Order placing method, process parameter requesting method, and related device, medium and program

Abstract

The application relates to the technical field of production order scheduling, and particularly discloses a production order scheduling method, a process parameter request method, related equipment, a storage medium and a computer program, which can be applied to a production order scheduling system. The system can comprise a production order scheduling device, a production scheduling device, a user device and a production device. The production order scheduling method comprises the following steps: the production order scheduling device acquires working condition information of the production device and at least one to-be-allocated production batch allocated to the production device through the production scheduling device; the production order scheduling device respectively acquires a production batch number of each to-be-allocated production batch, and determines a training sample number corresponding to the production batch number of each to-be-allocated production batch according to preset production scheduling information; the production order scheduling device determines a production time of each to-be-allocated production batch according to the working condition information; the production order scheduling device generates a production work order according to the production time of each to-be-allocated production batch and the training sample number corresponding to the production batch number of each to-be-allocated production batch; and the production order scheduling device sends the production work order to the user device.

Description

Technical Field

[0001] This invention relates to the field of production scheduling technology, specifically to a scheduling method, a process parameter request method, and related equipment, storage media, and computer programs. Background Technology

[0002] In actual production, a work order may only correspond to a portion of the process for a single production batch. Therefore, when production personnel produce according to a work order, they can only produce based on the process parameters corresponding to that work order. If an abnormal condition occurs in a production batch, causing a delay in production time, the process parameters previously assigned to each work order will become disordered, causing problems for actual production. Summary of the Invention

[0003] To address the aforementioned problems in the prior art, this application provides a scheduling method, a process parameter request method, and related equipment, storage medium, and computer program. These methods can automatically predict the production batch time corresponding to subsequent work orders based on the current working conditions, enabling production personnel to obtain the correct process parameters in a timely manner when producing according to the work orders, thus ensuring production accuracy and efficiency.

[0004] In a first aspect, embodiments of this application provide a scheduling method applied to a scheduling system, the system including a scheduling device, a production scheduling device, a user device, and a production device, the method comprising:

[0005] The scheduling device obtains the operating status information of the production unit and at least one production batch to be allocated to the production unit through the production scheduling device;

[0006] The scheduling device acquires the production batch number of each production batch to be assigned from at least one production batch to be assigned, and determines the training sample number corresponding to the production batch number of each production batch to be assigned based on the preset scheduling information.

[0007] The scheduling device determines the production time for each production batch to be assigned based on the working conditions.

[0008] The scheduling device generates production work orders based on the production time of each production batch to be assigned and the training sample number corresponding to the production batch number of each production batch to be assigned.

[0009] The scheduling device sends production work orders to the user device.

[0010] Secondly, embodiments of this application provide a process parameter request method. This method is applied to a scheduling system, which includes a scheduling device, a production scheduling device, a user device, and a production device. The method includes:

[0011] The user device receives production work orders sent by the scheduling device.

[0012] The user device determines the target production batch based on the production work order and the current time.

[0013] The user device generates a viewing request based on the target production batch and sends the viewing request to the scheduling device. The viewing request is used to request to view the process parameters of the target production batch.

[0014] The user device receives the process parameters corresponding to the target production batch returned by the scheduling device and displays the process parameters corresponding to the target production batch to the user.

[0015] Thirdly, embodiments of this application provide a scheduling device, the device comprising:

[0016] The acquisition module is used to acquire the operating status information of the production device and at least one production batch to be allocated to the production device through the production scheduling device, and to acquire the production batch number of each production batch to be allocated in the at least one production batch to be allocated, and to determine the training sample number corresponding to the production batch number of each production batch to be allocated according to the preset production scheduling information.

[0017] The scheduling module is used to determine the production time of each production batch to be assigned based on the working condition information, and to generate a production work order based on the production time of each production batch to be assigned and the training sample number corresponding to the production batch number of each production batch to be assigned.

[0018] The dispatch module is used to send production work orders to user devices.

[0019] Fourthly, embodiments of this application provide a user device, the device comprising:

[0020] The receiving module is used to receive production work orders sent by the scheduling device;

[0021] The request module is used to determine the target production batch based on the production work order and the current time, generate a viewing request based on the target production batch, and send the viewing request to the scheduling device. The viewing request is used to request to view the process parameters of the target production batch.

[0022] The display module is used to receive the process parameters corresponding to the target production batch returned by the scheduling device and display the process parameters corresponding to the target production batch to the user.

[0023] Fifthly, embodiments of this application provide a scheduling device, including: a processor connected to a memory for storing a computer program, and the processor for executing the computer program stored in the memory, so that the scheduling device performs the method as described in the first aspect.

[0024] In a sixth aspect, embodiments of this application provide a user equipment, including: a processor connected to a memory for storing a computer program, and the processor for executing the computer program stored in the memory to cause the user equipment to perform the method as described in the second aspect.

[0025] In a seventh aspect, embodiments of this application provide a computer-readable storage medium storing a computer program that causes a computer to perform the method as described in the first or second aspect.

[0026] Eighthly, embodiments of this application provide a computer program product including a non-transitory computer-readable storage medium storing a computer program, operable to cause the computer to perform the methods of the first or second aspect.

[0027] Implementing the embodiments of this application has the following beneficial effects:

[0028] In this embodiment, the scheduling device first obtains the operating status information of the production unit and at least one production batch to be allocated to the production unit through the production scheduling device. It then obtains the production batch number of each of the at least one production batch to be allocated, and determines the training sample number corresponding to the production batch number of each production batch to be allocated based on preset production scheduling information. Next, the scheduling device determines the production time of each production batch to be allocated based on the operating status information. Then, based on the production time of each production batch to be allocated and the training sample number corresponding to the production batch number of each production batch to be allocated, a production work order is generated. Finally, the scheduling device sends the production work order to the user device. Thus, the scheduling device can automatically predict the time based on the current operating status to calculate the production batch time corresponding to subsequent batches of work orders, achieving real-time adjustment of the work orders. Simultaneously, through the correspondence between the production batch number and the training sample number, production personnel can query the corresponding training sample number based on the production batch number when producing according to the work order, thereby obtaining the correct process parameters in a timely manner, ensuring production accuracy and efficiency. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 A schematic diagram of the hardware structure of a scheduling device or user device provided for an embodiment of this application;

[0031] Figure 2 A scheduling system framework diagram provided for the implementation of this application;

[0032] Figure 3 A flowchart illustrating a scheduling method provided for an embodiment of this application;

[0033] Figure 4 A flowchart illustrating a method for determining the average processing time of a production device for handling the same abnormal conditions based on abnormal information when the abnormal condition is equipment malfunction, provided for an embodiment of this application.

[0034] Figure 5 A schematic diagram illustrating the process order for determining at least one sub-abnormality, provided for an embodiment of this application;

[0035] Figure 6 A flowchart illustrating a process parameter request method provided for embodiments of this application;

[0036] Figure 7 A functional module block diagram of a scheduling device provided for embodiments of this application;

[0037] Figure 8 A functional module block diagram of a user device provided for an embodiment of this application;

[0038] Figure 9 A schematic diagram of a scheduling device provided for embodiments of this application;

[0039] Figure 10 This is a schematic diagram of the structure of a user equipment provided for an embodiment of this application. Detailed Implementation

[0040] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0041] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0042] In this document, the term "implementation" means that a specific feature, result, or characteristic described in connection with an implementation may be included in at least one implementation of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same implementation, nor is it a separate or alternative implementation mutually exclusive with other implementations. It will be explicitly and implicitly understood by those skilled in the art that the implementations described herein can be combined with other implementations.

[0043] First, refer to Figure 1 , Figure 1 This is a schematic diagram of the hardware structure of a scheduling device or user device provided for an embodiment of this application. The scheduling device or user device may include at least one processor 101, a communication line 102, a memory 103, and at least one communication interface 104.

[0044] In this embodiment, the processor 101 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present application.

[0045] Communication line 102 may include a path for transmitting information between the aforementioned components.

[0046] The communication interface 104 can be any transceiver-like device (such as an antenna) used to communicate with other devices or communication networks, such as Ethernet, RAN, wireless local area networks (WLAN), etc.

[0047] The memory 103 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0048] In this embodiment, the memory 103 can exist independently and be connected to the processor 101 via the communication line 102. Alternatively, the memory 103 can be integrated with the processor 101. The memory 103 provided in this embodiment is typically non-volatile. The memory 103 stores computer execution instructions for implementing the scheme of this application, and its execution is controlled by the processor 101. The processor 101 executes the computer execution instructions stored in the memory 103 to implement the method provided in the following embodiments of this application.

[0049] In an optional implementation, the computer execution instructions may also be referred to as application code, and this application does not specifically limit this terminology.

[0050] In an optional implementation, processor 101 may include one or more CPUs, for example... Figure 1 CPU0 and CPU1 in the CPU.

[0051] In an optional implementation, the scheduling device or user device may include multiple processors, such as... Figure 1 Processors 101 and 107 are shown in the diagram. Each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor here may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0052] In optional implementations, if the scheduling device or user device is a server, for example, it can be a standalone server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The scheduling device or user device may further include an output device 105 and an input device 106. The output device 105 communicates with the processor 101 and can display information in various ways. For example, the output device 105 can be a liquid crystal display (LCD), a light-emitting diode (LED) display device, a cathode ray tube (CRT) display device, or a projector. The input device 106 communicates with the processor 101 and can receive user input in various ways. For example, the input device 106 can be a mouse, keyboard, touchscreen device, or sensor device.

[0053] The aforementioned order placement device or user device can be a general-purpose device or a special-purpose device. The embodiments of this application do not limit the type of order placement device or user device.

[0054] Secondly Figure 2 This application provides a framework diagram of a scheduling system. Specifically, the scheduling system may include: a scheduling device 201, a production scheduling device 202, a user device 203, and a production device 204. The scheduling device 201, production scheduling device 202, and user device 203 can all be smartphones (such as Android phones, iOS phones, Windows Phones, etc.), tablets, PDAs, laptops, mobile internet devices (MIDs), etc. Specifically, the scheduling device 201 communicates with the production scheduling device 202 to obtain the real-time operating status of the production device 204, and then schedules subsequent production batches based on the real-time operating status. The production scheduling device 202 is used to obtain the real-time operating status of the production device 204 and control the production parameters of the production device. The user device 203 is associated with the production device 204 and is used to receive production work orders sent by the scheduling device 201 and submit process parameter viewing requests to the scheduling device 201 to request access to process parameters. The production device 204 is used to perform production activities according to the process parameters.

[0055] In an optional implementation, the user device 203 may be integrated into the production device 204 to ensure the correspondence between the user device 203 and the production device 204, thereby further ensuring the accuracy of the issued process parameters. Simultaneously, the user device 203 integrated with the production device 204 cannot be easily taken out of the production area, which can, to a certain extent, prevent production data from being taken out and improve the security of production data.

[0056] In this embodiment, production personnel can obtain their daily production work order through their corresponding user device 203 and request the corresponding process parameters for the production time according to the production work order. The production work order is updated in real-time by the scheduling device 201 based on the production status and scheduling information of the production device 204. Specifically, the scheduling information can be pre-stored in the scheduling device 202, which monitors the production status of the production device 204 and sends this information to the scheduling device 201. Thus, the scheduling device 201 can use the operating status information of the production device 204 obtained from the scheduling device 202, and the scheduling information stored in the scheduling device 202, to determine at least one production batch to be allocated to the production device 204. Then, the production batch number of each of the at least one production batch to be allocated is obtained, and the training sample number corresponding to each production batch number is determined according to the preset scheduling information. Finally, the production time for each production batch to be allocated is determined based on the operating status information of the production device 204. Then, based on the production time of each production batch to be assigned and the training sample number corresponding to the production batch number of each production batch to be assigned, a production work order is generated. Finally, the scheduling device 201 sends the generated production work order to the user device 203 corresponding to the production device 204.

[0057] In this embodiment, the scheduling device 201 can automatically predict the production batch time corresponding to the next batch of work orders based on the current working conditions, thereby realizing real-time adjustment of the work orders. Simultaneously, through the correspondence between the production batch number and the training sample number, production personnel can query the corresponding training sample number based on the production batch number when producing according to the work order, thus obtaining the correct process parameters in a timely manner, ensuring production accuracy and efficiency.

[0058] The scheduling method disclosed in this application will be described in detail below:

[0059] See Figure 3 , Figure 3 This is a flowchart illustrating a scheduling method provided for an embodiment of this application. This scheduling method is applied to... Figure 2 The order scheduling system shown includes the following steps:

[0060] 301: The scheduling device obtains the operating status information of the production unit through the production scheduling device, and assigns at least one production batch to be allocated to the production unit.

[0061] In this embodiment, the production scheduling device 202 can pre-store planned production scheduling information and monitor the real-time operating status of the production device 204. Therefore, the order scheduling device 201 can obtain the current operating status information of the production device 204, as well as at least one production batch to be allocated to the production device 204, through communication with the production scheduling device 202.

[0062] In this embodiment, communication between the scheduling device 201 and the production scheduling device 202, and between the production scheduling device 202 and the production device 204, can be achieved using blockchain technology. Specifically, taking the transmission of working condition information between the scheduling device 201 and the production scheduling device 202 as an example, the production scheduling device 202 can encrypt the working condition information using the device number of the production device 204 corresponding to the working condition information, such as the equipment number. Simultaneously, based on the data characteristics of the working condition information and the device number of the production device 204, it generates a unique hash for the working condition information. Then, this hash is added to the distributed ledger of the blockchain and synchronized to all nodes. After receiving the encrypted working condition information, the scheduling device 201 determines the unique hash of the encrypted working condition information through the distributed ledger in the blockchain, decrypts and restores the encrypted working condition information, and obtains the working condition information.

[0063] 302: The scheduling device obtains the production batch number of each production batch to be assigned from at least one production batch to be assigned, and determines the training sample number corresponding to the production batch number of each production batch to be assigned according to the preset scheduling information.

[0064] In this embodiment, the production batch number for each production batch to be assigned can be determined and stored before production, so that the corresponding production batch number can be directly queried during use. Similarly, the correspondence between production batch numbers and training sample numbers can also be determined and stored before production.

[0065] Specifically, in this embodiment, the training samples can be experimental parameters derived by engineers based on historical production experience during pilot production, or experimental parameters generated by a random generation algorithm of training sample parameters based on the design parameter scorecard during the R&D phase. Therefore, the scheduling method provided in this embodiment schedules these experimental parameters to achieve pilot production, training, and learning, thereby finding the optimal experimental parameters as process parameters. Alternatively, the training samples can also be the optimal production parameters obtained after systematic training and learning. Thus, the scheduling method provided in this embodiment schedules these optimal production parameters to achieve mass production of the product.

[0066] 303: The scheduling device determines the production time for each production batch to be assigned based on the working condition information.

[0067] In this embodiment, the scheduling device 201 can determine the remaining time and operating status of the current production batch of the production device 204 based on the operating status information of the production device 204. Then, based on the remaining time and operating status of the current production batch, multiple start time determination processes are performed to obtain the production time of each production batch to be assigned.

[0068] Specifically, the remaining time for the current production batch refers to the time required for the production batch currently being produced by production device 204 to complete its remaining production tasks. This can be achieved by querying the production batch number of the currently producing batch, determining its total task duration and start time, then determining the already running time of the currently producing batch based on the start time and the current time, and finally using the difference between the total task duration and the already running time of the currently producing batch as the remaining time for the current production batch of production device 204.

[0069] Based on this, this embodiment provides a method for determining the production time of each production batch to be allocated by performing multiple start time determination processes based on the remaining time and operating status of the current production batch, as detailed below:

[0070] In the i-th start time determination process, the scheduling device 201 determines the first start time A corresponding to this process. i And the first working hours B i Determine the production time C of the i-th production batch. i Specifically, the i-th production batch can be the i-th production batch among at least one production batch to be allocated, where i is an integer greater than or equal to 1. For example, in the second start time determination process, the production time of the production batch ranked second among at least one production batch to be allocated is determined. Meanwhile, in this embodiment, when i = 1, i.e., during the first start time determination process, the first start time A... i For the current moment, the first working time is B. i This represents the remaining time for the current production batch.

[0071] Then, the scheduling device 201 can determine the working time D of the i-th production batch based on the operating status of the production device 204. i The working hours are D i Used to identify the time required for the i-th production batch from the start of production to completion; in other words, the total time required for the production batch.

[0072] Specifically, in this embodiment, the operating conditions of the production device 204 can be divided into standard operating conditions and abnormal operating conditions. Under standard operating conditions, the scheduling device 201 can determine the product type of the i-th production batch, and then determine the average working time for the production device to complete the production of the same type of batch based on the product type, thereby using the average working time as the working time D of the i-th production batch. i In actual production, the average working time of production device 204 under standard operating conditions for processing various product types can be pre-calculated as standard working hours, and these standard working hours can be stored in a benchmark database. Therefore, when needed, the scheduling device 201 can directly access this benchmark database to obtain the corresponding standard working hours.

[0073] Under abnormal operating conditions, the scheduling device 201 can determine the product type of the i-th production batch and the abnormal information of the abnormal operating conditions. Then, based on the product type, it can determine the average working time for the production unit to complete the production of batches of the same type, and based on the abnormal information, it can determine the average processing time for the production unit to handle abnormal operating conditions with the same abnormal information. Finally, the sum of the average working time and the average processing time is taken as the working time D of the i-th production batch. i In actual production, the average processing time for each abnormality handled by production device 204 under abnormal operating conditions can be pre-calculated as abnormal processing time, and this calculated abnormal processing time can be stored in an abnormality database. Therefore, when needed, the scheduling device 201 can directly access this abnormality database to obtain the corresponding abnormal processing time.

[0074] Specifically, in this embodiment, abnormal operating conditions can be categorized into equipment abnormalities and parameter abnormalities based on their causes. Equipment abnormalities are caused by damage to production equipment, such as a material pump stopping operation. Parameter abnormalities are caused by abnormal process parameters, such as abnormalities in the vapor pressure and hydrogen pressure of infrastructure, leading to insufficient vapor pressure and hydrogen pressure, and a slower production reaction time.

[0075] Therefore, in this embodiment, different methods can be used to determine the average processing time for different types of abnormal operating conditions. Specifically, the following provides a method for determining the average processing time for abnormal operating conditions where the abnormal operating condition is equipment malfunction, based on the abnormal information, such as... Figure 4 As shown, the method includes:

[0076] 401: Based on the abnormal information, the abnormal condition is divided into at least one sub-abnormality.

[0077] In this embodiment, each of the at least one sub-anomaly is used to identify the smallest indivisible anomaly. For example, a foreign object may become stuck in the pump impeller, causing impeller damage, or the pump motor may be running under high load, leading to a short circuit and burnout.

[0078] 402: Obtain historical processing data for each sub-anomaly handled by the production unit.

[0079] In this embodiment, keywords that characterize the segmented sub-anomalies can be extracted. Then, a database search is performed based on these keywords to find historical processing data of the same sub-anomaly handled by production device 204, or other production devices identical to production device 204.

[0080] 403: Perform data filtering and data completion processing on the historical processing data of each sub-anomaly handled by the production unit to obtain at least one standard historical data.

[0081] In this embodiment, there is a one-to-one correspondence between the at least one standard historical data point and the at least one sub-anomaly. Specifically, the completion method may include neighbor completion, median completion, and mean completion.

[0082] 404: Determine the average exception handling time for each sub-anomaly based on each standard historical data in at least one standard historical data set.

[0083] 405: Determine the processing order of at least one sub-anomaly based on the preset failure mode library.

[0084] In this embodiment, the failure mode library can be a database that maintains a fault tree model. Specifically, the root node of the fault tree model is the entire production equipment, and the child nodes of the root node can be the various components included in the production equipment. The child nodes of the nodes corresponding to each component can be the various types of faults that the component may generate. At the same time, each type of fault can be further derived into new nodes based on whether it can be further broken down and the processing order, until the derived child nodes can no longer be broken down or the processing is completed (i.e., the last step in handling the anomaly). Therefore, after obtaining the anomaly information, the failure mode library can be queried based on the anomaly information to find the node corresponding to the anomaly information in the fault tree model. Then, using that node as the root node, all its child nodes can be obtained to determine the processing order of the at least one sub-anomaly.

[0085] In this embodiment, the processing sequence includes at least one series processing identifier and at least one parallel processing identifier. Each of the at least one parallel processing identifier is used to identify at least one first sub-anomaly that can be processed in parallel, and the at least one sub-anomaly includes at least one first sub-anomaly. Specifically, the processing sequence is used to identify the correlation between various sub-anomalies in the current abnormal operating condition. In other words, a sub-anomaly that is later in the sequence must wait for the previous sub-anomaly to be repaired before it can be repaired. For example, if a material pump fails to work, preventing the equipment from receiving materials, and analysis reveals two related sub-anomalies: one is that the pump impeller is damaged due to foreign objects being stuck, and the other is that the pump motor is running under high load, causing a short circuit and burning out. In the repair process, the motor must be repaired first before the impeller can be replaced or repaired. Therefore, in the processing sequence, "the pump motor running under high load causing a short circuit and burning out" is prioritized before "the pump impeller is damaged due to foreign objects being stuck."

[0086] Meanwhile, in this embodiment, the sub-anomaly marked by the serial processing identifier is a sub-anomaly that cannot be repaired simultaneously with other sub-anomalies. In other words, repairing this sub-anomaly will affect the repair of other sub-anomalies, thus preventing them from being repaired simultaneously. Examples include "the pump motor burning out due to high load operation" and "the pump impeller being damaged due to foreign objects stuck in it." On the other hand, multiple sub-anomalies marked by the parallel processing identifier are sub-anomalies that can be repaired synchronously. For example, the failure of the reactor and the material pump are two independent sub-anomalies that are not spatially related and do not affect each other, and can be repaired simultaneously.

[0087] 406: Based on the average exception handling time of each sub-exception, determine the series processing time of each series processing identifier in at least one series processing identifier, and the parallel processing time of each parallel processing identifier.

[0088] In this embodiment, the serial processing duration of the serial processing identifier is the exception processing duration of the sub-exception marked by the serial processing identifier. The parallel processing duration of the parallel processing identifier is the longest exception processing duration among the exception processing durations of at least one sub-exception marked by the parallel processing identifier.

[0089] Furthermore, in this embodiment, there may be sub-anomalies that are simultaneously marked by both serial processing and parallel processing identifiers, which can be referred to as hybrid processing identifiers. In this case, the sub-anomalies marked by the serial processing identifier can be merged into a total anomaly, and then compared with the sub-anomalies marked by the parallel processing identifier. The longest anomaly processing time among the total anomaly and the anomalies marked by the parallel processing identifiers is taken as the anomaly processing time corresponding to the hybrid processing identifier.

[0090] Based on this Figure 5A schematic diagram illustrating how to determine the processing order of at least one sub-anomaly is shown, such as... Figure 5 As shown, following the production stoppage caused by the material pump stopping and the reactor being damaged, disassembly reveals the following:

[0091] Sub-abnormality 1: Foreign objects are stuck in the pump impeller, causing impeller damage;

[0092] Sub-abnormality 2: The pump motor was short-circuited and burned out due to high load operation;

[0093] Sub-anomaly 3: Corrosion damage to the reactor packing box;

[0094] Sub-abnormality 4: Reactor seal leakage.

[0095] Sub-abnormality 1 and sub-abnormality 2 both belong to the abnormalities of the material pump, and there is a certain spatial correlation between them, so they cannot be handled synchronously. In short, the motor must be repaired before the impeller can be replaced or repaired. Therefore, sub-abnormality 1 and sub-abnormality 2 will be marked with a series processing identifier, and sub-abnormality 2 will be arranged before sub-abnormality 1, resulting in a series processing order [sub-abnormality 2, sub-abnormality 1].

[0096] Sub-anomalies 3 and 4 are abnormalities of the reactor. Similarly, there is a causal relationship between them; that is, the occurrence of sub-anomaly 4 may be caused by sub-anomaly 3. Therefore, sub-anomaly 3 needs to be addressed first to determine the true cause of sub-anomaly 4, and then sub-anomaly 4 can be addressed accordingly. Therefore, sub-anomalies 3 and 4 will be marked with a series-related processing identifier, and sub-anomaly 3 will be placed before sub-anomaly 4, resulting in a series-processing order [sub-anomaly 3, sub-anomaly 4].

[0097] Meanwhile, the reactor and the material pumps are not interconnected; maintenance on one will not affect the other, nor will it affect the other. Therefore, the total anomaly 1 formed by sub-anomalies 1 and 2 is processed synchronously with the total anomaly 2 formed by sub-anomalies 3 and 4. Consequently, sub-anomalies 1, 2, 3, and 4 will be marked with interconnected parallel processing identifiers. Combined with the previously determined series processing sequence, the final processing sequence is obtained:

[0098]

[0099] 407: The sum of the serial processing time of each serial processing identifier and the parallel processing time of each parallel processing identifier is used as the average processing time.

[0100] In an optional implementation, anomalies can be categorized into three severity levels: A (high), B (medium), and C (low). Each level of fault is further classified into different anomaly categories, and the corresponding average anomaly handling time is calculated. For example, category A anomalies can be further categorized by the type of equipment involved: pumps in material pipelines, reactors, evaporators, vacuum pumps, etc. Databases of anomaly handling times for different types are established, and the handling times for each anomaly type are sorted. Specifically, the relationship of anomaly handling times is A>B>C, meaning the more severe the fault, the longer the handling time.

[0101] In the optional real-time mode, the exception handling time for each anomaly can be combined and superimposed with the standard working hours for each product type to obtain the exception working hours for handling production batches of each product type under each anomaly condition. These exception working hours are then directly stored in the anomaly condition database for easy retrieval based on exception type and product type when needed, further improving scheduling efficiency.

[0102] In this embodiment, when the abnormal operating condition is a parameter abnormality, the abnormal parameter can be determined through the abnormal information. Then, the production process affected by the determined abnormal parameter is identified through the abnormal database, along with the abnormal production time required for that process under the influence of the parameter. The standard production time of that process is then replaced with the abnormal production time, and the working time D of the i-th production batch under this abnormal operating condition is calculated. i .

[0103] Specifically, the abnormal parameters of steam pressure and hydrogen pressure in the aforementioned infrastructure lead to insufficient steam pressure and hydrogen pressure, resulting in a slower production reaction time. Since the abnormality of these two parameters only affects the production reaction stage, we assume that all stages of this production process are: feeding, production reaction, discharging, and packaging, with corresponding standard production times of T1, T2, T3, and T4, respectively. Then, under standard operating conditions, the working time D for the i-th production batch is... i =T1+T2+T3+T4, Now, due to abnormal parameters of vapor pressure and hydrogen pressure, the production reaction time has slowed down. After querying the abnormal database, it was found that the abnormal production time of the production reaction process under these abnormal conditions is T5. Therefore, the original standard production time T2 of the production reaction process can be replaced with this abnormal production time T5. Calculate the working time D of the i-th production batch under abnormal conditions. i =T1+T5+T3+T4.

[0104] Finally, the scheduling device 201 will process the production time C obtained in this process. i As the first start time A in the (i+1)th start time determination process i+1And the working time D obtained in this process i The first working duration B is determined at the (i+1)th start time. i+1 The (i+1)th start time determination process is performed, and this process is repeated multiple times until the production time of each production batch to be allocated is obtained.

[0105] 304: The scheduling device generates a production work order based on the production time of each production batch to be assigned and the training sample number corresponding to the production batch number of each production batch to be assigned.

[0106] Specifically, in this embodiment, each production batch to be assigned can be sorted according to the order of its production time, and the training sample number corresponding to each production batch to be assigned can be associated to obtain the production work order.

[0107] Specifically, after determining the production time of each production batch to be assigned, a time queue is generated, which can reflect the start time of production for each production batch; the time queue is then placed into a continuous work order interval, which is shift information, such as morning, noon and evening (0-8 hours, 8-16 hours, 16-24 hours).

[0108] 305: The scheduling device sends the production work order to the user device.

[0109] In this embodiment, the scheduling device 201 can determine the user device 203 corresponding to the production work order by establishing the association between the user device and different production devices. Then, the scheduling device 201 communicates with the production scheduling device 202 to obtain the current production time and batch number of the user device 203, thereby predicting and determining the current work order time period for the user device 203, such as any shift in the morning, afternoon, or evening shifts. The corresponding production work order is then sent to the user device 203.

[0110] In this embodiment, communication between the scheduling device 201 and the user device 203 can also be achieved through blockchain technology. Specifically, this communication method is similar to the method of transmitting working condition information between the scheduling device 201 and the production scheduling device 202 in step 301, and will not be described in detail here.

[0111] In this embodiment, after receiving a production work order from the scheduling device 201, the user device 203 can display the production work order to the production personnel through its own display device. Thus, through the production personnel's operation of the user device 203, the target production batch for which the production personnel want to view the process parameters can be determined. Subsequently, based on the production batch number of the target production batch, a viewing request is generated and returned to the scheduling device 201.

[0112] Based on this, in this embodiment, after receiving the viewing request returned by the user device 203, the scheduling device 201 first confirms whether the target production batch in the viewing request exists in at least one production batch to be assigned. When the target production batch exists in at least one production batch to be assigned, that is, when the target production batch is any one of at least one production batch to be assigned, the scheduling device 201 obtains the production time of the target production batch and compares it with the current time. When the production time is less than or equal to the current time, it indicates that the production time of the target production batch has been reached or exceeded. Then, the scheduling device 201 obtains the production batch number of the target production batch, and then determines the target training sample number corresponding to the target production batch based on the production batch number. Finally, based on the target training sample number corresponding to the target production batch, the process parameters corresponding to the target production batch are determined, and the process parameters are sent to the user device.

[0113] Specifically, after receiving a viewing request, the scheduling device 201 needs to determine whether the target production batch requested in the request is a production batch included in the work order time period corresponding to the current time. Simultaneously, by determining the start time of the target production batch, it determines whether the current time has reached the start time of the target production batch. Then, after both of the above conditions are met, it queries the production process associated with the work order and sends the process parameters of the production batch corresponding to that production process to the corresponding user equipment. For example, if the current time is 8:00 AM, the corresponding work order period is the early shift. Then, it determines the production batches included in the work orders corresponding to the early shift, such as production batch 1. At this point, it determines the production process that the user needs to manage in each work order within the early shift, such as production process 1. Since the production start time of production batch 1 is 8:00 AM, the process parameters of production process 1 for batch 1 can be sent to the user equipment, which can then produce according to the process parameters. That is, this solution allows production personnel to view the actual production parameters corresponding to their work orders only during their working hours, thus improving confidentiality.

[0114] In summary, the scheduling method provided by this invention firstly involves the scheduling device acquiring the operating status information of the production equipment and at least one production batch to be allocated to the production equipment through the production scheduling device. It then acquires the production batch number of each of the at least one production batch to be allocated, and determines the training sample number corresponding to the production batch number of each production batch to be allocated based on preset production scheduling information. Next, the scheduling device determines the production time of each production batch to be allocated based on the operating status information. Then, based on the production time of each production batch to be allocated and the training sample number corresponding to the production batch number of each production batch to be allocated, a production work order is generated. Finally, the scheduling device sends the production work order to the user device. Thus, the scheduling device can automatically predict the time based on the current operating status to calculate the production batch time corresponding to subsequent batches of work orders, achieving real-time adjustment of the work orders. Simultaneously, through the correspondence between the production batch number and the training sample number, production personnel can query the corresponding training sample number based on the production batch number when producing according to the work order, thereby obtaining the correct process parameters in a timely manner, ensuring production accuracy and efficiency.

[0115] In addition, see Figure 6 , Figure 6 This is a flowchart illustrating a process parameter request method provided in an embodiment of this application. This process parameter request method is applied to... Figure 2 The order scheduling system shown includes the following steps:

[0116] 601: The user device receives the production work order sent by the scheduling device.

[0117] 602: The user equipment determines the target production batch based on the production work order and the current time.

[0118] In this embodiment, the user device can compare the current time with the start time of each production batch in the production work order to determine the production batch corresponding to the current time as the target production batch. For example, assume the production work order includes production batch 1, production batch 2, and production batch 3, where production batch 1 starts at 8:00 AM, production batch 2 starts at 9:30 AM, and production batch 3 starts at 11:00 AM. If the current time is 8:30 AM, then the current time falls between the start time of production batch 1 and the start time of production batch 2, belonging to the production time period corresponding to production batch 1; therefore, production batch 1 is selected as the target production batch.

[0119] In an optional implementation, the target production batch can also be selected and determined by production personnel. For example, after receiving a production work order, the user device 203 can display the production work order to the production personnel through its own display device, thereby accepting the operation instructions of the production personnel and determining the target production batch according to their operation instructions.

[0120] 603: The user device generates a viewing request based on the target production batch and sends the viewing request to the scheduling device.

[0121] In an optional implementation, the view request is used to request to view the process parameters of the target production batch.

[0122] 604: The user device receives the process parameters corresponding to the target production batch returned by the scheduling device and displays the process parameters corresponding to the target production batch to the user.

[0123] See Figure 7 , Figure 7 This is a block diagram illustrating the functional modules of a scheduling device provided for an embodiment of this application. For example... Figure 7 As shown, the order placement device 700 includes:

[0124] The acquisition module 701 is used to acquire the operating status information of the production device and at least one production batch to be allocated to the production device through the production scheduling device, and to acquire the production batch number of each production batch to be allocated in the at least one production batch to be allocated, and to determine the training sample number corresponding to the production batch number of each production batch to be allocated according to the preset production scheduling information.

[0125] The scheduling module 702 is used to determine the production time of each production batch to be assigned based on the working condition information, and to generate a production work order based on the production time of each production batch to be assigned and the training sample number corresponding to the production batch number of each production batch to be assigned.

[0126] The dispatch module 703 is used to send production work orders to user devices.

[0127] In an embodiment of the present invention, the scheduling module 702, in determining the production time of each production batch to be allocated based on working condition information, is specifically used for:

[0128] Based on the operating information, determine the remaining production time and operating status of the current production batch of the production unit;

[0129] Based on the remaining time and operating status of the current production batch, the start time determination process is performed multiple times to obtain the production time of each production batch to be allocated.

[0130] In an embodiment of the present invention, in order to obtain the production time of each production batch to be allocated by performing multiple start time determination processes based on the remaining time and working status of the current production batch, the scheduling module 702 is specifically used for:

[0131] In the process of determining the i-th start time, based on the first start time A i And the first working hours B iDetermine the start time C of the i-th production batch. i Wherein, the i-th production batch is the production batch ranked i-th among at least one production batch to be allocated, i is an integer greater than or equal to 1, and when i = 1, the first starting time A i For the current moment, the first working time is B. i This represents the remaining time for the current production batch.

[0132] The working time D of the i-th production batch is determined based on the working conditions. i Among them, working hours D i Used to identify the time required for the i-th production batch to be completed from the start of production;

[0133] Start time C i As the first start time A in the (i+1)th start time determination process i+1 and working hours D i The first working duration B is determined at the (i+1)th start time. i+1 The (i+1)th start time determination process is performed, and this process is repeated multiple times until the production time of each production batch to be allocated is obtained.

[0134] In an embodiment of the present invention, when the operating condition is a standard operating condition, the working time D of the i-th production batch is determined based on the operating condition. i Regarding the scheduling module 702, it is specifically used for:

[0135] Determine the product type for the i-th production batch;

[0136] Determine the average working time for the production unit to complete the same type of batch production based on the product type;

[0137] The average working time is taken as the working time D of the i-th production batch. i .

[0138] In an embodiment of the present invention, when the operating condition is abnormal, the working time D of the i-th production batch is determined based on the operating condition. i Regarding the scheduling module 702, it is specifically used for:

[0139] Determine the product type of the i-th production batch and the abnormal information of the abnormal operating conditions;

[0140] The average working time for the production unit to complete the same type of batch production is determined based on the product type, and the average processing time for the production unit to handle the same abnormal conditions is determined based on the abnormal information.

[0141] The sum of the average working time and the average processing time is taken as the working time D of the i-th production batch. i .

[0142] In an embodiment of the present invention, the scheduling module 702 is specifically used for determining the average processing time for abnormal operating conditions of the production unit handling the same abnormal information based on the abnormal information.

[0143] Based on the abnormal information, the abnormal operating condition is divided into at least one sub-abnormality, wherein each sub-abnormality in the at least one sub-abnormality is used to identify the smallest indivisible abnormal condition;

[0144] Acquire historical processing data for each sub-anomaly handled by the production unit;

[0145] The historical processing data of each sub-anomaly in the production unit is filtered and completed to obtain at least one standard historical data, and at least one standard historical data corresponds one-to-one with at least one sub-anomaly.

[0146] Based on each standard historical data in at least one standard historical data set, determine the average anomaly handling time for each corresponding sub-anomaly.

[0147] According to a preset failure mode library, the processing order of at least one sub-abnormality is determined, wherein the processing order includes at least one serial processing identifier and at least one parallel processing identifier, each of the at least one parallel processing identifier is used to identify at least one first sub-abnormality that can be processed synchronously, and at least one sub-abnormality includes at least one first sub-abnormality.

[0148] Based on the average exception handling time of each sub-exception, determine the serial processing time of each serial processing identifier in at least one serial processing identifier, and the parallel processing time of each parallel processing identifier.

[0149] The average processing time is the sum of the serial processing time of each serial processing identifier and the parallel processing time of each parallel processing identifier.

[0150] In an embodiment of the present invention, after the production work order is sent to the user device, the scheduling module 702 is further configured to:

[0151] Receive a viewing request returned by the user device, wherein the viewing request is used to request to view the process parameters of the target production batch;

[0152] When the target production batch is any one of at least one production batch to be allocated, obtain the production time of the target production batch;

[0153] If the production time is less than or equal to the current time, obtain the production batch number of the target production batch;

[0154] Determine the target training sample number corresponding to the target production batch based on the production batch number of the target production batch;

[0155] Based on the target training sample number corresponding to the target production batch, determine the process parameters corresponding to the target production batch, and send the process parameters to the user equipment.

[0156] See Figure 8 , Figure 8 This is a functional module block diagram of a user device provided for an embodiment of this application. For example... Figure 8 As shown, the user device 800 includes:

[0157] Receiver module 801 is used to receive production work orders sent by the scheduling device;

[0158] The request module 802 is used to determine the target production batch based on the production work order and the current time, generate a viewing request based on the target production batch, and send the viewing request to the scheduling device. The viewing request is used to request to view the process parameters of the target production batch.

[0159] The display module 803 is used to receive the process parameters corresponding to the target production batch returned by the scheduling device and display the process parameters corresponding to the target production batch to the user.

[0160] See Figure 9 , Figure 9 This is a schematic diagram of a scheduling device provided for an embodiment of this application. (See attached diagram.) Figure 9 As shown, the scheduling device 900 includes a transceiver 901, a processor 902, and a memory 903. These are connected via a bus 904. The memory 903 stores computer programs and data, and can transfer data stored in the memory 903 to the processor 902.

[0161] Processor 902 is used to read the computer program in memory 903 and perform the following operations:

[0162] The production scheduling device obtains the operating status information of the production unit and at least one production batch to be allocated to the production unit.

[0163] Obtain the production batch number of each production batch to be allocated in at least one production batch to be allocated, and determine the training sample number corresponding to the production batch number of each production batch to be allocated according to the preset production scheduling information.

[0164] Based on the operating conditions, determine the production time for each production batch to be allocated;

[0165] A production work order is generated based on the production time of each production batch to be assigned and the training sample number corresponding to the production batch number of each production batch to be assigned.

[0166] Send the production work order to the user device.

[0167] In an embodiment of the present invention, in determining the production time of each production batch to be allocated based on working condition information, the processor 902 is specifically configured to perform the following operations:

[0168] Based on the operating information, determine the remaining production time and operating status of the current production batch of the production unit;

[0169] Based on the remaining time and operating status of the current production batch, the start time determination process is performed multiple times to obtain the production time of each production batch to be allocated.

[0170] In an embodiment of the present invention, in order to obtain the production time of each production batch to be allocated by performing multiple start time determination processes based on the remaining time of the current production batch and the working status, the processor 902 is specifically used to perform the following operations:

[0171] In the process of determining the i-th start time, based on the first start time A i And the first working hours B i Determine the start time C of the i-th production batch. i Wherein, the i-th production batch is the production batch ranked i-th among at least one production batch to be allocated, i is an integer greater than or equal to 1, and when i = 1, the first starting time A i For the current moment, the first working time is B. i This represents the remaining time for the current production batch.

[0172] The working time D of the i-th production batch is determined based on the working conditions. i Among them, working hours D i Used to identify the time required for the i-th production batch to be completed from the start of production;

[0173] Start time C i As the first start time A in the (i+1)th start time determination process i+1 and working hours D i The first working duration B is determined at the (i+1)th start time. i+1 The (i+1)th start time determination process is performed, and this process is repeated multiple times until the production time of each production batch to be allocated is obtained.

[0174] In an embodiment of the present invention, when the operating condition is a standard operating condition, the working time D of the i-th production batch is determined based on the operating condition. i In this regard, processor 902 is specifically used to perform the following operations:

[0175] Determine the product type for the i-th production batch;

[0176] Determine the average working time for the production unit to complete the same type of batch production based on the product type;

[0177] The average working time is taken as the working time D of the i-th production batch. i .

[0178] In an embodiment of the present invention, when the operating condition is abnormal, the working time D of the i-th production batch is determined based on the operating condition. i In this regard, processor 902 is specifically used to perform the following operations:

[0179] Determine the product type of the i-th production batch and the abnormal information of the abnormal operating conditions;

[0180] The average working time for the production unit to complete the same type of batch production is determined based on the product type, and the average processing time for the production unit to handle the same abnormal conditions is determined based on the abnormal information.

[0181] The sum of the average working time and the average processing time is taken as the working time D of the i-th production batch. i .

[0182] In an embodiment of the present invention, the processor 902 is specifically configured to perform the following operations in determining the average processing time for abnormal operating conditions in which the production unit processes the same abnormal information based on the abnormal information:

[0183] Based on the abnormal information, the abnormal operating condition is divided into at least one sub-abnormality, wherein each sub-abnormality in the at least one sub-abnormality is used to identify the smallest indivisible abnormal condition;

[0184] Acquire historical processing data for each sub-anomaly handled by the production unit;

[0185] The historical processing data of each sub-anomaly in the production unit is filtered and completed to obtain at least one standard historical data, and at least one standard historical data corresponds one-to-one with at least one sub-anomaly.

[0186] Based on each standard historical data in at least one standard historical data set, determine the average anomaly handling time for each corresponding sub-anomaly.

[0187] According to a preset failure mode library, the processing order of at least one sub-abnormality is determined, wherein the processing order includes at least one serial processing identifier and at least one parallel processing identifier, each of the at least one parallel processing identifier is used to identify at least one first sub-abnormality that can be processed synchronously, and at least one sub-abnormality includes at least one first sub-abnormality.

[0188] Based on the average exception handling time of each sub-exception, determine the serial processing time of each serial processing identifier in at least one serial processing identifier, and the parallel processing time of each parallel processing identifier.

[0189] The average processing time is the sum of the serial processing time of each serial processing identifier and the parallel processing time of each parallel processing identifier.

[0190] In an embodiment of the present invention, after the production work order is sent to the user device, the processor 902 is specifically configured to perform the following operations:

[0191] Receive a viewing request returned by the user device, wherein the viewing request is used to request to view the process parameters of the target production batch;

[0192] When the target production batch is any one of at least one production batch to be allocated, obtain the production time of the target production batch;

[0193] If the production time is less than or equal to the current time, obtain the production batch number of the target production batch;

[0194] Determine the target training sample number corresponding to the target production batch based on the production batch number of the target production batch;

[0195] Based on the target training sample number corresponding to the target production batch, determine the process parameters corresponding to the target production batch, and send the process parameters to the user equipment.

[0196] See Figure 10 , Figure 10 This is a schematic diagram of the structure of a user equipment provided for an embodiment of this application. For example... Figure 10 As shown, user equipment 1000 includes a transceiver 1001, a processor 1002, and a memory 1003. They are connected to each other via a bus 1004. The memory 1003 is used to store computer programs and data, and can transfer data stored in the memory 1003 to the processor 1002.

[0197] Processor 1002 is used to read computer programs from memory 1003 and perform the following operations:

[0198] Receive production work orders sent by the scheduling device;

[0199] Determine the target production batch based on the production work order and the current time.

[0200] Based on the target production batch, a viewing request is generated and sent to the scheduling device. The viewing request is used to request to view the process parameters of the target production batch.

[0201] Receive the process parameters corresponding to the target production batch returned by the scheduling device, and display the process parameters corresponding to the target production batch to the user.

[0202] It should be understood that the user device or scheduling device in this application may include smartphones (such as Android phones, iOS phones, Windows Phones, etc.), tablets, PDAs, laptops, mobile internet devices (MIDs), robots, or wearable devices, etc. The above-mentioned user devices or scheduling devices are merely examples and not exhaustive, and include, but are not limited to, user devices or scheduling devices. In practical applications, the above-mentioned user devices or scheduling devices may also include: intelligent vehicle terminals, computer equipment, etc.

[0203] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software combined with a hardware platform. Based on this understanding, all or part of the technical solution of the present invention that contributes to the background art can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0204] Therefore, embodiments of this application also provide a computer-readable storage medium storing a computer program that is executed by a processor to implement some or all of the steps of any of the scheduling methods or process parameter request methods described in the above method embodiments. For example, the storage medium may include a hard disk, floppy disk, optical disk, magnetic tape, magnetic disk, USB flash drive, flash memory, etc.

[0205] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the scheduling methods or process parameter request methods described in the above method embodiments.

[0206] It should be noted that, for the sake of simplicity, the aforementioned methods are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are optional, and the actions and modules involved are not necessarily essential to this application.

[0207] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0208] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0209] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0210] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0211] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0212] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0213] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The above description of the embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A scheduling method, characterized in that, The method includes: The scheduling device obtains the operating status information of the production device and at least one production batch to be allocated to the production device through the production scheduling device; The scheduling device obtains the production batch number of each production batch to be assigned in the at least one production batch to be assigned, and determines the training sample number corresponding to the production batch number of each production batch to be assigned according to the preset scheduling information. The scheduling device determines the production time for each production batch to be allocated based on the working condition information. The scheduling device generates a production work order based on the production time of each production batch to be assigned and the training sample number corresponding to the production batch number of each production batch to be assigned. The scheduling device sends the production work order to the user device. The scheduling device determines the production time of each production batch to be allocated based on the working condition information, including: The scheduling device determines the remaining time and operating status of the current production batch of the production device based on the operating condition information. In the i-th start time determination process, the scheduling device determines the order based on the first start time A. i And the first working hours B i Determine the production time C of the i-th production batch. i Wherein, the i-th production batch is the production batch ranked i-th among the at least one production batch to be allocated, i is an integer greater than or equal to 1, and when i = 1, the first start time A i At the current moment, the first working duration B i The remaining time for the current production batch; The scheduling device determines the working time D of the i-th production batch based on the working condition. i Wherein, the working time D i Used to identify the time required from the start of production to the completion of production for the i-th production batch; The scheduling device will schedule the production time C i As the first start time A in the (i+1)th start time determination process i+1 And the working time D i The first working duration B of the process for determining the (i+1)th start time i+1 The (i+1)th start time determination process is performed, and after multiple start time determination processes are performed, the production time of each production batch to be allocated is obtained.

2. The method according to claim 1, characterized in that, When the operating condition is abnormal, the scheduling device determines the working time D of the i-th production batch based on the operating condition. i ,include: The scheduling device determines the product type of the i-th production batch and the abnormal information of the abnormal working condition; The scheduling device determines the average working time for the production device to complete the production of the same type of batch based on the product type, and determines the average processing time for the production device to handle abnormal conditions with the same abnormal information based on the abnormal information. The scheduling device uses the sum of the average working time and the average processing time as the working time D of the i-th production batch. i .

3. The method according to claim 2, characterized in that, The step of determining the average processing time for abnormal operating conditions of the production unit to handle the same abnormal information based on the abnormal information includes: The abnormal operating condition is divided into at least one sub-abnormality based on the abnormal information, wherein each sub-abnormality in the at least one sub-abnormality is used to identify the smallest indivisible abnormal situation; Obtain historical processing data of each sub-abnormality handled by the production unit; The historical processing data of each sub-abnormality in the production device is subjected to data filtering and data completion processing to obtain at least one standard historical data, and the at least one standard historical data corresponds one-to-one with the at least one sub-abnormality. Based on each standard historical data in the at least one standard historical data, determine the average anomaly handling time for each corresponding sub-anomaly; According to a preset failure mode library, the processing order of at least one sub-abnormality is determined, wherein the processing order includes at least one serial processing identifier and at least one parallel processing identifier, each of the at least one parallel processing identifier is used to identify at least one first sub-abnormality that can be processed synchronously, and the at least one sub-abnormality includes the at least one first sub-abnormality. Based on the average exception handling time of each sub-exception, the series processing time of each series processing identifier and the parallel processing time of each parallel processing identifier in the at least one series processing identifier are determined respectively. The average processing time is the sum of the serial processing time of each serial processing identifier and the parallel processing time of each parallel processing identifier.

4. A scheduling device, characterized in that, The order scheduling device includes: The acquisition module is used to acquire the operating status information of the production device and at least one production batch to be allocated to the production device through the production scheduling device, and to acquire the production batch number of each production batch to be allocated in the at least one production batch to be allocated, and to determine the training sample number corresponding to the production batch number of each production batch to be allocated according to the preset production scheduling information. The scheduling module is used to determine the production time of each production batch to be assigned based on the working condition information, and to generate a production work order based on the production time of each production batch to be assigned and the training sample number corresponding to the production batch number of each production batch to be assigned. The distribution module is used to send the production work order to the user device; Specifically, in determining the production time of each production batch to be allocated based on the working condition information, the scheduling module is used for: Based on the operating condition information, determine the remaining production time and operating status of the current production batch of the production unit; In the process of determining the i-th start time, based on the first start time A i And the first working hours B i Determine the production time C of the i-th production batch. i Wherein, the i-th production batch is the production batch ranked i-th among the at least one production batch to be allocated, i is an integer greater than or equal to 1, and when i = 1, the first start time A i At the current moment, the first working duration B i The remaining time for the current production batch; The working time D of the i-th production batch is determined based on the aforementioned working conditions. i Wherein, the working time D i Used to identify the time required from the start of production to the completion of production for the i-th production batch; The production time C i As the first start time A in the (i+1)th start time determination process i+1 And the working time D i The first working duration B of the process for determining the (i+1)th start time i+1 The (i+1)th start time determination process is performed, and after multiple start time determination processes are performed, the production time of each production batch to be allocated is obtained.

5. A device, characterized in that, It includes a processor, a memory, and a communication interface, wherein one or more programs are stored in the memory and configured to be executed by the processor, the one or more programs including instructions for performing the steps of the method of any one of claims 1-3.

6. A computer-readable storage medium or computer program product, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is executed by a processor to implement the method as claimed in any one of claims 1-4; or, the computer program product is executed by a processor to implement the method as claimed in any one of claims 1-3.

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