Method, related device, medium and program based on abnormal operating condition database management

Abstract

The embodiment of the present application discloses a method, related device, medium and program based on abnormal working condition database management, the method comprising the following steps: when an abnormal working condition exists, a non-standard working condition code is generated, and it is determined whether there is a first database corresponding to the non-standard working condition code, and when the first database does not exist, a new second database is created; the process parameters corresponding to the non-standard working condition code are obtained, the process parameters are stored in the second database, and the production product data and production process data corresponding to the process parameters are recorded in the second database; based on the second database, the abnormal working condition is processed and the preset value of the process parameter is reset. The present application can greatly improve the adjustment efficiency of production under non-standard working conditions.

Description

Technical Field

[0001] The present application belongs to the general data processing field of the Internet industry, and mainly involves a method, related devices, media and programs based on abnormal operating condition database management. Background Art

[0002] At present, the general benchmark operating condition database is adapted to simple production process parameters. When non-standard operating conditions occur in production, it is necessary to adjust the process parameters of the corresponding database to keep the production quality at a good level.

[0003] In the prior art, the process parameters of the production database are generally adjusted based on the production personnel's years of experience to adapt to changes in non-standard working conditions, and the adjustment process is inefficient. Summary of the invention

[0004] One purpose of the present application is to provide a method, related device and related medium program based on abnormal working condition database management, which has the advantage of greatly improving the adjustment efficiency of production under non-standard working conditions.

[0005] To achieve the above objectives, in a first aspect, an embodiment of the present application provides a method for managing an abnormal operating condition database, which includes:

[0006] When an abnormal working condition exists, a non-standard working condition code is generated, and it is determined whether there is a first database corresponding to the non-standard working condition code. If the first database does not exist, a new second database is created;

[0007] Acquire the process parameters corresponding to the non-standard working condition code, store the process parameters in the second database, and record the production product data and production process data corresponding to the process parameters in the second database;

[0008] Based on the second database, the abnormal operating condition is processed and the preset value of the process parameter is reset.

[0009] It can be understood that when an abnormal operating condition exists, a non-standard operating condition code is generated, and it is determined whether there is a first database corresponding to the non-standard operating condition code. When the first database does not exist, a new second database is created to obtain the process parameters corresponding to the non-standard operating condition code, and the process parameters are stored in the second database. The production product data and production process data corresponding to the process parameters are recorded in the second database. Based on the second database, the abnormal operating condition is processed and the preset value of the process parameter is reset, which can improve the adjustment efficiency of production under non-standard working conditions.

[0010] In a possible example, the second database includes a parameter scorecard, and the parameter scorecard includes the adjustable preset value, and the preset value is calculated using a prediction algorithm according to the abnormal operating condition.

[0011] It can be understood that, according to the abnormal operating conditions, the preset value is calculated using a prediction algorithm, which can optimize the accuracy of the preset value.

[0012] In a possible example, recording the production product data and the production process data corresponding to the process parameters in the second database includes the following steps:

[0013] storing abnormal values ​​of process parameters in the second database;

[0014] The production verification result product parameters are stored in the second database.

[0015] It can be understood that storing abnormal values ​​of process parameters in the second database and storing product parameters of production verification results in the second database can optimize the completeness of production product data and production process data.

[0016] In a possible example, processing the abnormal operating condition based on the second database includes the following steps:

[0017] monitoring the abnormal operating condition;

[0018] The production process data is updated.

[0019] It can be understood that monitoring the abnormal operating conditions and updating the production process data can optimize the efficiency of coping with abnormal operating conditions.

[0020] In a possible example, monitoring the abnormal operating condition includes the following steps:

[0021] When the process parameter is abnormal, the process parameter data item is generated.

[0022] It can be understood that when the process parameters are abnormal, the process parameter data items are generated to optimize the monitoring of the process parameters.

[0023] In a possible example, when the process parameter is abnormal, generating the process parameter data item includes the following steps:

[0024] The process parameter data item is generated based on at least one of the batch standard reaction time, benchmark condition reaction time, remaining reaction time, number of parameter changes, change time period position and process parameter mean.

[0025] It can be understood that generating the process parameter data item based on at least one of the batch standard reaction time, benchmark operating condition reaction time, remaining reaction time, number of parameter changes, change time period position and process parameter mean value can improve the recording efficiency of abnormal process parameters.

[0026] In a possible example, processing the abnormal operating condition based on the second database and resetting the preset value of the process parameter includes the following steps:

[0027] Based on the batch number of the product, obtaining data corresponding to the batch number from the second database;

[0028] Based on the corresponding data, the change trend of the non-standard parameters in the batch number is analyzed, and the preset value is reset.

[0029] It can be understood that obtaining data corresponding to the batch number from the second database, analyzing the change trend of the non-standard parameters in the batch number, and resetting the preset value can optimize the efficiency of handling abnormal working conditions.

[0030] In a second aspect, a device based on abnormal operating condition database management includes a module for executing the method provided by the first aspect or any embodiment of the first aspect.

[0031] In the third aspect, a device based on abnormal operating condition database management is characterized in that it includes 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 is configured to be executed by the processor, and the program includes instructions for executing the method provided in the first aspect or any embodiment of the first aspect.

[0032] In a fourth aspect, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium stores a computer program, and the computer program enables a computer to execute to implement the method provided in the first aspect or any embodiment of the first aspect.

[0033] Implementing the embodiments of the present application will have the following beneficial effects:

[0034] When an abnormal operating condition exists, a non-standard operating condition code is generated according to the abnormal operating condition, and it is determined whether there is a first database corresponding to the non-standard operating condition code; when the first database does not exist, a new second database is created, and the non-standard operating condition data is stored in the second database; based on the non-standard operating condition data in the second database, the abnormal operating condition is processed, thereby greatly improving the adjustment efficiency of production under non-standard operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0036] Figure 1 An application scenario diagram based on abnormal operating condition database management provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of a process based on abnormal operating condition database management provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of the structure of a database management device based on abnormal working conditions provided in an embodiment of the present application;

[0039] Figure 4 A structural diagram of an abnormal operating condition database management device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] The terms "1" and "2" etc. in this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0042] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0043] See also Figure 1 , Figure 1 This is an application scenario diagram based on abnormal operating condition database management provided by the embodiment of the present application. Figure 1 As shown, the application scenario diagram includes a user 101, an electronic device 102, and a server 103. It should be noted that: Figure 1 The number of devices, the form of devices, and the number of users in the system shown are for illustrative purposes only and do not constitute a limitation on the embodiments of the present application. One user can use multiple electronic devices.

[0044] 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 Figure 1 The laptop shown may also be a personal computer (PC), a smart phone, an all-in-one machine, a handheld computer, a tablet computer (pad), a smart TV player terminal, and a portable device. The operating system of the electronic device on the PC side, such as an all-in-one machine, may include but is not limited to Linux system, Unix system, Windows series system (such as Windows XP, Windows 7, etc.) and other operating systems. The operating system of the electronic device on the mobile side, such as a smart phone, may include but is not limited to Android system, IOS (Apple mobile phone operating system), Window system and other operating systems.

[0045] The following introduces a method for abnormal operating condition database management provided in an embodiment of the present application. The method can be executed by an abnormal operating condition database management device, which can be implemented by software and / or hardware and can generally be integrated in an electronic device or a server.

[0046] Please refer to Figure 2 , Figure 2 A flowchart of abnormal working condition database management is provided in an embodiment of the present application. The method is applied to the abnormal working condition database management process as an example. The device based on abnormal working condition database management may include a server or an electronic device. The method includes the following steps S201-S203, wherein:

[0047] S201: When an abnormal operating condition exists, a non-standard operating condition code is generated, and it is determined whether there is a first database corresponding to the non-standard operating condition code. If the first database does not exist, a new second database is created.

[0048] S202: Acquire process parameters corresponding to the non-standard working condition code, store the process parameters in the second database, and record production product data and production process data corresponding to the process parameters in the second database.

[0049] For example, the second database is established according to the following ideas:

[0050] IF there is no non-standard working condition code THEN;

[0051] Create a second database;

[0052] The second database is updated.

[0053] It should be noted that the abnormal value X in the second database is fixed, and the normal X is the same as the design score card. The design score card includes the target value of each process parameter. If this parameter is normal, the target value in the design score card is regarded as the parameter value of the parameter. During the update of the second database, the database corresponding to each production query non-standard working condition code is updated.

[0054] Furthermore, recording the production product data and production process data corresponding to the process parameters in the second database can also be implemented through blockchain technology, specifically including sharding the data generated by the production equipment or production process corresponding to the production product data and production process data, and then encrypting the data corresponding to at least one shard to obtain the encryption key of the at least one shard, and then generating a unique hash, i.e., a fixed-length encrypted output string, based on the encryption key or the data corresponding to the shard. The hash will be added to the ledger and shard metadata to link the transaction to the stored shard, and finally all transactions in the blockchain ledger will be recorded through the storage system, and the information will be synchronized between all nodes. In particular, the storage system can also replicate the data of each shard as needed to obtain multiple copies, and the number of copies and storage location corresponding to each shard data can be determined according to user needs or the priority of the shard.

[0055] S203: Based on the second database, the abnormal operating condition is processed and the preset value of the process parameter is reset.

[0056] See also Figure 3 , Figure 3 The schematic diagram of the structure of a database management device based on abnormal working conditions provided in an embodiment of the present application. Based on the above system architecture, the database management device based on abnormal working conditions 300 can be a server or a module in a server. The device 300 at least includes: a collection module 301 and a processing module 302, wherein:

[0057] The acquisition module 301 is used to acquire abnormal working conditions;

[0058] The processing module 302 is used to generate a non-standard operating condition code; determine whether there is a first database corresponding to the non-standard operating condition code; create a new second database; obtain the process parameters corresponding to the non-standard operating condition code; store the process parameters in the second database, and record the production product data and production process data corresponding to the process parameters in the second database; process the abnormal operating condition and reset the preset value of the process parameter.

[0059] In a 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.

[0060] For example, production process parameters are the index parameters of all production processes that control the production process to ensure the quality and efficiency of the products produced, including equipment control parameters (temperature, pressure and speed), production steps, production time and feed amount, etc. During production, multi-objective machine learning is performed according to the production conditions to obtain the machine learning training model of the production conditions. When abnormal process parameters occur, the training model is used to perform a prediction algorithm to adjust the normal adjustable X process parameters and give the optimal value (preset value) of the normal adjustable X, so that the adjusted production product indicators reach the optimal target. Among them, machine learning algorithms can be roughly divided into the following three types:

[0061] Supervised learning: is a machine learning task that establishes a mathematical (mapping) relationship between input X and output Y variables. Such X, Y pairs constitute the labeled data used to build a model in order to learn how to predict the output from the input.

[0062] Unsupervised learning: is a machine learning task that uses only input X variables, which are unlabeled data, and the learning algorithm uses the inherent structure of the data when modeling.

[0063] Reinforcement learning: is a machine learning task of deciding the next course of action, and it does this through trial and error learning, trying to maximize the reward.

[0064] In supervised learning, when data and model hypothesis space are given, you can construct an optimization problem, that is, how to determine the parameters to optimize your optimization goal. This is called training. When the training is completed, these parameters can be determined, and a certain hypothesis function in the hypothesis space determined by these parameters is the so-called model.

[0065] For adjusting the normal adjustable X process parameter, the optimal value (preset value) of the normal adjustable X is given. This embodiment uses the hyperparameter of parameter tuning as an example. Hyperparameters are essentially parameters of the machine learning algorithm, which directly affect the learning process and prediction performance. Since there is no "one-size-fits-all" hyperparameter setting that can be universally applied to all data sets, hyperparameter optimization (also called hyperparameter adjustment or model adjustment) is required.

[0066] One of the popular machine learning algorithms is the Support Vector Machine. The hyperparameters that need to be optimized are the C parameter and the gamma parameter of the Radial Basis Function (RBF) kernel (i.e., only the C parameter for a linear kernel; C and the exponent for a polynomial kernel). The C parameter is a penalty term to limit overfitting, while the gamma parameter controls the width of the RBF kernel. As mentioned above, tuning is usually done to arrive at an optimal set of values ​​for the hyperparameters, although there is also some research aimed at finding good starting values ​​for the C parameter and the gamma parameter.

[0067] In addition, the horizontal and vertical collaboration of machine learning is not limited in this embodiment. For example, the machine learning model trained based on the data generated by the blockchain market has a strong artificial intelligence feature, which can combine the powerful original resources and advantages of both. Machine learning allows training without revealing sensitive private data, and the incentive mechanism based on the blockchain allows these systems to attract the best data and models to make them more intelligent. In this way, an open market can be formed in the end, where any user can sell data and keep their data private, and developers can use incentives to attract the best data for the user's algorithm. The collaboration between the two can make the machine learning model more perfect.

[0068] In a possible example, the processing module 302 stores the abnormal values ​​of the process parameters in the second database, and stores the production verification result product parameters in the second database.

[0069] For example, the above deposit process can be carried out in the following specific ways:

[0070] IF the prediction algorithm is finished OR mass production is finished THEN;

[0071] The process parameter preset value X=(X1, X2 to X m ) is stored in a second database;

[0072] The abnormal value of the process parameter X n (the value of the abnormal process parameter) is stored in a second database;

[0073] The production verification result product parameters Y1-Y n Stored in the second database.

[0074] In addition, in the supporting environment of the operation of this embodiment, a new training set can also be created based on the second database according to the non-standard working condition code corresponding to the abnormal value of the process parameter, and the format of the training set is set as: number, non-standard working condition code, production batch, full process working condition value, preset value, and corresponding product quality characteristic value. When encountering abnormal working conditions, the training set of the non-standard working condition can be updated. Specifically, if all abnormal X of the abnormal working condition batch production n If the working condition code is available, the data of this batch is saved to the training set database; if the training samples do not meet the overall requirements or the data quality is abnormal, the data of this batch can be deleted from the training set.

[0075] In a possible example, the abnormal operating condition is processed based on the second database, the processing module 302 monitors the abnormal operating condition, and updates the production process data.

[0076] For example, at the beginning of production, the processing module 302 monitors the hydrogen production pressure used by the equipment. At this time, the hydrogen production pressure is in normal working condition. After 20 minutes of continuous production, it is found that the hydrogen production pressure used by the equipment is insufficient and far lower than the pressure of normal working condition. This situation belongs to one of the abnormal working conditions. When an abnormal working condition occurs under mass production conditions, the production process P n Parameter X n If the design parameters are exceeded, non-standard working condition codes will be generated. At the same time, due to the occurrence of abnormal working conditions, it is necessary to combine the type of process parameters to n Trend prediction, if it is immutable X n , then generate non-standard working condition code according to the current parameter value and predict the preset value; if it is variable X n , according to the mean value of process parameters at the database sampling points for a continuous period of time, the predicted mean value within the production time is given.

[0077] In a possible example, when the process parameter is abnormal, the processing module 302 generates the process parameter data item.

[0078] For example, each time a product is produced, if an abnormality occurs in monitoring X, a process parameter data item of process parameter X is automatically generated. The name of the above process parameter data item is a description from a practical perspective. From a data management perspective, the process parameter data item can also be called an abnormal process parameter database.

[0079] In one possible example, the processing module 302 generates the process parameter data item based on at least one of the batch standard reaction time, benchmark condition reaction time, remaining reaction time, number of parameter changes, change time period position and process parameter mean.

[0080] For example, the function of the process parameter data item is to record in detail various data when the working condition is abnormal. In order to record the abnormal working condition from multiple angles, the following conceptual settings can be made in the process parameter data item:

[0081] A: Database address, whose number corresponds to the database address of this fault code. The address is used to query or update the detailed information of the abnormal parameter. Here, the fault code is specially explained. The coding form can be (the number of abnormal processes, the number of abnormal parameters: process number, process parameter number, process parameter fault code, fault code available flag, abnormal parameter data item address number);

[0082] B: Batch number. When an abnormality occurs, the corresponding device P n The corresponding production batch number facilitates quality traceability and data analysis;

[0083] C: Batch standard reaction time, which means the average production reaction time of the product in the device under benchmark conditions. This time can be obtained using the benchmark condition scorecard;

[0084] D: Benchmark condition response time. When X occurs abnormally, the device P n The time of the produced reaction under the benchmark condition. If the time is greater than 0, it means that the reaction has occurred;

[0085] E: Remaining reaction time. The remaining reaction time of standard production conditions = batch standard reaction time - benchmark production reaction time. In addition, this time can also be used to process the compensation algorithm of material parameters.

[0086] The remaining reaction time has various uses. For example, we can use it to make the following conceptual settings, and then use the following formula relationship to obtain the total amount of a certain gas Y. Take H2 as a specific example:

[0087] P1 is (benchmark condition H2 pressure);

[0088] T is the standard reaction time of the benchmark batch;

[0089] T1 is the reaction time of the benchmark working condition;

[0090] P2 is the average pressure of H2 under abnormal conditions;

[0091] T2 is the reaction time;

[0092] The total amount of Y (H2 total amount) under the benchmark condition is a constant;

[0093] Then, through the formula Y(total H2 usage) = F(P1, T) = F(P1, T1) + F(P2, T2), we can calculate the total H2 gas usage Y. From the above examples, we can see that the remaining reaction time has various uses and plays an important role in the process parameter data items.

[0094] We can also use it to calculate the reaction time T2. Taking H2 as an example, the H2 pressure actually corresponds to the total amount of H2 used in the reaction, Y. The amount of hydrogen is based on Avogadro's law and the ideal gas state equation PV=nRT. At the same temperature and volume, the ratio of gas pressure is equal to the ratio of gas mass. Assuming the benchmark hydrogen pressure P1, the hydrogen consumption is n1 (number of moles), the abnormal hydrogen pressure P2, and the hydrogen consumption n2, then P1 / P2=n1 / n2. The greater the pressure, the more hydrogen is used, and the more is used in the same time. If the total amount of hydrogen is to be guaranteed, when the hydrogen pressure is low, since the volume of the reactor is fixed and the temperature does not change, then the reaction time needs to be compensated. For the specific process, for example, Y=F(P,T), P is the pressure, T is the reaction time of H2, and Y 标 =F(P1,T1)=Y 非标 =F(P2,T2), if the pressure, standard reaction time, P1 and T1 of the benchmark working condition are known, T2 can be calculated by obtaining P2.

[0095] Next, the embodiment of the present application further describes the contents of the process parameter data item in detail, and then makes the following settings:

[0096] F: Number of changes of parameter X. Divide the time period into N equal parts. For example, divide 8 hours into 32 equal parts. Each time period is 15 minutes. Use this method to count the number of changes of process parameters in 32 equal parts.

[0097] G: The position of the change time period. For example, there is an array or queue with a coding length of N. When the number of the array is 1, it indicates that the change occurred at the equal division point. When the array is 0, it indicates that there is no change.

[0098] H: mean value of process parameters. The specific calculation process is: mean value of abnormal parameter production time process parameters = (∑H n ) / n;

[0099] One of its functions is that if the preset value is within the mean range, it means that the prediction algorithm is successful. Next, the fault code interval of the mean is determined and the fault code is updated. In addition, it also has the following judgment function. If the remaining time is over and it is determined that the preset value is not equal to the mean, it means that the prediction has failed, and the compensation algorithm is performed, the fault code is updated, and the fault code is set to unavailable.

[0100] After setting the concepts in the process parameter data items, numerous process parameter data items constitute the process parameter database. In order to further illustrate the advantages of the process parameter data items, some detailed parameters of the process parameter database are used as examples: for example, A is 3, indicating that its address in the database is the third row in the database; B is 20220201, indicating that when an abnormality occurs, its corresponding batch number is 20220201; C is 480, indicating that the batch standard reaction time is 480 minutes; D is 30, indicating that the benchmark condition has reacted for 30 minutes, that is, when the abnormality occurs at X, the device P n Under the benchmark conditions, the production reaction time is 30 minutes; E is 450, indicating that the remaining reaction time is 450 minutes; F is 1, indicating that parameter X has changed once; G is 0010.0000.0000.0000.0000.0000.0000.0000, indicating that the data has changed at the third equal division point of array 0010; H is 613.2047073, indicating that the mean value of the process parameter is 613.2047073.

[0101] In a possible example, the processing module 302 obtains data corresponding to the batch number from the second database based on the batch number of the product; based on the corresponding data, analyzes the change trend of the non-standard parameters in the batch number and resets the preset value.

[0102] For example, the batch number of a batch is obtained from the second database, and the data corresponding to the batch number is obtained from the second database. Based on the corresponding data, the change trend of the abnormal parameters in the batch is determined according to the value of the H queue, and then a coordinate diagram about time and pressure is generated according to the change trend. The total hydrogen consumption is determined according to the coordinate diagram, and the compensation time is determined according to the current hydrogen pressure and the total hydrogen consumption. The preset value of the abnormal parameter is re-predicted according to the compensation time. In addition, the operating condition code can be regenerated according to the preset value, and the original operating condition code is updated in the database. For example, before each recommendation algorithm, the preset value of the process parameter is predicted. The prediction method is the predicted mean of the overall reaction interval, and the prediction algorithm is performed using the mean value, thereby reducing the error of the prediction algorithm. The specific logical process is given as an example:

[0103] If the predicted process parameter preset value = H actual mean interval;

[0104] THEN The fault code corresponding to parameter X is available flag = 1;

[0105] ELSE The fault code corresponding to the X parameter is available flag = 0.

[0106] The above steps are to deal with the abnormal working conditions. In addition, in the step of dealing with the abnormal working conditions, it is also necessary to dynamically update the process parameter data items. For example, each time production is completed, X n Analyze the monitoring and update the non-standard working condition coding n The fault code corresponding to the parameter can be marked; each time production is completed, it is necessary to record X n The process parameter mean value; in each sampling period, save the mean value of the H queue and record the number of parameter changes; save the abnormal X n The corresponding production batch number and the production time of the saved benchmark working condition.

[0107] See also Figure 4 , Figure 4 This is a structural diagram of a device for managing an abnormal operating condition database provided in an embodiment of the present application. Figure 4 As shown, the abnormal operating condition database management device 400 includes a processor 401, a memory 402, a communication interface 404, and one or at least one program 403. The one or at least one program 403 is stored in the memory 402 and is configured to be executed by the processor 401. The program 403 includes instructions for executing the following steps:

[0108] When an abnormal operating condition exists, generating a non-standard operating condition code according to the abnormal operating condition, and determining whether there is a first database corresponding to the non-standard operating condition code;

[0109] When the first database does not exist, creating a second database, and storing the non-standard working condition data in the second database;

[0110] The abnormal operating condition is processed based on the non-standard operating condition data in the second database.

[0111] In a possible example, the second database includes a parameter score card, the parameter score card includes the adjustable preset value, and the program 403 is specifically used to execute the following steps:

[0112] A prediction algorithm is used to calculate a preset value based on the abnormal operating condition.

[0113] In a possible example, the program 403 is specifically used to execute instructions for the following steps:

[0114] storing abnormal values ​​of process parameters in the second database;

[0115] The production verification result product parameters are stored in the second database.

[0116] In a possible example, the program 403 is specifically used to execute instructions for the following steps:

[0117] monitoring the abnormal operating condition;

[0118] The production process data is updated.

[0119] In a possible example, the program 403 is specifically used to execute instructions for the following steps:

[0120] When the process parameter is abnormal, the process parameter data item is generated.

[0121] In a possible example, the program 403 is specifically used to execute instructions for the following steps:

[0122] The process parameter data item is generated based on at least one of the batch standard reaction time, benchmark condition reaction time, remaining reaction time, number of parameter changes, change time period position and process parameter mean.

[0123] In a possible example, the program 403 is specifically used to execute instructions for the following steps:

[0124] Based on the batch number of the product, obtaining data corresponding to the batch number from the second database;

[0125] Based on the corresponding data, the change trend of the non-standard parameters in the batch number is analyzed, and the preset value is reset.

[0126] Those skilled in the art will appreciate that for ease of description, Figure 4 Only one memory 402 and processor 401 are shown. 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, etc., which is not limited in the embodiments of the present application.

[0127] It should be understood that in the embodiments 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 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor may also be a general-purpose microprocessor, graphics processing unit (GPU), or one or more integrated circuits for executing related programs to implement the functions required to be executed in the embodiments of the present application.

[0128] The processor 401 can also be an integrated circuit chip with signal processing capabilities. In the implementation process, the various steps of the present application can be completed by the integrated logic circuit of the hardware in the processor 401 or the instructions in the form of software. The above-mentioned processor 401 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor are combined and executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory and a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 402, and the processor 401 reads the information in the memory 402, and completes the functions required to be performed by the unit included in the method, device and storage medium of the embodiment of the present application in combination with its hardware.

[0129] It should also be understood that the memory mentioned in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct RAM bus random access memory (DR RAM). The memory can also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by the computer, but is not limited to this. The memory can be independent and connected to the processor through a bus. The memory may also be integrated with the processor, and the memory may store programs. When the programs stored in the memory are executed by the processor, the processor is used to execute the various steps of the determination method in the above embodiments of the present application.

[0130] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) is integrated into the processor. It should be noted that the memory described herein is intended to include but is not limited to these and any other suitable types of memory.

[0131] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0132] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory, and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0133] Those skilled in the art will appreciate that the various illustrative logical blocks (ILBs) and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0134] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a program product of computer programming. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a computer network, or other programmable device. The computer instructions can 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 can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center, or can be transmitted from a website site, computer, server or data center to a mobile phone processor by wired mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0135] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method based on abnormal operating condition database management, characterized in that: The following steps are involved: When an abnormal working condition exists, a non-standard working condition code is generated, and it is determined whether there is a first database corresponding to the non-standard working condition code. If the first database does not exist, a second database is newly created, wherein the second database includes a parameter score card, and the parameter score card includes adjustable preset values ​​of process parameters, and the preset values ​​are calculated using a prediction algorithm, and the process parameters include equipment control parameters, production time, and feed amount; Acquire the process parameters corresponding to the non-standard working condition code, store the process parameters in the second database, and record the production product data and production process data corresponding to the process parameters in the second database; Monitoring the abnormal working condition, and generating the process parameter data item when the process parameter is abnormal; Updating the production process data; Based on the batch number of the product, acquiring data corresponding to the batch number from the second database, the corresponding data including the process parameters, the production product data and the production process data; Based on the corresponding data, the change trend of the non-standard parameters in the batch number is analyzed, and the preset value is reset.

2. The method according to claim 1, characterized in that The step of recording the production product data and production process data corresponding to the process parameters in the second database comprises the following steps: storing abnormal values ​​of process parameters in the second database; The production verification result product parameters are stored in the second database.

3. The method according to claim 1, characterized in that When the process parameter is abnormal, generating the process parameter data item comprises the following steps: The process parameter data item is generated based on at least one of the batch standard reaction time, benchmark condition reaction time, remaining reaction time, number of parameter changes, change time period position and process parameter mean.

4. A device based on abnormal operating condition database management, characterized in that: Used to perform the method according to any one of claims 1 to 3.

5. A device based on abnormal operating condition database management, characterized in that: The invention comprises 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 according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which enables a computer to execute to implement the method according to any one of claims 1 to 3.

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