Data flow management system and data flow management method

Abstract

The invention provides a data flow management system and a data flow management method. The data flow management system comprises a memory and a processor. The processor is electrically connected with the memory and is used for executing a data flow. The processor executes task nodes in the data flow and generates output data. The processor determines whether the data state of the output data reaches the target data state to determine whether to acquire the next task node in the data flow. When the processor judges that the data state of the output data does not reach the target data state, the processor obtains the next task node, and the processor determines to execute the next task node in a data branch mode or a data distribution mode according to the data feature set and the branch identifier. And when the processor judges that the data state of the output data reaches the target data state, the processor ends the data flow.

Description

Technical Field

[0001] The present invention relates to an efficient and automated data processing process, and in particular to a data process management system and a data process management method. Background Art

[0002] In the application of Enterprise Resource Planning (ERP) system, the business data flow can be automated by executing the relevant business logic through the computer to realize the corresponding business processing and business management. In this regard, with the increase of business complexity and the amount of business data, the business data flow may not be limited to a single execution path. However, the existing business data flow processing methods are unable to realize the ability of data branching and data diversion in the business data flow. Therefore, if complex business processing is to be realized, the traditional business processing flow can only achieve the business goal by repeatedly executing at least a part of the flow. The traditional business processing flow has no data branching and data diversion processing functions, but can only repeat the whole or at least a part of the flow for the same or different multiple data in the same input data to complete the business. Therefore, the design of the traditional business data flow often leads to the system needing to repeatedly execute the same task nodes in large quantities, resulting in a waste of computing resources and a reduction in computing efficiency. Summary of the invention

[0003] The present invention is directed to a data flow management system and a data flow management method, which can realize efficient and automated data processing flow.

[0004] According to an embodiment of the present invention, the data flow management system of the present invention includes a memory and a processor. The processor is electrically connected to the memory and is used to execute the data flow. The processor executes the task node in the data flow and generates output data. The processor determines whether the data state of the output data reaches the target data state to determine whether to obtain the next task node in the data flow. When the processor determines that the data state of the output data does not reach the target data state, the processor obtains the next task node, and the processor determines to execute the next task node in a data branching mode, a data diversion mode or a path selection mode based on the data feature set and the branch identifier. When the processor determines that the data state of the output data reaches the target data state, the processor ends the data flow.

[0005] According to an embodiment of the present invention, the data flow management method of the present invention includes the following steps: executing the data flow through a processor; executing the task node in the data flow through the processor and generating output data; judging through the processor whether the data state of the output data reaches the target data state to decide whether to obtain the next task node in the data flow; when the processor judges that the data state of the output data does not reach the target data state, obtaining the next task node through the processor, and deciding through the processor to execute the next task node in a data branching manner, a data diversion manner or a path selection manner according to a data feature set and a branch identifier; and ending the data flow when the processor judges that the data state of the output data reaches the target data state.

[0006] The data flow management system and the data flow management method of the present invention can determine whether to execute the next task node in a data branching manner or a data diversion manner according to the data feature set and the branch identifier, so as to realize an efficient data processing flow.

[0007] In order to make the above features and advantages of the present invention more clearly understood, embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a schematic diagram of a data flow management system according to an embodiment of the present invention;

[0009] Figure 2 is a flow chart of a data flow management method according to an embodiment of the present invention;

[0010] Figure 3 is a flow chart of a data flow management method according to an embodiment of the present invention;

[0011] Figure 4 is a flow chart of a data flow management method according to an embodiment of the present invention;

[0012] Figure 5A is a schematic diagram of data features of an embodiment of the present invention;

[0013] Figure 5B is a schematic diagram of a data branch according to an embodiment of the present invention;

[0014] Fig. 6A is a schematic diagram of data features of an embodiment of the present invention;

[0015] Figure 6B is a schematic diagram of data splitting according to an embodiment of the present invention;

[0016] Figure 7 is a schematic diagram of path selection according to an embodiment of the present invention;

[0017] Fig. 8A is a schematic diagram of input data according to an embodiment of the present invention;

[0018] Figure 8B is a schematic diagram of a data feature set according to an embodiment of the present invention;

[0019] Fig. 9A is a schematic diagram of a data branch according to an embodiment of the present invention;

[0020] Fig. 9B is a schematic diagram of a data branch according to an embodiment of the present invention;

[0021] Fig. 10A is a schematic diagram of input data according to an embodiment of the present invention;

[0022] Fig. 10B is a schematic diagram of a data feature set according to an embodiment of the present invention;

[0023] Fig.11A is a schematic diagram of a task node of an embodiment of the present invention;

[0024] Fig. 11B is a schematic diagram of a task node of an embodiment of the present invention;

[0025] Fig. 11C is a schematic diagram of a task node of an embodiment of the present invention;

[0026] Fig. 12A is a schematic diagram of data splitting according to an embodiment of the present invention;

[0027] Fig. 12B It is a schematic diagram of data splitting according to an embodiment of the present invention.

[0028] Description of Reference Numerals

[0029] 100: Data process management system;

[0030] 110: processor;

[0031] 120: memory;

[0032] 210: computer device;

[0033] 220: external database;

[0034] 221: processor;

[0035] 222: storage device;

[0036] S210-S250, S310-S350, S410-470: steps;

[0037] 510, 610, 720, 820, 1020: data feature set;

[0038] 520, 620, 810, 1010: input data;

[0039] 531, 532, 631, 632, 710, 730, 911-913, 921-924, 1110-1130, 1211, 1212: task nodes. DETAILED DESCRIPTION

[0040] Reference will now be made in detail to exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

[0041] Figure 1 FIG. 1 is a schematic diagram of a data flow management system according to an embodiment of the present invention. Figure 1 The data flow management system 100 includes a processor 110 and a memory 120. The processor 110 is electrically connected to the memory 120. In this embodiment, the data flow management system 100 can communicate with an external computer device 210 and an external database 220. The memory 120 can be used to temporarily store source data, data status, data features and other data during the data flow. In this embodiment, the external database 220 includes a processor 221 and a storage device 222. The processor 221 is electrically connected to the storage device 222. The storage device 222 can be used to store relevant process variables of the data flow, source data and a predefined data feature set.

[0042] In this embodiment, the user can operate the computer device 210 to issue a process initiation request instruction or signal to the data flow management system 100, so that the processor 110 of the data flow management system 100 can access the data of the external database 220 and execute the relevant algorithms, programs and / or software of the data flow to create and execute the corresponding data flow.

[0043] In the present embodiment, the processor 110 and the processor 221 may be a system on a chip (SOC), or may include a central processing unit (CPU) or other programmable general-purpose or special-purpose microprocessor, digital signal processor (DSP), programmable controller, application specific integrated circuits (ASIC), programmable logic device (PLD), other similar processing devices or a combination of these devices. In the present embodiment, the memory 120 may be, for example, a dynamic random access memory (DRAM), a flash memory or a non-volatile random access memory (NVRAM). In the present embodiment, the storage device 222 may be, for example, a disk.

[0044] Figure 2 FIG. 1 is a flow chart of a data flow management method according to an embodiment of the present invention. Figure 1 as well as Figure 2 , the data flow management system 100 may execute the following steps S210 to S250. In step S210, the processor 110 executes the data flow. In the present embodiment, the data flow may be composed of a plurality of task nodes. In step S220, the processor 110 executes the task nodes in the data flow and generates output data. In the present embodiment, a flow path is formed between the plurality of task nodes, and the output data of the previous task node is used as the input data of the next task node. In step S230, the processor 110 determines whether the data state of the output data reaches the target data state to determine whether to obtain the next task node in the data flow. In the present embodiment, the processor 110 may obtain the data state of the output data and match the data flow. The processor 110 may query the next task node in the data flow according to the data state, the data feature set and the branch identifier. Furthermore, when the processor 110 determines that there are multiple next task nodes, the processor 110 may determine whether to execute the next task node in a data branching manner or a data diversion manner according to the branch identifier.

[0045] In step S240, when the data state of the output data does not reach the target data state, the processor 110 obtains the next task node, and determines to execute the next task node in a data branching manner or a data diversion manner according to the data feature set and the branch identifier. In step S250, when the data state of the output data reaches the target data state, the processor 110 ends the data flow. Therefore, the data flow management system and the data flow management method of this embodiment can realize the data flow management functions of data branching and data diversion.

[0046] Figure 3 FIG. 1 is a flow chart of a data flow management method according to an embodiment of the present invention. Figure 1 as well as Figure 3 , the implementation method of the overall data flow management process can be as follows S310 to S350. In step S310, the processor 110 obtains the first task node according to the initial state of the data flow. In this embodiment, the processor 110 can obtain the first task node and the data feature set in a state. The data feature set refers to a collection of data features of the same type, and can be used to identify the features on which the data in the process depends, such as the order gross profit margin feature set, the deposit ratio feature set, etc. The data feature set may include a data feature set code, a data feature set name, and one or more data features. Each data feature in the data feature set includes a record of the data feature code, feature name information, feature matching method information, and feature matching rule information. In this embodiment, the feature matching rule information can be an expression or an external application program port (Application Programming Interface, API) request address. When the feature matching rule information is configured to take values ​​from process variables, the processor 110 executes the next task node in a data branching manner.

[0047] In step S320, the processor 110 executes the task node. In step S330, the processor 110 generates output data. In the present embodiment, the output data includes a data state code, a data state name, and data features. And, the output data will be used as input data for the next task node. In step S340, the processor 110 determines whether the data state of the output data reaches the target data state. In this regard, the processor 110 may determine whether the state code of the output data is consistent with the state code of the target data. If so, the processor 110 ends the execution of the data flow. If not, in step S350, the processor 110 obtains the next task node and re-executes step S320.

[0048] Figure 4 FIG. 1 is a flow chart of a data flow management method according to an embodiment of the present invention. Figure 1 as well as Figure 4 , the above Figure 3 The specific implementation process of step S320 for determining the next task node may be as follows: steps S410 to S470. In step S410, the processor 110 obtains the data state of the output data and matches the data flow. In this embodiment, the data state may include a data state code, a data state name, and data features. In this regard, the data state can be used to advance the data flow. In the data-driven process mode, the processor 110 can select a process node by inputting the data state, and output data will be generated after the process node (task node) is executed. The output data state of the output data becomes the input data state of the next node. By analogy, after the final process is executed to the target data state, the processor 110 ends the data flow.

[0049] In step S420, the processor 110 queries the next task node in the data flow according to the data state, data feature set and branch identifier of the output data. In this embodiment, the processor 110 can match the data features in the output data with the data in the data flow. It is further explained that the branch identifier is defined in the task node. When the processor 110 searches for the next node, the processor 110 can match multiple task nodes through the data state and data feature set. In this regard, if the branch identifier is set in the data, all matched task nodes are returned, and if the branch identifier is not set in the data, only the task node with the highest score is returned. In step S430, the processor 110 determines whether the next task node is multiple. If not, in step S460, the processor 110 will return the judgment result of a single next task node. If so, in step S440, the processor 110 determines whether the data is branched. In this embodiment, the processor 110 can determine whether to perform a data branch operation according to the branch identifier. If so, in step S470, the processor 110 returns the judgment results of multiple next task nodes. If not, in step S450, the processor 110 determines to execute one of the multiple next task nodes according to the multiple weight values ​​corresponding to the multiple next task nodes. In the present embodiment, the processor 110 can be sorted according to the (feature) weight corresponding to each of the multiple task nodes to determine the task node with the highest weight value. The processor 110 can choose to execute one of the multiple next task nodes corresponding to the highest weight value. In other words, the processor 110 can implement the path selection function of the task node. In step S460, the processor 110 returns the judgment result of a single next task node. Therefore, the data flow management system and the data flow management method of the present embodiment can automatically determine to execute the next task node in a data branching mode or a data diversion mode to achieve an efficient data processing flow.

[0050] Figure 5AIt is a schematic diagram of data features according to an embodiment of the present invention. Figure 5B FIG. 1 is a schematic diagram of a data branch according to an embodiment of the present invention. Figure 1 , Figure 5A as well as Figure 5B , this embodiment specifically describes the method of implementing data branching. It should be noted that the data structure of each data described in the following embodiments can be JSON (JavaScript Object Notation) format data as an example, but the present invention is not limited to this. In one embodiment, the data structure of each data described in each embodiment can also be implemented using data formats that meet the hierarchical structure, such as Extensible Markup Language (XML), TOML (Tom's Obvious, Minimal Language), CSON (Cursive Script Object Notation), and YAML.

[0051] like Figure 5A As shown, the data features in the data feature set 510 may include a data feature code "code", feature name information "name", feature matching method information "type" and feature matching rule information "expression". The data feature code "code" may be "dataFeature-A". The feature name information "name" may be a "vip" feature. The feature matching method information "type" may be "script". The feature matching rule information "expression" may be "$(is_vip==true)" (i.e., a script). Figure 5B As shown, the input data 520 may include data [1, 2, 3, 4]. In this embodiment, the processor 110 may search for the task nodes with the same "vip" feature in the data flow according to the feature name information "name" in the data feature set 510 to obtain task node (A) 631 and task node (B) 632, and may determine whether the value is taken from the process variable according to the feature matching method information "type" and the feature matching rule information "expression" in the data feature, so it may be determined to perform the data branching operation of task node (A) 631 and task node (B) 632 according to the input data 520. In other words, the processor 110 may input the data [1, 2, 3, 4] of the input data 520 into the logic of task node (A) 631 and task node (B) 632 respectively to perform the corresponding logical operation. Therefore, the data flow management system 100 can realize the process operation scenario of data branching in the process of executing the data flow.

[0052] Fig. 6ASchematic diagram of data features of an embodiment of the present invention. Figure 6B Schematic diagram of data shunting of an embodiment of the present invention. Refer to Figure 1 , Fig. 6A and Figure 6B , this embodiment specifically illustrates the method of realizing data shunting. As Fig. 6A shown, the data feature set 610 may include two data features. The data feature code "code" of one data feature may be "dataFeature-A". The feature name information "name" may be "female feature". The feature matching method information "type" may be "script". The feature matching rule information "expression" may be "$(data).sex == 'female'}" (i.e., script). The data feature code "code" of the other data feature may be "dataFeature-B". The feature name information "name" may be "male feature". The feature matching method information "type" may be "script". The feature matching rule information "expression" may be "$(data).sex =='male}" (i.e., script).

[0053] As Figure 6B shown, the input data 620 may include data [1, 2, 3, 4]. In this embodiment, the processor 110 may search for task nodes that are also "female feature" and "male feature" in the data flow according to the feature name information "name" of the two data features in the data feature set 610 to obtain task node A and task node B, and may determine whether to obtain values from the process variables according to the feature matching method information "type" and the feature matching rule information "expression" in the data features. Therefore, it can be determined to perform data shunting operations on task node 631 and task node 632 according to the input data 620. In other words, the processor 110 may input the data [1, 3] of the input data 620 into the logic of task node 631 to perform corresponding logical operations. Alternatively, the processor 110 may input the data [2, 4] of the input data 620 into the logic of task node 632 to perform corresponding logical operations. The processor 110 may perform path selection to select to execute task node 631 or task node 632.

[0054] Figure 7 Schematic diagram of path selection of an embodiment of the present invention. Refer to Figure 1 and Figure 7, this embodiment specifically describes the method for implementing path selection. In this embodiment, task node (1) 710 can generate input data for the next task node, including data "data01", input data state "dataState01" and data features "dataFeature01" and "dataFeature02". The data feature set 720 may include data features of two task nodes (2-1) and (2-2). The data features of task node (2-1) include input data state "dataState01", output data state "dataState02", node name "node 2-1", feature code "dataFeature01" and weight value "80" of the weight occupied by the feature. The data features of task node (2-2) include input data state "dataState01", output data state "dataState02", node name "node 2-2", feature code "dataFeature02" and weight value "60" of the weight occupied by the feature.

[0055] In this embodiment, the processor 110 can obtain two task nodes (2-1) and (2-2) that are connected to the task node (1) 710 according to the input data state "dataState01" of the input data, and the processor 110 can use the data features "dataFeature01" and "dataFeature02" to determine whether the two data features match the task nodes (2-1) and (2-2) respectively. In this regard, the processor 110 can compare that the weight of the feature of the task node (2-1) (weight value 80) is higher than the weight of the feature of the task node (2-2) (weight value 60), so the processor 110 will report the task node (2-1) as the next task node (2-1) to be executed. Therefore, based on the data branching method and the current path selection method described in the above embodiment, the data flow management system 100 can realize the process operation scenario of data branching during the execution of the data flow.

[0056] Fig. 8A is a schematic diagram of input data according to an embodiment of the present invention. Figure 8B It is a schematic diagram of a data feature set according to an embodiment of the present invention. Fig. 9A is a schematic diagram of data branches according to an embodiment of the present invention. Fig. 9B is a schematic diagram of data branches according to an embodiment of the present invention. FIG. 8A to FIG. 9B This is an exemplary embodiment of the data branch of the present invention. Fig. 8A, the input data 810 may include multiple data. For example, the first data may include the employee ID "emp_no": "0001", the mobile phone number "telephone": "139xxxxx", the email address "email": "xxxx@digiwin.com", and the communication software account name "wechat": "erww43". The second data may include the employee ID "emp_no": "0012", the mobile phone number "telephone": "185xxxxxx", the email address "email": "yyyyy@digiwin.com", and the communication software account name "wechat": "efdd43".

[0057] refer to Figure 8B , the data feature set 820 may include multiple data features. For example, the first data feature may include, for example, the data feature code ““code”: “normal””, the feature name information ““name”: “normal””, the feature matching method information ““type”: “script””, and the feature matching rule information ““expression”: “$(emergencyDegree)=='normal'””. The second data feature may include, for example, the data feature code ““code”: “normal””, the feature name information ““name”: “normal””, the feature matching method information ““type”: “script””, and the feature matching rule information ““expression”: “$(emergencyDegree)=='normal'””.

[0058] refer to Figure 1 as well as Fig. 9A , task node 911 can be used to execute the purchase requisition pending approval operation. Task node 911 can output the relevant data of the purchase requisition to be approved, and perform data branching according to the input data status and data characteristics. In this regard, processor 110 can decide which notification method to use to notify the approver according to the urgency of the purchase requisition to be approved. In this regard, processor 110 can match the data of the process with the conditions of the data characteristics to obtain the data characteristics of the urgency, and processor 110 can obtain the information of the urgency by judging the data characteristics of the urgency. Fig. 9A As shown, if the urgency level is normal, the processor 110 Fig. 8A The data feature set 820 is used to determine whether to continue to execute task nodes 912 and 913, so as to obtain multiple branch tasks to perform different processing on the same data. Task node 912 can be used according to Figure 8BThe email address "email": "xxxx@digiwin.com" and the email address "email": "yyyyy@digiwin.com" in the input data are used to send email notifications to the users corresponding to the employee ID "emp_no": "0001" and the employee ID "emp_no": "0012". Task node 913 can be used to Figure 8B The communication software account name ""wechat":"erww43"" and the communication software account name ""wechat":"efdd43"" in the input data send communication software notifications to users corresponding to employee numbers ""emp_no":"0001"" and employee numbers ""emp_no":"0012"".

[0059] Alternatively, refer to Figure 1 as well as Fig. 9B , task node 921 can be used to execute the purchase requisition pending approval operation. Task node 921 can output the relevant data of the purchase requisition pending approval, and perform data branching according to the input data status and data characteristics. Fig. 9B As shown, if the urgency level is urgent, the processor 110 Fig. 8A The data feature set 820 is used to determine the next execution of task nodes 922, 923, and 924, so as to obtain multiple branch tasks to perform different processing on the same data. Task node 922 can be used according to Figure 8B The email address "email": "xxxx@digiwin.com" and the email address "email": "yyyyy@digiwin.com" in the input data are used to send email notifications to the users corresponding to the employee ID "emp_no": "0001" and the employee ID "emp_no": "0012". Task node 923 can be used to Figure 8B The communication software account name "wechat": "erww43"" and the communication software account name "wechat": "efdd43"" in the input data of the communication software notification are sent to the user corresponding to the employee number "emp_no": "0001"" and the employee number "emp_no": "0012". Task node 924 can be used to Figure 8B The mobile phone number ""telephone":"139xxxxx"" and the mobile phone number ""telephone":"185xxxxxx"" in the input data send SMS notifications to the users corresponding to the employee number ""emp_no":"0001"" and the employee number ""emp_no":"0012"".

[0060] Fig. 10Ais a schematic diagram of input data according to an embodiment of the present invention. Fig. 10B It is a schematic diagram of a data feature set according to an embodiment of the present invention. Fig.11A It is a schematic diagram of a task node according to an embodiment of the present invention. Fig. 11B It is a schematic diagram of a task node according to an embodiment of the present invention. Fig. 11C It is a schematic diagram of a task node according to an embodiment of the present invention. Fig. 12A It is a schematic diagram of data splitting according to an embodiment of the present invention. Fig. 12B It is a schematic diagram of data splitting according to an embodiment of the present invention. FIG. 10A to FIG. 12B This is an exemplary embodiment of the data diversion of the present invention. This embodiment can be used to implement the process data initiation flow, wherein the data flow may include task nodes such as shipment, start of work, and delivery. Therefore, the system may predefine two data feature sets, which may be, for example, a lead time feature set and an order gross profit feature set. The lead time feature set may include emergency features and general features. The order gross profit feature set may include high gross profit features and low gross profit features.

[0061] refer to Fig. 10A , the input data 1010 may include multiple data. For example, the first data may include the work order number ""wo_no": "S600-0001"", the deadline days ""deadline": "20"" and the gross profit margin ""margin": "0.25"". The second data may include the work order number ""wo_no": "S600-0002"", the deadline days ""deadline": "5"" and the gross profit margin ""margin": "0.2"".

[0062] refer to Fig. 10B, the data feature set 1020 may be a plurality of data feature sets. For example, the first data feature of the first data set may include, for example, the data feature code ““code”: “lowMargin””, the feature name information ““name”: “Low gross profit””, the feature matching method information ““type”: “script””, and the feature matching rule information ““expression”: “$(margin)<0.15””. The second data feature of the first data set may include, for example, the data feature code ““code”: “highMargin””, the feature name information ““name”: “High gross profit””, the feature matching method information ““type”: “script””, and the feature matching rule information ““expression”: “$(margin)>=0.15””. The first data feature of the second data set may, for example, include a data feature code ““code”: “normal””, feature name information ““name”: “normal””, feature matching method information ““type”: “script””, and feature matching rule information ““expression”: “$(deadline)>10””. The second data feature of the second data set may, for example, include a data feature code ““code”: “emergency””, feature name information ““name”: “urgent””, feature matching method information ““type”: “script””, and feature matching rule information ““expression”: “$(deadline)<=10””.

[0063] refer to Fig.11AThe data features of task node (A) 1110 may include input data state "from": "dataState01", output data state "to": "dataState02", node name "name": "delivery confirmation", feature code "code": "highMargin" and its corresponding feature weight "weight": "80", and feature code "code": "normal" and its corresponding feature weight "weight": "50". The data features of task node (B) 1120 may include input data state "from": "dataState01", output data state "to": "dataState02", node name "name": "batch feeding", feature code "code": "lowMargin" and its corresponding feature weight "weight": "80", and feature code "code": "normal" and its corresponding feature weight "weight": "50". The data features of task node (C) 1110 may include input data state "from": "dataState01", output data state "to": "dataState04", node name "name": "Purchasing materials from the same industry", feature code "code": "highMargin" and its corresponding feature weight "weight": "80", and feature code "code": "emergency" and its corresponding feature weight "weight": "100".

[0064] refer to Figure 1 as well as Fig. 12A ,for Fig. 10A For the data of the work order number "S600-0001" in the input data 1010, after the processor 110 executes the task node 1211 of early delivery and the task node 1212 of rush purchase according to the input data 1010, the processor 110 can perform data diversion according to the input data status and data characteristics to select one of the task nodes 1110 to 1130 as the next task node. Task node 1110 can be used to execute delivery confirmation. Task node 1120 can be used to execute batch feeding. Task node 1130 can be used to execute peer purchase. In this regard, the processor 110 can be based on the relevant data of the work order number "S600-0001" in the input data 1010 and Fig. 10BThe processor 110 determines that the deadline ""deadline":"20"" and the gross profit margin ""margin":"0.25"" corresponding to the work order number "S600-0001" can match the feature code ""code":"highMargin" and the feature code ""code":"normal" of the task node 1110 with high gross profit and normal urgency, and it has a higher weight value than other task nodes. Therefore, the processor 110 can select task node 1110 as the next task node, and use the relevant data corresponding to the work order number "S600-0001" to continue to execute it.

[0065] refer to Figure 1 as well as Fig. 12B ,for Fig. 10A For the data of the work order number "S600-0002" in the input data 1010, after the processor 110 executes the task node 1211 of early delivery and the task node 1212 of rush delivery according to the input data 1010, the processor 110 can perform data diversion according to the input data state and data characteristics to select one of the task nodes 1110 to 1130 as the next task node. In this regard, the processor 110 can select one of the task nodes 1110 to 1130 as the next task node according to the relevant data of the work order number "S600-0002" in the input data 1010 and Fig. 10B The processor 110 determines that the deadline ""deadline":"5"" and the gross profit margin ""margin":"0.2"" corresponding to the work order number "S600-0002" can match the feature code ""code":"highMargin" and the feature code ""code":"emergency" of the task node 1130 with high gross profit and urgent urgency, and it has a higher weight value than other task nodes. Therefore, the processor 110 can select task node 1130 as the next task node, and use the relevant data corresponding to the work order number "S600-0002" to continue to execute it.

[0066] In summary, the data flow management system and data flow management method of the present invention can automatically execute the next task node in a data branching manner or a data diversion manner to achieve an efficient data processing flow and can effectively reduce the amount of computation required during the data processing flow.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data flow management system, characterized in that: include: Memory; as well as A processor, electrically connected to the memory, and used to execute a data flow, wherein the processor executes the task nodes in the data flow and generates output data, The processor determines whether the data state of the output data reaches the target data state to determine whether to obtain the next task node in the data flow. When the processor determines that the data state of the output data has not reached the target data state, the processor obtains the next task node, and the processor determines whether to execute the next task node in a data branching manner or a data diversion manner according to a data feature set and a branch identifier, When the processor determines that the data state of the output data reaches the target data state, the processor ends the data flow.

2. The data flow management system according to claim 1, characterized in that: The processor uses the output data as input data of the next task node.

3. The data flow management system according to claim 1, characterized in that: The processor obtains the data state of the output data and matches the data flow, The processor queries the next task node in the data flow according to the data state, the data feature set and the branch identifier.

4. The data flow management system according to claim 3, characterized in that: When the processor determines that there are multiple next task nodes, the processor determines whether to execute the next task node in the data branching manner or the data diversion manner according to the branch identifier.

5. The data flow management system according to claim 4, characterized in that: When the processor determines to execute the next task node in the data splitting manner according to the branch identifier, the processor determines to execute one of the multiple next task nodes according to multiple weight values ​​corresponding to the multiple next task nodes.

6. The data flow management system according to claim 5, characterized in that: The processor selects to execute one of the plurality of next task nodes corresponding to the highest weight value.

7. The data flow management system according to claim 1, characterized in that: The data state of the output data includes a data state code, a data state name, and data characteristics.

8. The data flow management system according to claim 1, characterized in that: Each data feature in the data feature set includes recording data feature code, feature name information, feature matching method information and feature matching rule information.

9. The data flow management system according to claim 8, characterized in that: When the feature matching rule information is configured to take values ​​from process variables, the processor executes the next task node in the data branching manner.

10. The data flow management system according to claim 8, characterized in that: The feature matching rule information is an expression or a request address of an external application port.

11. A data flow management method, characterized in that: include: Execute data flow through processors; Executing, by the processor, task nodes in the data flow and generating output data; Determining, by the processor, whether the data state of the output data reaches the target data state, so as to determine whether to obtain the next task node in the data flow; When the processor determines that the data state of the output data has not reached the target data state, the processor acquires the next task node, and the processor determines whether to execute the next task node in a data branching manner or a data diversion manner according to a data feature set and a branch identifier; as well as When the processor determines that the data state of the output data reaches the target data state, the data flow ends.

12. The data flow management method according to claim 11, characterized in that: The processor uses the output data as input data of the next task node.

13. The data flow management method according to claim 11, characterized in that: The step of obtaining the next task node in the data flow includes: acquiring the data state of the output data through the processor, and matching the data flow; and The processor queries the next task node in the data flow according to the data state, the data feature set and the branch identifier.

14. The data flow management method according to claim 13, characterized in that: The step of obtaining the next task node in the data flow also includes: When the processor determines that there are multiple next task nodes, the processor determines whether to execute the next task node in the data branching manner or the data diversion manner according to the branch identifier.

15. The data flow management method according to claim 14, characterized in that: The step of obtaining the next task node in the data flow also includes: When the processor determines to execute the next task node in the data splitting manner according to the branch identifier, the processor determines to execute one of the multiple next task nodes according to multiple weight values ​​corresponding to the multiple next task nodes.

16. The data flow management method according to claim 15, characterized in that: The processor selects to execute one of the plurality of next task nodes corresponding to the highest weight value.

17. The data flow management method according to claim 11, characterized in that: The data state of the output data includes a data state code, a data state name, and data characteristics.

18. The data flow management method according to claim 11, characterized in that: Each data feature in the data feature set includes recording data feature code, feature name information, feature matching method information and feature matching rule information.

19. The data flow management method according to claim 18, characterized in that: When the feature matching rule information is configured to take values ​​from process variables, the processor executes the next task node in the data branching manner.

20. The data flow management method according to claim 18, characterized in that: The feature matching rule information is an expression or a request address of an external application port.

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