Data processing methods, apparatus and readable storage media

Abstract

This application discloses a data processing method, apparatus, and readable storage medium, relating to the field of information technology. The data processing method includes: determining, when considering data to be processed, the data parameters, data source information, input-output relationships, data rules, and data processing flow corresponding to the data to be processed; determining the workflow sequence corresponding to the data to be processed based on the data parameters, data source information, input-output relationships, data rules, and data processing flow; inputting the data to be processed into the workflow sequence to obtain the output data of the workflow sequence; and determining the processing result of the data to be processed based on the output data. This application improves the processing efficiency of the data to be processed.

Description

Technical Field

[0001] This application relates to the field of information technology, and in particular to a data processing method, apparatus and readable storage medium. Background Technology

[0002] Currently, traditional data processing methods mostly employ static rule engines or single models, lacking flexibility and scalability. When business requirements change, recoding and deployment are often necessary, resulting in long development cycles and low processing efficiency. Therefore, existing data processing methods suffer from technical problems such as low data processing efficiency. Summary of the Invention

[0003] This application provides a data processing method, apparatus, and readable storage medium to solve technical problems such as low processing efficiency in the prior art.

[0004] A first aspect of this application provides a data processing method, the method comprising: When dealing with data to be processed, determine the data parameters, data source information, input-output relationships, data rules, and data processing flow corresponding to the data to be processed. Based on data parameters, data source information, input-output relationships, data rules, and data processing flow, determine the workflow sequence corresponding to the data to be processed; Input the data to be processed into the workflow sequence to obtain the output data of the workflow sequence; Based on the output data, determine the processing result of the data to be processed.

[0005] The data processing method in this embodiment determines the workflow sequence corresponding to the data to be processed by customizing and configuring data parameters, data source information, input-output relationships, data rules, and data processing flow. This improves the customizability of the workflow sequence and thus improves the development efficiency of the workflow sequence. Furthermore, the method processes the data to be processed through an efficient workflow program to determine the processing result, thereby improving the efficiency of determining the processing result and ultimately improving the processing efficiency of the data to be processed.

[0006] A second aspect of this application provides a data processing apparatus, the apparatus comprising: The first processing unit is used to determine the data parameters, data source information, input-output relationships, data rules and data processing flow corresponding to the data to be processed, when the data to be processed is to be processed. The second processing unit is used to determine the workflow sequence corresponding to the data to be processed based on data parameters, data source information, input-output relationships, data rules, and data processing flow. The third processing unit is used to input the data to be processed into the workflow sequence to obtain the output data of the workflow sequence; The fourth processing unit is used to determine the processing result of the data to be processed based on the output data.

[0007] A third aspect of this application provides another data processing apparatus, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the steps of the data processing method as described in any of the above embodiments. Therefore, this data processing apparatus possesses all the beneficial effects of the data processing method in any of the above embodiments, and will not be elaborated further here.

[0008] A fourth aspect of this application provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the data processing method as described in any of the above embodiments. Therefore, this readable storage medium possesses all the beneficial effects of the data processing method in any of the above embodiments, which will not be elaborated further here. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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 based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating the data processing method provided in the embodiments of this application; Figure 2 Functional block diagram of the data processing apparatus provided in the embodiments of this application; Figure 3 This is a structural block diagram of the data processing apparatus provided in the embodiments of this application. Detailed Implementation

[0011] To better understand the technical solutions provided in the embodiments of this specification, the technical solutions of the embodiments of this specification will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this specification and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this specification, rather than limitations on the technical solutions of this specification. In the absence of conflict, the embodiments of this specification and the technical features in the embodiments can be combined with each other.

[0012] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The term "two or more" includes two or more cases.

[0013] In some embodiments, Figure 1 A flowchart of the data processing method provided in the embodiments of this application is shown below. Figure 1 As shown, an embodiment of this application provides a data processing method, including: Step S101: In the case of data to be processed, determine the data parameters, data source information, input-output relationship, data rules and data processing flow corresponding to the data to be processed; In this embodiment, data to be processed is acquired, wherein the data to be processed is a dataset that needs to be processed.

[0014] For example, the data to be processed can specifically be the quality index data of steel. The quality index of steel mainly includes core aspects such as chemical composition, mechanical properties, process performance and surface quality. These indicators together determine the performance and application scope of steel.

[0015] For example, the data to be processed can specifically be steel inventory data, which can be steel inventory in various regions or steel inventory in various warehouses.

[0016] When data needs to be processed, determine the data parameters, data source information, input-output relationships, data rules, and data processing flow corresponding to the data to be processed.

[0017] Among them, the data parameters are the specific parameters of the objects involved in the data to be processed.

[0018] For example, when the data to be processed is the quality index data of steel, the data parameters may include specific parameters such as the type of steel and the type of test. The specific data parameters are shown in Table 1. In addition, the value set configuration of Table 1 is shown in Table 2.

[0019] Table 1

[0020] Table 2

[0021] The data source information is the specific information of the data source head to be processed.

[0022] For example, the data source information can specifically be information from a steel sample database. Specific data source information is shown in Table 3, and data source parameter configuration is shown in Table 4.

[0023] Table 3

[0024] Table 4

[0025] Input-output relationships represent the contextual relationships of the data to be processed.

[0026] Input-output relationships can be specifically defined as the workflow context. The context is the runtime environment used to store and transmit information during workflow execution. It carries the state of process instances, shared data between tasks, and system- or user-defined variables, ensuring that each processing node can collaborate and make decisions based on a unified context. Specific input-output relationships are shown in Table 5.

[0027] Table 5

[0028] Data rules are the corresponding rules for the data in the data to be processed.

[0029] For example, the data to be processed includes input parameters and output parameters, and the data rules can be specifically linear rules between the input parameters and the output parameters.

[0030] For example, the data to be processed includes input parameters and output parameters, and the data rules can be specifically the data operation rules between the input parameters and the output parameters.

[0031] For example, the specific data rules are shown in Tables 6 and 7.

[0032] Table 6

[0033] Table 7

[0034] The data processing flow is the data reasoning flow of the data to be processed. The data reasoning flow refers to the process of deriving conclusions or decisions from data, combining statistical methods and logical rules, through a series of systematic steps. It integrates data processing, pattern recognition, and inferential analysis.

[0035] For example, the specific data processing flow is shown in Table 8.

[0036] Table 8

[0037] Step S102: Determine the workflow sequence corresponding to the data to be processed based on data parameters, data source information, input-output relationship, data rules and data processing flow. By configuring workflows based on data parameters, data source information, input-output relationships, data rules, and data processing procedures, the workflow sequence corresponding to the data to be processed can be determined. The workflow sequence is the application that enables workflow functionality.

[0038] For example, by customizing data parameters, data source information, input-output relationships, data rules, and data processing flows, the workflow sequence can be avoided by recoding and re-deploying, thus shortening the development cycle and reducing the maintenance cost of the workflow sequence.

[0039] For example, parameters for a workflow sequence must be defined before use. Parameter definitions include parameter ID, parameter name, data type, value set, unit, and validity. Parameter IDs must be unique. Data types include, but are not limited to, numbers, text, booleans, and dates. If a parameter has constraints, its value set can be defined. Before use, a parameter's validity must be defined as true; invalid parameters are not allowed.

[0040] Parameter configuration includes adding parameters, modifying parameters, deleting parameters, data type configuration, value set configuration, validity configuration, etc.

[0041] For example, the data source configuration for a workflow sequence includes two parts: data source definition and data source parameter definition. The parameter values ​​corresponding to the data source can be obtained through the data source configuration.

[0042] The data source definition includes data source ID, data source name, and validity. The data source ID must be unique, and the validity must be valid and usable.

[0043] The data source parameter definition mainly defines the parameter ID and parameter name corresponding to the data source ID. The parameter ID and parameter name come from the parameter configuration, and the parameter value comes from data received by the system from other systems through the interface.

[0044] For example, the input-output relationship configuration of the workflow sequence mainly solves the application problem of the same parameter ID in different scenarios, and the parameter source can be uniquely determined through context.

[0045] The context configuration includes: context ID, context name, context definition, validity, etc. The context ID must be unique. The context definition is generally a script SQL (Structured Query Language) that can determine the source of the parameters corresponding to the context. The context can only be used when the validity is true.

[0046] For example, the data rule configuration of the workflow sequence includes three parts: rule definition, rule structure definition, and rule content definition.

[0047] Rule definitions include rule ID definition, rule name definition, and validity definition. Rule IDs must be unique, and the rule can only be used in context if its validity is true.

[0048] The rule structure definition mainly defines the parameter ID, parameter type, display name, and context corresponding to the parameter ID. The parameter ID comes from the parameter ID defined in the first step of parameter configuration. The parameter type has two types: input and output. The display name defaults to the name corresponding to the parameter ID, but can be modified to other names as needed. The context comes from the context ID defined in the second step of context configuration; when parameter IDs are the same, the parameter source can be distinguished by the context. It should be noted that the same parameter ID can exist in different contexts and with different parameter types simultaneously. When parameter IDs are the same, their corresponding parameter names must be modified to different names.

[0049] The rule content definition mainly defines the rule corresponding to the rule ID, and mainly defines the output content of one or more output parameter IDs through the combination of input parameter IDs.

[0050] For example, the data processing flow configuration of the workflow sequence includes two parts: inference definition and inference process definition.

[0051] The definition of an inference includes inference ID, inference name, and validity. An inference ID must be unique, and the inference can only be used if its validity is true.

[0052] The inference process definition mainly defines the inference steps corresponding to the inference ID, the inference parameter ID, the inference parameter type, the data type, the low value, the high value, and the range type. The inference steps are numbers, representing the inference steps from smallest to largest; the inference parameter ID comes from the parameter ID configured in step one; there are three types of inference parameters: input, output, and check, where the check result has only two possibilities: true or false (continue inference if true, stop inference if false); there are three types of data types: constant, expression, and rule, where the rule comes from the rule ID and output parameter defined in step three, and the expression can be SQL or reference to the parameter value of the context (\context\PAR_ID); the low value represents the minimum value of the parameter ID, and when the high value and range type are empty, it means the parameter ID equals the minimum value; the high value represents the maximum value of the parameter ID; the range type has four types: (), [], (], and [).

[0053] For example, the configuration of the workflow sequence is shown in Table 9, and the process definition of the workflow sequence is shown in Table 10.

[0054] Table 9

[0055] Table 10

[0056] For example, workflow configuration includes two parts: workflow definition and workflow process definition.

[0057] A workflow definition includes a workflow ID, workflow name, and validity information. A workflow ID must be unique, and the workflow can only be used if its validity is true.

[0058] The workflow process definition primarily defines the workflow steps, data source IDs, and inference IDs corresponding to the workflow ID. The workflow process definition mainly involves matching corresponding inferences to one or more data sources according to the workflow steps. After the workflow completes the inference step by step, it saves the last result of the input and output parameters corresponding to each data source. Workflow steps can be defined with the same value. When workflow steps are the same, it is necessary to perform traversal and combination inference. For example, if the steps are defined as 10, 20(A), 20(B), 30, the workflow becomes two combinations: 10-20(A)-30 and 10-20(B)-30, and the system performs inference separately for each combination.

[0059] Step S103: Input the data to be processed into the workflow sequence to obtain the output data of the workflow sequence; The data to be processed is input into the workflow sequence, which processes the data to be processed and obtains the output data of the workflow sequence. The output data is the data output by the workflow sequence.

[0060] For example, the output data can specifically be the execution output data of a workflow sequence.

[0061] For example, the execution method of the workflow sequence is as follows: Check if the workflow ID is valid. If invalid, stop and issue a warning message. If the workflow ID is valid, check if the data source ID, inference ID, parameter ID, rule ID, etc., corresponding to the workflow ID are valid (stop and issue a warning message if invalid, proceed according to the workflow steps if valid).

[0062] Workflow Step 1: Retrieve the parameter values ​​defined by the data source ID in Step 1, and perform inference according to the inference steps defined by the inference ID in Step 1. If the parameter type is "check", check whether the value corresponding to the parameter is true. If it is false, stop the inference and give a warning message; save the last parameter type of each parameter ID as input or output.

[0063] Workflow Step 2: Retrieve the parameter values ​​defined in Step 2 (Data Source ID), and perform inference according to the inference steps defined in Step 2 (Inference ID). If the parameter type is "check," check whether the value corresponding to that parameter is true. If it is false, stop the inference and issue a warning message. Save the last parameter type (input / output) value for each parameter ID.

[0064] Workflow step n: Retrieve the parameter values ​​defined by the data source ID in step n, and perform inference according to the inference steps defined by the inference ID in step n. If the parameter type is check, check whether the value corresponding to the parameter is true. If it is false, stop the inference and give a warning message; save the last parameter type of input / output for each parameter ID.

[0065] Step S104: Determine the processing result of the data to be processed based on the output data.

[0066] Based on the output data, determine the processing result of the data to be processed, where the processing result represents the outcome of the data to be processed.

[0067] For example, when the data to be processed is the quality index data of steel, the processing result can be specifically the quality test result of steel.

[0068] For example, when the data to be processed is steel inventory data, the processing result can be specifically a steel logistics planning scheme.

[0069] The data processing method in this embodiment determines the workflow sequence corresponding to the data to be processed by customizing and configuring data parameters, data source information, input-output relationships, data rules, and data processing flow. This improves the customizability of the workflow sequence and thus improves the development efficiency of the workflow sequence. Furthermore, the method processes the data to be processed through an efficient workflow program to determine the processing result, thereby improving the efficiency of determining the processing result and ultimately improving the processing efficiency of the data to be processed.

[0070] In some embodiments, this application provides a data processing method that inputs data to be processed into a workflow sequence to obtain output data of the workflow sequence, including: The validity of steel quality data is checked through the workflow sequence to obtain the first inspection result corresponding to the steel quality data; In this embodiment, the data to be processed includes steel quality data, which mainly covers multiple dimensions such as chemical composition, mechanical properties, dimensional deviation, surface quality and process performance, and is the core basis for evaluating whether the steel meets the requirements of engineering applications.

[0071] The validity of steel quality data is checked through the workflow sequence to obtain the first inspection result corresponding to the steel quality data, where the first inspection result indicates whether the steel quality data is valid.

[0072] For example, the first check result can be either the data is valid or the data is invalid.

[0073] If the initial inspection result indicates that the data is valid, the steel quality data is processed through the workflow sequence to obtain the output data.

[0074] If the first inspection result indicates that the data is valid, it means that the steel quality data can be processed. The steel quality data is processed through the workflow sequence to obtain the output data.

[0075] For example, if the first check result is that the data is invalid, data processing of the steel quality data is stopped and the steel quality data is updated.

[0076] In some embodiments, this application provides a data processing method for determining the processing result of data to be processed based on output data, including: If the steel quality index is greater than or equal to the preset index, the processing result is determined to be qualified. In this embodiment, the output data includes a steel quality index, which is an index representing the quality of steel.

[0077] For example, the steel quality index can be a value between 1 and 100, with a higher value indicating better steel quality.

[0078] Obtain the preset index, where the preset index is a preset index threshold.

[0079] For example, the preset index can be specifically 80. If the steel quality index is greater than or equal to the preset index, it indicates that the steel quality is good, and the processing result is determined to be qualified.

[0080] If the steel quality index is less than the preset index, the processing result is determined to be unqualified.

[0081] If the steel quality index is less than the preset index, it indicates that the steel quality is poor, and the processing result is determined to be unqualified.

[0082] In some embodiments, this application provides a data processing method that inputs data to be processed into a workflow sequence to obtain output data of the workflow sequence, including: The validity of steel inventory data is checked through the workflow sequence to obtain the second check result corresponding to the steel quality data; In this embodiment, the data to be processed includes steel inventory data, which is data representing the steel inventory status.

[0083] The steel inventory data is validated through a workflow sequence to obtain a second validation result corresponding to the steel quality data. The second validation result indicates the validity of the steel inventory data.

[0084] For example, the second check result may specifically be that the data is valid or invalid.

[0085] If the second check result indicates that the data is valid, the steel inventory data is processed through the workflow sequence to obtain the output data.

[0086] If the second check result indicates that the data is valid, it means that the steel inventory data can be processed. The steel inventory data is processed through the workflow sequence to obtain the output data.

[0087] In some embodiments, this application provides a data processing method for determining the processing result of data to be processed based on output data, including: If the inventory quantity is greater than or equal to the preset quantity, the processing result is determined to be sufficient inventory; In this embodiment, the output data includes the inventory quantity, where the inventory quantity is the quantity of steel inventory.

[0088] If the inventory quantity is greater than or equal to the preset quantity, it indicates that the inventory quantity is sufficient, and the processing result is determined as sufficient inventory.

[0089] If the inventory quantity is less than the preset quantity, the processing result is determined as inventory shortage.

[0090] If the inventory quantity is less than the preset quantity, it indicates that the inventory quantity is insufficient, and the processing result is determined as inventory shortage.

[0091] In some embodiments of this application, a data processing method is provided. After determining the processing result of the data to be processed based on the output data, the method further includes: Based on the processing results, determine the data update plan for the data to be processed.

[0092] In this embodiment, after determining the processing result of the data to be processed, a data update scheme for the data to be processed is determined based on the processing result, wherein the data update scheme is the update scheme for the data to be processed.

[0093] For example, if the processing result meets the customer's needs, it is necessary to determine the data update plan for the data to be processed.

[0094] For example, data update solutions are mainly divided into three categories based on different application scenarios and data scales: batch update, incremental update, and real-time synchronization. Each solution has its own applicable scenarios and key technical implementation points.

[0095] Batch updates are suitable for scenarios with small amounts of data or those requiring complete coverage. They are simple to operate but consume a lot of resources.

[0096] Incremental updates only process newly added or modified data, making them highly efficient and the preferred choice for large-scale systems.

[0097] Real-time data synchronization is used for systems with high timeliness requirements.

[0098] For example, after updating the data to be processed, the workflow sequence needs to be updated further.

[0099] In some embodiments of this application, a data processing method is provided. After determining the workflow sequence corresponding to the data to be processed, the method further includes: Determine the data source for storing the data to be processed; When establishing a data connection between the data source and the workflow sequence, receive the data to be processed sent by the data source.

[0100] In this embodiment, a data source for storing the data to be processed is determined, wherein the data source is the data source head that stores the data to be processed.

[0101] For example, the data source can be specifically a database that stores the data to be processed.

[0102] For example, the data source can be specifically the cloud that stores the data to be processed.

[0103] When establishing a data connection between the data source and the workflow sequence, receive the data to be processed sent by the data source.

[0104] In some embodiments, Figure 2 This is a functional block diagram of the data processing apparatus provided in the embodiments of this application, such as... Figure 2 As shown, an embodiment of this application provides a data processing apparatus 200, including: The first processing unit 202 is used to determine the data parameters, data source information, input-output relationship, data rules and data processing flow corresponding to the data to be processed when the data to be processed is to be processed. The second processing unit 204 is used to determine the workflow sequence corresponding to the data to be processed based on data parameters, data source information, input-output relationship, data rules and data processing flow. The third processing unit 206 is used to input the data to be processed into the workflow sequence to obtain the output data of the workflow sequence; The fourth processing unit 208 is used to determine the processing result of the data to be processed based on the output data.

[0105] In some embodiments of this application, a data processing apparatus 200 is provided, and the third processing unit 206 is further configured to... The validity of steel quality data is checked through the workflow sequence to obtain the first inspection result corresponding to the steel quality data; If the initial inspection result indicates that the data is valid, the steel quality data is processed through the workflow sequence to obtain the output data.

[0106] In some embodiments of this application, a data processing apparatus 200 is provided, and the fourth processing unit 208 is further used for... If the steel quality index is greater than or equal to the preset index, the processing result is determined to be qualified. If the steel quality index is less than the preset index, the processing result is determined to be unqualified.

[0107] In some embodiments of this application, a data processing apparatus 200 is provided, and the third processing unit 206 is further configured to... The validity of steel inventory data is checked through the workflow sequence to obtain the second check result corresponding to the steel quality data; If the second check result indicates that the data is valid, the steel inventory data is processed through the workflow sequence to obtain the output data.

[0108] In some embodiments of this application, a data processing apparatus 200 is provided, and the fourth processing unit 208 is further used for... If the inventory quantity is greater than or equal to the preset quantity, the processing result is determined to be sufficient inventory; If the inventory quantity is less than the preset quantity, the processing result is determined as inventory shortage.

[0109] In some embodiments of this application, a data processing apparatus 200 is provided, further comprising a fifth processing unit for... Based on the processing results, determine the data update plan for the data to be processed.

[0110] In some embodiments of this application, a data processing apparatus 200 is provided, further comprising a sixth processing unit for... Determine the data source for storing the data to be processed; When establishing a data connection between the data source and the workflow sequence, receive the data to be processed sent by the data source.

[0111] In some embodiments, Figure 3 This is a structural block diagram of the data processing apparatus provided in the embodiments of this application, such as... Figure 3 As shown, a data processing apparatus 300 is proposed. The data processing apparatus 300 includes a processor 302 and a memory 304. The memory 304 stores a computer program, which, when executed by the processor 302, implements the steps of the data processing method as described in any of the above embodiments. Therefore, the data processing apparatus 300 possesses all the beneficial effects of the data processing method in any of the above embodiments, which will not be elaborated further here.

[0112] In some embodiments, a readable storage medium is provided on which a program is stored, which, when executed by a processor, implements the steps of the data processing method as described in any of the above embodiments, and thus has all the beneficial technical effects of the data processing method in any of the above embodiments.

[0113] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0114] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0115] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform a data processing method.

[0119] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods 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, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0122] 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 according to actual needs.

[0123] 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 functional unit.

[0124] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. 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 storage medium 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 of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0125] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0126] Although preferred embodiments have been described in this specification, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this specification.

[0127] Obviously, those skilled in the art can make various modifications and variations to this specification without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims and their equivalents, this specification is also intended to include such modifications and variations.

Claims

1. A data processing method, characterized in that, The method includes: When processing data to be processed, determine the data parameters, data source information, input-output relationships, data rules, and data processing flow corresponding to the data to be processed. Based on the data parameters, the data source information, the input-output relationship, the data rules, and the data processing flow, determine the workflow sequence corresponding to the data to be processed. The data to be processed is input into the workflow sequence to obtain the output data of the workflow sequence; Based on the output data, determine the processing result of the data to be processed.

2. The method according to claim 1, characterized in that, When the data to be processed includes steel quality data, the step of inputting the data to be processed into the workflow sequence to obtain the output data of the workflow sequence includes: The steel quality data is validated through the aforementioned workflow sequence to obtain the first check result corresponding to the steel quality data. If the first inspection result is valid, the steel quality data is processed through the workflow sequence to obtain the output data.

3. The method according to claim 2, characterized in that, The output data includes a steel quality index, and determining the processing result of the data to be processed based on the output data includes: If the steel quality index is greater than or equal to the preset index, the processing result is determined to be of qualified quality. If the steel quality index is less than the preset index, the processing result is determined to be substandard.

4. The method according to claim 1, characterized in that, When the data to be processed includes steel inventory data, the step of inputting the data to be processed into the workflow sequence to obtain the output data of the workflow sequence includes: The steel inventory data is validated through the aforementioned workflow sequence to obtain a second check result corresponding to the steel quality data. If the second check result indicates that the data is valid, the steel inventory data is processed through the workflow sequence to obtain the output data.

5. The method according to claim 4, characterized in that, The output data includes inventory quantity, and determining the processing result of the data to be processed based on the output data includes: If the inventory quantity is greater than or equal to the preset quantity, the processing result is determined to be sufficient inventory. If the inventory quantity is less than the preset quantity, the processing result is determined to be an inventory shortage.

6. The method according to any one of claims 1 to 5, characterized in that, After determining the processing result of the data to be processed based on the output data, the method further includes: Based on the processing results, a data update scheme for the data to be processed is determined.

7. The method according to any one of claims 1 to 5, characterized in that, After determining the workflow sequence corresponding to the data to be processed, the method further includes: Determine the data source for storing the data to be processed; When establishing a data connection between the data source and the workflow sequence, the pending data sent by the data source is received.

8. A data processing apparatus, characterized in that, The device includes: The first processing unit is used to determine the data parameters, data source information, input-output relationship, data rules and data processing flow corresponding to the data to be processed when the data to be processed is to be processed. The second processing unit is used to determine the workflow sequence corresponding to the data to be processed based on the data parameters, the data source information, the input-output relationship, the data rules, and the data processing flow. The third processing unit is used to input the data to be processed into the workflow sequence to obtain the output data of the workflow sequence; The fourth processing unit is used to determine the processing result of the data to be processed based on the output data.

9. A data processing apparatus, characterized in that, include: processor; A memory that stores programs or instructions, wherein a processor, when executing the programs or instructions in the memory, implements the steps of the data processing method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, A program or instructions are stored on a readable storage medium, which, when executed by a processor, implement the steps of the data processing method as described in any one of claims 1 to 7.

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