
Next, the embodiments of the present application will be described in detail with reference to the contents of the process parameter data items, and the following settings are made:
f, the number of times of change of the parameter X, evenly divide the time period into N equal parts, for example, evenly divide 8 hours into 32 equal parts, each time period is 15 minutes, and the number of times of change of the process parameter in the 32 equal parts is counted by the method;
changing the time period position, for example, an array or a queue with the coding length of N exists, when the digit of the array is 1, the change is shown at the several equant points, and when the array is 0, the change is not shown;
h, the average value of the process parameters, wherein the specific calculation process is that the average value of the process parameters in the abnormal parameter production time is = (∑ H)n）/n；
One function of the method is that if the preset value is in the range of the mean value, the prediction algorithm is successful, next, the fault code interval of the mean value is judged, and the fault code is updated; in addition, the method also has a judging function, if the residual time is over and the preset value is judged to be unequal to the average value, the prediction is failed, a compensation algorithm is carried out, the fault code is updated, and the fault code is set to be unavailable.
After the concept is set in the process parameter data item, the process parameter data item forms a process parameter database, and in order to further explain the advantages of the process parameter data item, the detailed parameters of the process parameter database are used for example: for example, A is 3, indicating its address in the database, line 3 in the database; b is 20220201, which indicates that when abnormality occurs, the corresponding batch number is 20220201; c is 480, indicating a batch standard reaction time of 480 minutes; d is 30, which indicates that the working condition of the marker post has reacted for 30 minutes, namely the device P occurs at the abnormal moment of XnThe time of the produced reaction is 30 minutes under the working condition of the marker post; e is 450, indicating a residual reaction time of 450 minutes; f is 1, indicating that the parameter X is changed 1 time; g is 0010.0000.0000.0000.0000.0000.0000.0000, indicating that the data changed at the third bisecting point of array 0010; h is 613.2047073, indicating a mean process parameter of 613.2047073.
In one possible example, the processing module 302 obtains the data corresponding to the batch number from the second database based on the batch number of the product; and analyzing the variation trend of the nonstandard parameters in the batch number based on the corresponding data, and resetting the preset value.
For example, a batch number of a batch is obtained from the second database, data corresponding to the batch number is obtained from the second database, a variation trend of the abnormal parameter in the batch is determined according to the value of the H queue based on the corresponding data, then a coordinate graph related to time and pressure is generated according to the variation trend, the total hydrogen consumption is determined according to the coordinate graph, the compensation time is determined according to the current hydrogen supply pressure and the total hydrogen consumption, and the preset value of the abnormal parameter is predicted again according to the compensation time. And, the working condition code can be regenerated according to the preset value, and the original working condition code is updated in the database, for example, the preset value of the process parameter is predicted before the algorithm is recommended each time, the prediction method is that the average value of the prediction of the whole reaction interval is used for carrying out the prediction algorithm, thereby reducing the error of the prediction algorithm, and the specific logic process is as follows:
if predicting a process parameter preset value = H actual mean value interval;
the fault code available flag =1 corresponding to the THEN X parameter;
the fault code available flag =0 corresponds to the ELSE X parameter.
The steps belong to the processing of the abnormal working conditions, and in addition, in the step of processing the abnormal working conditions, the process parameter data items are required to be dynamically updated. For example, each time production is completed, for XnThe monitoring of (1) is analyzed, and X in the nonstandard working condition codes is updatednThe fault coding available mark corresponding to the parameter; at the completion of each production run, X needs to be recordednThe mean value of the process parameters; storing the average value of the H queue in each sampling time period, and recording the times of parameter change; preservation of Exception XnThe corresponding production batch number and the production time of the working condition of the marker post are saved.
Referring to fig. 4, fig. 4 is a structural diagram of an abnormal condition-based database management apparatus according to an embodiment of the present application. As shown in fig. 4, the abnormal situation-based database management apparatus 400 includes a processor 401, a memory 402, a communication interface 404, and one or at least one program 403. The one or more programs 403 are stored in the memory 402 and configured to be executed by the processor 401, the programs 403 including instructions for:
when an abnormal working condition exists, generating a nonstandard working condition code according to the abnormal working condition, and judging whether a first database corresponding to the nonstandard working condition code exists or not;
when the first database does not exist, a second database is newly established, and nonstandard working condition data are stored in the second database;
and processing the abnormal working condition based on the nonstandard working condition data of the second database.
In one possible example, the second database includes a parameter scorecard, the parameter scorecard includes the adjustable preset value, and the program 403 is specifically configured to execute instructions for:
and calculating a preset value by using a prediction algorithm according to the abnormal working condition.
In one possible example, the program 403 is specifically configured to execute the following steps:
storing the abnormal values of the process parameters into the second database;
and storing the production verification result product parameters into the second database.
In one possible example, the program 403 is specifically configured to execute the following steps:
monitoring the abnormal working condition;
and updating the production process data.
In one possible example, the program 403 is specifically configured to execute the following steps:
and when the process parameters are abnormal, generating the process parameter data items.
In one possible example, the program 403 is specifically configured to execute the following steps:
and generating the process parameter data item based on at least one of batch standard reaction time, post working condition reacted time, residual reaction time, parameter change times, change time period positions and process parameter mean values.
In one possible example, the program 403 is specifically configured to execute the following steps:
based on the batch number of the product, acquiring data corresponding to the batch number from the second database;
and analyzing the variation trend of the nonstandard parameters in the batch number based on the corresponding data, and resetting the preset value.
Those skilled in the art will appreciate that only one memory 402 and processor 401 are shown in fig. 4 for ease of illustration. In an actual terminal or server, there may be multiple processors and memories. The memory may also be referred to as a storage medium or a storage device, and the like, which is not limited in this application.
It should be understood that, in the embodiment of the present Application, the processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. The processor may also be a general-purpose microprocessor, a Graphics Processing Unit (GPU), or one or more integrated circuits, and is configured to execute the relevant programs to implement the functions required to be executed in the embodiments of the present application.
The processor 401 may also be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the present application may be implemented by integrated logic circuits in hardware or instructions in software in the processor 401. The processor 401 described above may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present application. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash and rom, programmable rom or electrically erasable programmable memory, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 402, and the processor 401 reads information in the memory 402, and completes functions required to be executed by the units included in the method, apparatus, and storage medium of the embodiments of the present application in combination with hardware thereof.
It will also be appreciated that the memory referred to in the embodiments of the application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct bus RAM (DR RAM). The Memory may also be, but is not limited to, a Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, optical disk storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor via a bus. The memory may also be integrated with the processor, and the memory may store a program, which when executed by the processor is adapted to perform the steps of the method of the present application as defined in the above embodiments.
It should be noted that when the processor is a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, the memory (memory module) is integrated in the processor. It should be noted that the memory described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in a processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and performs the steps of the above method in combination with hardware thereof, which are not described in detail herein to avoid repetition.
Those of ordinary skill in the art will appreciate that the various Illustrative Logical Blocks (ILBs) and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer programmed program product. The computer program product includes one or more computer instructions. When loaded and executed on a processor, cause the processes or functions described in accordance with the embodiments of the application to occur in whole or in part. The computer may be a general purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (e.g., coaxial cable, optical fiber) or wireless (e.g., infrared, wireless, microwave, etc.) manner, or may be transmitted from one website, computer, server or data center to a mobile phone processor through a wired manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk), among others.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application.
Claims (10)
1. A method for managing a database based on abnormal working conditions is characterized by comprising the following steps:
when abnormal working conditions exist, generating nonstandard working condition codes, judging whether a first database corresponding to the nonstandard working condition codes exists or not, and when the first database does not exist, establishing a second database;
acquiring process parameters corresponding to the nonstandard working condition codes, storing the process parameters into the second database, and recording production product data and production process data corresponding to the process parameters into the second database;
and processing the abnormal working conditions based on the second database, and resetting the preset values of the process parameters.
2. The method of claim 1, wherein the second database comprises a parameter scorecard, the parameter scorecard comprising the adjustable preset values, the preset values calculated using a predictive algorithm based on the abnormal operating conditions.
3. The method of claim 1, wherein the step of recording production product data and production process data corresponding to the process parameters in the second database comprises the steps of:
storing the abnormal values of the process parameters into the second database;
and storing the production verification result product parameters into the second database.
4. The method of claim 1, wherein said processing said abnormal operating condition based on said second database comprises the steps of:
monitoring the abnormal working condition;
and updating the production process data.
5. The method of claim 4, wherein said monitoring said abnormal operating condition comprises the steps of:
and when the process parameters are abnormal, generating the process parameter data items.
6. The method of claim 5, wherein said generating said process parameter data item when an anomaly in said process parameter occurs comprises the steps of:
and generating the process parameter data item based on at least one of batch standard reaction time, post working condition reacted time, residual reaction time, parameter change times, change time period positions and process parameter mean values.
7. The method of claim 1, wherein said processing said abnormal operating condition based on said second database to reset said preset values of said process parameters comprises the steps of:
based on the batch number of the product, acquiring data corresponding to the batch number from the second database;
and analyzing the variation trend of the nonstandard parameters in the batch number based on the corresponding data, and resetting the preset value.
8. An apparatus for abnormal situation based database management, configured to perform the method of any of claims 1-7.
9. An apparatus based abnormal situation database management comprising a processor, a memory, and one or at least one program, wherein the one or at least one program is stored in the memory and configured to be executed by the processor, the program comprising instructions for performing the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program causing a computer to execute to implement the method of any one of claims 1-7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210256168.3A CN114356895B (en) | 2022-03-16 | 2022-03-16 | Abnormal condition database management-based method, device, equipment and storage medium |
| CN202210676370.1A CN115129687A (en) | 2022-03-16 | 2022-03-16 | Abnormal condition database management-based method, related device, medium and program |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210256168.3A CN114356895B (en) | 2022-03-16 | 2022-03-16 | Abnormal condition database management-based method, device, equipment and storage medium |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210676370.1A Division CN115129687A (en) | 2022-03-16 | 2022-03-16 | Abnormal condition database management-based method, related device, medium and program |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114356895A true CN114356895A (en) | 2022-04-15 |
| CN114356895B CN114356895B (en) | 2022-06-07 |
Family
ID=81094751
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210256168.3A Active CN114356895B (en) | 2022-03-16 | 2022-03-16 | Abnormal condition database management-based method, device, equipment and storage medium |
| CN202210676370.1A Pending CN115129687A (en) | 2022-03-16 | 2022-03-16 | Abnormal condition database management-based method, related device, medium and program |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210676370.1A Pending CN115129687A (en) | 2022-03-16 | 2022-03-16 | Abnormal condition database management-based method, related device, medium and program |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN114356895B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114493057A (en) * | 2022-04-18 | 2022-05-13 | 希望知舟技术(深圳)有限公司 | Production process parameter recommendation method based on abnormal working conditions and related equipment |
| CN114756595A (en) * | 2022-06-14 | 2022-07-15 | 希望知舟技术(深圳)有限公司 | Data processing method for database and related device |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6424877B1 (en) * | 1997-04-04 | 2002-07-23 | Minolta Co., Ltd. | Reproduction apparatus |
| JP2009110439A (en) * | 2007-10-31 | 2009-05-21 | Nec Corp | Database access system, database access method and program |
| CN101598263A (en) * | 2009-07-10 | 2009-12-09 | 东北大学 | Portable pipeline leakage detection method and device |
| CN102272743A (en) * | 2011-05-09 | 2011-12-07 | 华为终端有限公司 | Management method for information of universal integrated circuit card and device thereof |
| CN202639268U (en) * | 2012-03-02 | 2013-01-02 | 中冶连铸技术工程股份有限公司 | Online control system for surface quality of continuous casting billet |
| CN103676836A (en) * | 2013-10-17 | 2014-03-26 | 中国石油化工股份有限公司 | Online safe operation guiding method |
| CN109240261A (en) * | 2018-09-10 | 2019-01-18 | 汽解放汽车有限公司 | A kind of HIL test method for car networking performance test |
| CN109753417A (en) * | 2018-12-17 | 2019-05-14 | 新视家科技(北京)有限公司 | Abnormal process management method and its device, electronic equipment, computer-readable medium |
| CN110285988A (en) * | 2019-06-10 | 2019-09-27 | 江苏徐工工程机械研究院有限公司 | Hydraulic cylinder barrel inner hole straightness control system |
| CN111546854A (en) * | 2020-06-18 | 2020-08-18 | 中南大学 | On-road identification and diagnosis method for intelligent train air conditioning unit |
| CN112099450A (en) * | 2020-09-16 | 2020-12-18 | 江苏徐工工程机械研究院有限公司 | Product quality control method, server, terminal, system and storage medium |
| CN112491854A (en) * | 2020-11-19 | 2021-03-12 | 郑州迪维勒普科技有限公司 | Multi-azimuth security intrusion detection method and system based on FCNN |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7503052B2 (en) * | 2004-04-14 | 2009-03-10 | Microsoft Corporation | Asynchronous database API |
| CN104091035B (en) * | 2014-07-30 | 2017-02-08 | 中国科学院空间应用工程与技术中心 | Health monitoring method for effective loads of space station based on data-driven algorithm |
| CN105095056B (en) * | 2015-08-14 | 2018-01-12 | 焦点科技股份有限公司 | A kind of method of data warehouse data monitoring |
| US10382465B2 (en) * | 2016-01-22 | 2019-08-13 | Virsec Systems, Inc. | Pattern matching based dataset extraction |
| CN107506451B (en) * | 2017-08-28 | 2020-11-03 | 泰康保险集团股份有限公司 | Abnormal information monitoring method and device for data interaction |
| CN108446379A (en) * | 2018-03-16 | 2018-08-24 | 中国长江三峡集团有限公司 | Fault Diagnosis Method of Hydro-generating Unit |
| CN110659255A (en) * | 2018-06-28 | 2020-01-07 | 武汉斗鱼网络科技有限公司 | Data migration method, device and system and storage medium |
| CN110858194A (en) * | 2018-08-16 | 2020-03-03 | 北京京东尚科信息技术有限公司 | Method and device for expanding database |
| CN113312380B (en) * | 2021-07-29 | 2022-01-07 | 湖南五凌电力科技有限公司 | Method and device for automatically acquiring vibration region of hydroelectric generating set based on data driving |
| CN113704354B (en) * | 2021-08-30 | 2023-10-20 | 康键信息技术（深圳）有限公司 | Data synchronization method and device, computer equipment and storage medium |
| CN115130737A (en) * | 2022-02-08 | 2022-09-30 | 希望知舟技术(深圳)有限公司 | Machine learning method based on working condition, related device and medium program product |
-
2022
- 2022-03-16 CN CN202210256168.3A patent/CN114356895B/en active Active
- 2022-03-16 CN CN202210676370.1A patent/CN115129687A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6424877B1 (en) * | 1997-04-04 | 2002-07-23 | Minolta Co., Ltd. | Reproduction apparatus |
| JP2009110439A (en) * | 2007-10-31 | 2009-05-21 | Nec Corp | Database access system, database access method and program |
| CN101598263A (en) * | 2009-07-10 | 2009-12-09 | 东北大学 | Portable pipeline leakage detection method and device |
| CN102272743A (en) * | 2011-05-09 | 2011-12-07 | 华为终端有限公司 | Management method for information of universal integrated circuit card and device thereof |
| CN202639268U (en) * | 2012-03-02 | 2013-01-02 | 中冶连铸技术工程股份有限公司 | Online control system for surface quality of continuous casting billet |
| CN103676836A (en) * | 2013-10-17 | 2014-03-26 | 中国石油化工股份有限公司 | Online safe operation guiding method |
| CN109240261A (en) * | 2018-09-10 | 2019-01-18 | 汽解放汽车有限公司 | A kind of HIL test method for car networking performance test |
| CN109753417A (en) * | 2018-12-17 | 2019-05-14 | 新视家科技(北京)有限公司 | Abnormal process management method and its device, electronic equipment, computer-readable medium |
| CN110285988A (en) * | 2019-06-10 | 2019-09-27 | 江苏徐工工程机械研究院有限公司 | Hydraulic cylinder barrel inner hole straightness control system |
| CN111546854A (en) * | 2020-06-18 | 2020-08-18 | 中南大学 | On-road identification and diagnosis method for intelligent train air conditioning unit |
| CN112099450A (en) * | 2020-09-16 | 2020-12-18 | 江苏徐工工程机械研究院有限公司 | Product quality control method, server, terminal, system and storage medium |
| CN112491854A (en) * | 2020-11-19 | 2021-03-12 | 郑州迪维勒普科技有限公司 | Multi-azimuth security intrusion detection method and system based on FCNN |
Non-Patent Citations (2)
| Title |
|---|
| W. PENZO: "Rewriting rules to permeate complex similarity and fuzzy queries within a relational database system", 《KNOWLEDGE AND DATA ENGINEERING》, vol. 17, no. 2, 10 January 2005 (2005-01-10), pages 255 - 270, XP011124656, DOI: 10.1109/TKDE.2005.33 * |
| 邓建旺 等: "管带机智能巡检***设计及与输送机的配合设计", 《科学技术创新》, 25 December 2021 (2021-12-25), pages 55 - 57 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114493057A (en) * | 2022-04-18 | 2022-05-13 | 希望知舟技术(深圳)有限公司 | Production process parameter recommendation method based on abnormal working conditions and related equipment |
| CN114756595A (en) * | 2022-06-14 | 2022-07-15 | 希望知舟技术(深圳)有限公司 | Data processing method for database and related device |
| CN114756595B (en) * | 2022-06-14 | 2022-08-26 | 希望知舟技术(深圳)有限公司 | Data processing method and related device for database |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115129687A (en) | 2022-09-30 |
| CN114356895B (en) | 2022-06-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114356895B (en) | Abnormal condition database management-based method, device, equipment and storage medium | |
| US11288602B2 (en) | Computer-based systems, computing components and computing objects configured to implement dynamic outlier bias reduction in machine learning models | |
| US11106486B2 (en) | Techniques to manage virtual classes for statistical tests | |
| US11694109B2 (en) | Data processing apparatus for accessing shared memory in processing structured data for modifying a parameter vector data structure | |
| Herbst et al. | Sequential Monte Carlo sampling for DSGE models | |
| US9576262B2 (en) | Self learning adaptive modeling system | |
| US20220277232A1 (en) | Computer-based systems, computing components and computing objects configured to implement dynamic outlier bias reduction in machine learning models | |
| JP7139932B2 (en) | Demand forecasting method, demand forecasting program and demand forecasting device | |
| US20230351426A1 (en) | Techniques to predict and implement an amortized bill payment system | |
| CN110634030A (en) | Application service index mining method, device and equipment | |
| CN116739618B (en) | Variable code tracing system based on block chain and data processing method | |
| WO2022252630A1 (en) | Model set-based data prediction method, apparatus and device, and storage medium | |
| US11797565B2 (en) | Data validation using encode values | |
| CN110968564A (en) | Data processing method and training method of data state prediction model | |
| CN111078500A (en) | Method and device for adjusting operation configuration parameters, computer equipment and storage medium | |
| US20210342691A1 (en) | System and method for neural time series preprocessing | |
| JP7015927B2 (en) | Learning model application system, learning model application method, and program | |
| WO2022156064A1 (en) | Flash memory chip reliability level prediction method, apparatus, and storage medium | |
| CN109214544A (en) | Life prediction and maintenance measures method and apparatus and storage medium, computer | |
| US20220058668A1 (en) | Adaptive forecasting models | |
| CN113627513A (en) | Training data generation method and system, electronic device and storage medium | |
| CN111382874B (en) | Method and device for realizing update iteration of online machine learning model | |
| Görtz et al. | Solving models with jump discontinuities in policy functions | |
| CN116611930A (en) | Recommendation method and device for financial products, processor and electronic equipment | |
| GB2612960A (en) | Authorisation system and methods |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |