A data processing system for obtaining target task data sets

Abstract

The present invention provides a data processing system for obtaining a target task data set, comprising: a target question statement, an initial task data set, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining a first task data set based on the initial task data set; obtaining a first keyword list based on the target question statement; obtaining a second task data set based on the first task data set; and obtaining a target task data set based on the second task data set. It can be seen that the present invention performs data governance on the initial task data set, obtains the first task data set, and obtains the target task data set from the first task data set, thereby reducing the amount of data. In the process of obtaining the target task data set, the system expands the keywords and obtains the target task data set based on the keywords and words related to the keywords, thereby improving the accuracy of obtaining the target task data set.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a data processing system for acquiring a target task data set. Background Art

[0002] With the rapid development of computer technology, there are thousands of task data lists for each task. Users cannot select the required target task data list from thousands of task data lists. Therefore, it is very necessary to intelligently obtain the target task data list. Most of the existing methods for obtaining the target task data list are to input the data retrieval statement into the information extraction model, obtain the entities and entity relationships, and entity types in the retrieval statement, and search in the database according to the entities and entity types; perform knowledge graph retrieval in the database according to the entities and entity relationships to obtain the target task data list.

[0003] However, the above method also has the following technical problems:

[0004] In the process of searching in the database based on entities, entity types and entity relationships, the original database is not simply managed. The retrieval process requires searching all task data lists in the database. The amount of data is large and the entities are not expanded in the process of obtaining the target task data list. The obtained target task data list has limitations and is incomplete, which reduces the accuracy of obtaining the target task data set. Summary of the Invention

[0005] In view of the above technical problems, the technical solution adopted by the present invention is:

[0006] A data processing system for obtaining a target task data set includes: a target problem statement, an initial task data set, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented:

[0007] S100: Acquire a first task dataset corresponding to a target question statement based on an initial task dataset.

[0008] S200, according to the target question statement, obtain the first keyword list A corresponding to the target question statement = {A1, ..., A i ,……,A m}, A i is the i-th first keyword corresponding to the target question statement, i=1...m, m is the number of first keywords, and the target question statement is the question statement input by the user.

[0009] S300, according to the first task data set, obtain the second task data set B corresponding to A = {B1, ..., B i,……,B m}, B i A i The corresponding second task data set; wherein, the step S300 includes the following steps to obtain B i :

[0010] S301: Input A into the third preset natural language model, and obtain the SQL string list ZB corresponding to A = {ZB1, ..., ZB i ,……,ZB m}, ZB i A i The corresponding SQL string.

[0011] S303, when i=1, according to ZB i , get B from the first task dataset i .

[0012] S305, when i≠1, according to ZB i , from B (i-1) Get B i .

[0013] S400, according to B m , obtaining a target task data set; wherein, step S400 includes the following steps:

[0014] S401, obtain B m ={B m1 ,……,B mg ,……,B mh}, B mg For B m The gth second task data list in B, g = 1...h, h is m The number of second task data lists in .

[0015] S403, according to B m , get B m Corresponding saturation priority list C m ={C m1 ,……,C mg ,……,C mh}, C mg For B mg The corresponding saturation priority, where C mg Meet the following conditions:

[0016] C mg =1-C 0 mg / C 1 mg , C 0 mgThe data item in the second task data list is NULL, C 1 mg All data items in the second task data list.

[0017] S405, when C mg ≥C, change B mg Insert into the first key task dataset B 1 m Middle, B 1 m ={B 1 m1 ,……,B 1 mx ,……,B 1 mp}, B 1 mx is the xth first key task data list in the first key task data set, x=1...p, p is the number of first key task data lists in the first key task data set, wherein C is the preset saturation priority threshold.

[0018] S407, when p≥p 0 When B 1 m Send to the user to determine the second key task data set, where p 0 The number of preset first key task data lists.

[0019] S409, when p<p 0 When , according to A, determine the second key task data set.

[0020] The present invention has at least the following beneficial effects:

[0021] The present invention provides a data processing system for obtaining a target task data set, comprising: a target question statement, an initial task data set, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining a first task data set based on the initial task data set; obtaining a first keyword list based on the target question statement; obtaining a second task data set based on the first task data set; and obtaining a target task data set based on the second task data set. It can be seen that the present invention performs data governance on the initial task data set, obtains the first task data set, and obtains the target task data set from the first task data set, thereby reducing the amount of data. In the process of obtaining the target task data set, the system expands the keywords and obtains the target task data set based on the keywords and words related to the keywords, thereby improving the accuracy of obtaining the target task data set. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of a data processing system executing a computer program for obtaining a target task data set provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] The present invention provides a data processing system for obtaining a target task data set, comprising: a target problem statement, an initial task data set, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: Figure 1 As shown:

[0027] S100: Acquire a first task dataset corresponding to a target question statement based on an initial task dataset.

[0028] Specifically, the initial task data set includes several initial task data lists, wherein the initial task data list is a data list of tasks to be processed specified by the user, for example, the task to be processed is a cargo query task.

[0029] Furthermore, each initial task data list includes a number of field names, and each field name corresponds to a number of data items.

[0030] Specifically, in step S100, the first task data set is determined by the following steps:

[0031] S101, obtain the initial task data set D = {D1, ..., D e ,……,D f}, D e is the e-th initial task data list in the initial task data set, e=1…f, and f is the number of initial task data lists in the initial task data set.

[0032] S103: Perform data management on D to obtain the first intermediate task dataset D corresponding to D. 0 ={D 0 1, ..., D 0 e ,……,D 0 f}, D 0 e D e The corresponding first intermediate task data list, wherein those skilled in the art know that any data governance method in the prior art falls within the scope of protection of the present invention and will not be described in detail here. It can be understood as: standardizing the field names in all initial task data lists and labeling the initial task data lists. For example, all the field names representing the identity card numbers in the initial data lists are uniformly modified to "identity card ID". If the data in the initial task data list records the data of vehicles transporting goods, the initial task data list is labeled "transportation".

[0033] S105, according to D 0 , get D 0 The corresponding first tag list E={E1, ……, E e ,……,E f}, E e D 0 e The corresponding first tag, wherein the first tag is a tag obtained during the data governance process.

[0034] S107: Input the target question sentence into the first preset natural language model to obtain the second label E corresponding to the target question sentence. 0 , wherein the first preset natural language model is a natural language model for obtaining labels. Those skilled in the art know that any natural language model in the prior art that can obtain labels falls within the protection scope of the present invention and will not be repeated here.

[0035] Specifically, the second label can be understood as the type of the target question sentence, such as: transportation, electricity.

[0036] As described above, through the first label corresponding to the initial task data list and the second label corresponding to the question statement, the word vectors corresponding to the first label and the second label can be obtained. Furthermore, the label similarity between the first label and the second label can be obtained, which is conducive to obtaining a more accurate first task data set.

[0037] S109, according to E e and E 0 , obtain the first task dataset.

[0038] Specifically, step S109 includes the following steps:

[0039] S1091, E e Input into the second preset natural language model to obtain E e The corresponding first word vector list F e ={F e1 ,……,F er ,……,F es}, F er For E e The corresponding r-th first word vector, r=1...s, s is the number of first word vectors, which can be understood as: the first word vector is the word vector generated by the first label, wherein the second preset natural language model is a natural language model for obtaining word vectors. Those skilled in the art know that any natural language model in the prior art that can obtain word vectors falls within the scope of protection of the present invention and will not be repeated here, for example: word2vec model.

[0040] S1093: Input E0 into the second preset natural language model to obtain E 0 The corresponding second word vector list F 0 ={F 0 1, ..., F 0 r ,……,F 0 s}, F 0 r For E 0 The corresponding r-th second word vector can be understood as: the second word vector is the word vector generated by the second label.

[0041] S1095, according to F er and F 0 r , get E e With E 0 The label similarity G 0 e , where G 0 e Meet the following conditions:

[0042]

[0043] S1097, when G 0 e ≥G 1 When E e Corresponding D 0 e Inserted into the first task dataset, where G 1 is the preset tag similarity threshold.

[0044] Specifically, G 1 The value range is [0.8, 1]. Preferably, G 1 =0.9, to prevent the preset tag similarity threshold from being set too high or too low, thereby resulting in obtaining an erroneous first task dataset.

[0045] In the above, the label similarity of the first label and the second label is obtained through the word vectors of the first label and the second label, so that the first task data set can be accurately obtained, and the target task data set can be obtained in the first task data set, which reduces the amount of data and is beneficial to improving the accuracy of obtaining the target task data set.

[0046] S200, according to the target question statement, obtain the first keyword list A corresponding to the target question statement = {A1, ..., A i ,……,A m}, A i is the i-th first keyword corresponding to the target question statement, i=1...m, m is the number of first keywords, the target question statement is the question statement input by the user, wherein the first keyword is the keyword in the target question statement. Those skilled in the art know that any method of obtaining keywords from question statements in the prior art falls within the scope of protection of the present invention and will not be repeated here.

[0047] S300, according to the first task data set, obtain the second task data set B corresponding to A = {B1, ..., B i ,……,B m}, B i A i The corresponding second task dataset.

[0048] Specifically, the second task data set includes several second task data lists.

[0049] Specifically, B i The number of second task data lists in B is not greater than (i -1) in the second task data list.

[0050] Specifically, step S300 includes the following steps:

[0051] S301: Input A into the third preset natural language model, and obtain the SQL string list ZB corresponding to A = {ZB1, ..., ZB i ,……,ZB m}, ZB i A i The corresponding SQL string, wherein in some embodiments the SQL string can be replaced by other DSLs, and the technical solution of the present invention can also be implemented. The third preset natural language model is a natural language model for obtaining the SQL string. Those skilled in the art know that any natural language model in the prior art that can obtain the SQL string falls within the scope of protection of the present invention and will not be repeated here.

[0052] Specifically, any A i The corresponding SQL string is the SQL string of the query class.

[0053] S303, when i=1, according to ZB i , get B from the first task dataset i Those skilled in the art know that any method in the prior art for obtaining a data set corresponding to an SQL string from a data set based on an SQL string falls within the scope of protection of the present invention and will not be described in detail here.

[0054] S305, when i≠1, according to ZB i , from B (i-1) Get B i . It can be understood as: from B (i-1) Get ZB i The corresponding data set is known to those skilled in the art. i , from B (i-1) Get B i Method, refer to step S303 according to ZB i , get B from the first task dataset i The method will not be described here.

[0055] As described above, in the order of keywords, the second task data set corresponding to the current first keyword is obtained from the second task data set corresponding to the previous first keyword in sequence until the second task data set corresponding to the last first keyword is obtained. The second task data set corresponding to the last first keyword is processed to obtain the target task data set, which is conducive to improving the accuracy of obtaining the target task data set.

[0056] S400, according to B m , obtain the target task dataset.

[0057] Specifically, the target task dataset includes a first key task dataset and a second key task dataset.

[0058] Specifically, step S400 includes the following steps:

[0059] S401, obtain B m ={B m1 ,……,B mg ,……,B mh}, B mg For B m The gth second task data list in B, g = 1...h, h is m The number of second task data lists in .

[0060] S403, according to B m , get B m Corresponding saturation priority list C m ={C m1 ,……,C mg ,……,C mh}, C mg For B mg The corresponding saturation priority, where C mg Meet the following conditions:

[0061] C mg =1-C 0 mg / C 1 mg , C 0 mg The data item in the second task data list is NULL, C 1 mg These are all the data items in the second task data list. Those skilled in the art know that any method in the prior art for obtaining the number of NULL data items and the number of all data items in a data list falls within the scope of protection of the present invention and will not be described in detail here.

[0062] S405, when C mg ≥C, change B mg Insert into the first key task dataset B 1 m Middle, B 1 m ={B 1 m1 ,……,B 1 mx ,……,B 1 mp}, B 1 mxis the xth first key task data list in the first key task data set, x=1...p, p is the number of first key task data lists in the first key task data set, wherein C is the preset saturation priority threshold.

[0063] Specifically, the value range of C is [0.6, 1]. Those skilled in the art can set the preset saturation priority threshold according to actual needs.

[0064] S407, when p≥p 0 When B 1 m Send to the user to determine the second key task data set, where p 0 To preset the number of first key task data lists, those skilled in the art may set the number of first key task data lists according to actual needs.

[0065] Specifically, step S407 includes the following steps:

[0066] S4071, obtain the user received B 1 m The first target identifier after feedback.

[0067] S4073: When the first target identifier is “0”, determine that the second key task data set is NULL.

[0068] Specifically, the mark "0" represents: "The target task data set is confirmed to be correct."

[0069] S4075. When the first target identifier is "1", update p 0 =p 0 +Δp, and execute step S407, where Δp is the number of preset designated task data lists, and Δp meets the following conditions:

[0070] Δp=1 / 2×p 0 .

[0071] Specifically, the mark "1" represents: "The target task data set is incomplete and needs to continue searching."

[0072] As described above, determining the second key task dataset through the first target identifier fed back by the user can improve the accuracy of obtaining the second key task dataset, reduce resource waste, and thus help improve the accuracy of obtaining the target task dataset.

[0073] S409, when p<p 0 When , according to A, determine the second key task data set.

[0074] As described above, judging the number of first key task data lists in the first key task data set and selecting a corresponding processing method can save resources and further improve the accuracy of obtaining the target task data set.

[0075] Specifically, step S409 includes the following steps:

[0076] S4091. Obtain the second keyword list H corresponding to A = {H1, ..., H i ,……,H m}, H i ={H i1 ,……,H iy ,……,H yq(i)} and H iy The corresponding first preset relevance weight list L iy ={L 1 iy ,……,L i iy ,……,L m iy}, H iy A i The corresponding y-th second keyword list, y=1...p(i), p(i) is A i The number of corresponding second keywords, L i iy H iy With A i A first preset correlation weight between them, wherein those skilled in the art know that those skilled in the art set the first preset correlation weight according to actual needs.

[0077] S4093, Get H iy The corresponding second preset relevance weight list P iy ={P 1 iy ,……,P i iy ,……,P m iy}, P i iy H iy With A i The second preset correlation weight between them is known to those skilled in the art. Those skilled in the art will set the second preset correlation weight according to actual needs.

[0078] S4095, according to L iy and P iy , get the relevance priority list Q corresponding to H i ={Q i1 ,……,Q iy,……,Q iq(i)}, Q iy H iy The corresponding relevance priority, where Q iy Meet the following conditions:

[0079] Q iy =(∑ m a＝1 (L a iy ×P a iy )) / m.

[0080] S4097, A in A i Replace with the largest value that is not less than Q 0 Q iy The corresponding H iy , construct the third keyword list, where Q 0 is the preset relevance priority threshold, which can be understood as the third keyword list DS = {A1, ..., H iy ,……,A m}.

[0081] Specifically, Q 0 The value range of is [0.6, 1]. Those skilled in the art can set the relevance priority threshold according to actual needs.

[0082] S4099. Obtain a second key task data set according to the third keyword list.

[0083] As described above, in the process of obtaining the target task data set, the keywords are expanded, and the target task data set is obtained based on the keywords and words related to the keywords, which further improves the accuracy of obtaining the target task data set.

[0084] Specifically, step S4099 includes the following steps:

[0085] S1. Obtain a second intermediate task data set according to the third keyword list. Those skilled in the art know that the method for obtaining the second intermediate task data set according to the third keyword list refers to the method for obtaining the first key task data set according to the first keyword list, and will not be repeated here.

[0086] S2. Insert the second intermediate task list in the second intermediate task data set into the designated task data set in sequence.

[0087] S3. Obtain the number b of the specified task data lists in the specified task data set.

[0088] S4, when p+b≥p 0When the first key task data set and the specified task data set are sent to the user.

[0089] S5. Obtain a second target identifier fed back by the user after receiving the first key task dataset and the designated task dataset.

[0090] S6. When the second target identifier is “0”, determine that the designated task dataset is a second key task dataset.

[0091] S7. When the second target identifier is "1", according to p 0 Identify the second most critical mission data set.

[0092] Specifically, step S7 includes the following steps:

[0093] S71, update p 0 =p 0 +Δp.

[0094] S73, Update Q i , the maximum Q iy From Q i Delete and execute step S4097.

[0095] S8. When the second target identifier is "2", according to Q i Identify the second most critical mission data set.

[0096] Specifically, the label "2" represents: "Too many target task data sets."

[0097] Specifically, step S8 includes the following steps:

[0098] S81. Set the specified task dataset to NULL.

[0099] S83, Update Q i , the maximum Q iy From Q i Delete and execute step S4097.

[0100] In the above, the first keyword is expanded to obtain the second keyword and the relevance priority corresponding to the second keyword. By analyzing the relevance priority, the second intermediate task data set is obtained, and the second intermediate task data list in the second intermediate task data set is inserted into the specified task data set according to the relevance of the corresponding keyword to obtain the second key task data set, and further obtain the target task data set, which is conducive to improving the accuracy of obtaining the target task data set.

[0101] The present invention provides a data processing system for obtaining a target task data set, comprising: a target question statement, an initial task data set, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining a first task data set based on the initial task data set; obtaining a first keyword list based on the target question statement; obtaining a second task data set based on the first task data set; and obtaining a target task data set based on the second task data set. It can be seen that the present invention performs data governance on the initial task data set, obtains the first task data set, and obtains the target task data set from the first task data set, thereby reducing the amount of data. In the process of obtaining the target task data set, the system expands the keywords and obtains the target task data set based on the keywords and words related to the keywords, thereby improving the accuracy of obtaining the target task data set.

[0102] Although some specific embodiments of the present invention have been described in detail by way of example, it will be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A data processing system for obtaining a target task data set, characterized in that: The system includes: a target problem statement, an initial task data set, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: S100: Obtain a first task dataset corresponding to a target question statement based on an initial task dataset; S200, according to the target question statement, obtain the first keyword list A corresponding to the target question statement = {A1, ..., A i ,……,A m }, A i is the i-th first keyword corresponding to the target question sentence, i=1…m, m is the number of first keywords, and the target question sentence is the question sentence input by the user; S300, according to the first task data set, obtain the second task data set B corresponding to A = {B1, ..., B i ,……,B m }, B i A i The corresponding second task data set; wherein, the step S300 includes the following steps to obtain B i : S301: Input A into the third preset natural language model, and obtain the SQL string list ZB corresponding to A = {ZB1, ..., ZB i ,……,ZB m }, ZB i A i The corresponding SQL string; S303, when i=1, according to ZB i , get B from the first task dataset i ; S305, when i≠1, according to ZB i , from B (i-1) Get B i ; S400, according to B m , obtaining a target task data set; wherein, step S400 includes the following steps: S401, obtain B m ={B m1 ,……,B mg ,……,B mh }, B mg For B m The gth second task data list in B, g = 1...h, h is m The number of second task data lists in ; S403, according to B m , get B m Corresponding saturation priority list C m ={C m1 ,……,C mg ,……,C mh }, C mg For B mg The corresponding saturation priority, where C mg Meet the following conditions: C mg =1-C 0 mg / C 1 mg , C 0 mg The data item in the second task data list is NULL, C 1 mg All data items in the second task data list; S405, when C mg ≥C, change B mg Insert into the first key task dataset B 1 m Middle, B 1 m ={B 1 m1 ,……,B 1 mx ,……,B 1 mp }, B 1 mx is the xth first key task data list in the first key task data set, x=1...p, p is the number of first key task data lists in the first key task data set, wherein C is the preset saturation priority threshold; S407, when p≥p 0 When B 1 m Send to the user to determine the second key task data set, where p 0 To preset the number of first key task data lists; S409, when p<p 0 When , according to A, determine the second key task data set.

2. The data processing system for obtaining a target task data set according to claim 1, characterized in that: In step S100, the first task data set is determined by the following steps: S101, obtain the initial task data set D = {D1, ..., D e ,……,D f }, D e is the e-th initial task data list in the initial task data set, e=1…f, f is the number of initial task data lists in the initial task data set; S103: Perform data management on D to obtain the first intermediate task dataset D corresponding to D. 0 ={D 0 1, ..., D 0 e ,……,D 0 f }, D 0 e D e Corresponding first intermediate task data list; S105, according to D 0 , get D 0 The corresponding first tag list E={E1, ……, E e ,……,E f }, E e D 0 e The corresponding first tag, where the first tag is a tag obtained during the data governance process; S107: Input the target question sentence into the first preset natural language model to obtain the second label E corresponding to the target question sentence. 0 , wherein the first preset natural language model is a natural language model used to obtain labels; S109, according to E e and E 0 , obtain the first task dataset.

3. The data processing system for obtaining a target task data set according to claim 1, characterized in that: The target task data set includes a first key task data set and a second key task data set.

4. The data processing system for obtaining a target task data set according to claim 1, characterized in that: Step S407 includes the following steps: S4071, obtain the user received B 1 m First target identification of post-feedback; S4073: When the first target identifier is "0", determine that the second key task data set is NULL; S4075: When the first target identifier is "1", update p 0 =p 0 +Δp, and execute step S407, where Δp is the number of preset designated task data lists, and Δp meets the following conditions: Δp=1 / 2×p 0 。 5. The data processing system for obtaining a target task data set according to claim 1, characterized in that: Step S409 includes the following steps: S4091. Obtain the second keyword list H corresponding to A = {H1, ..., H i ,……,H m }, H i ={H i1 ,……,H iy ,……,H yq(i) } and H iy The corresponding first preset relevance weight list L iy ={L 1 iy ,……,L i iy ,……,L m iy }, H iy A i The corresponding y-th second keyword list, y=1...p(i), p(i) is A i The number of corresponding second keywords, L i iy H iy With A i A first preset correlation weight between ; S4093, Get H iy The corresponding second preset relevance weight list P iy ={P 1 iy ,……,P i iy ,……,P m iy }, P i iy H iy With A i A second preset correlation weight between ; S4095, according to L iy and P iy , get the relevance priority list Q corresponding to H i ={Q i1 ,……,Q iy ,……,Q iq(i) }, Q iy H iy The corresponding relevance priority, where Q iy Meet the following conditions: Q iy =(∑ m a＝1 (L a iy ×P a iy )) / m; S4097, A in A i Replace with the largest value that is not less than Q 0 Q iy The corresponding H iy , construct the third keyword list, where Q 0 is the preset relevance priority threshold; S4099. Obtain a second key task data set according to the third keyword list.

6. The data processing system for obtaining a target task data set according to claim 5, characterized in that: The S4099 step includes the following steps: S1. Obtain a second intermediate task dataset according to the third keyword list; S2. inserting the second intermediate task list in the second intermediate task data set into the designated task data set in sequence; S3. Obtain the number b of the specified task data list in the specified task data set; S4, when p+b≥p 0 When the first key task data set and the specified task data set are sent to the user; S5. Obtaining a second target identifier fed back by the user after receiving the first key task dataset and the designated task dataset; S6. When the second target identifier is "0", determining that the designated task dataset is a second key task dataset; S7, when the second target identifier is identifier "1", according to p 0 Identify the second mission-critical dataset; S8. When the second target identifier is "2", according to Q i Identify the second most critical mission data set.

7. The data processing system for obtaining a target task data set according to claim 6, characterized in that: The S7 step includes the following steps: S71, update p 0 =p 0 +Δp; S73, Update Q i , the maximum Q iy From Q i Delete and execute step S4097.

8. The data processing system for obtaining a target task data set according to claim 6, characterized in that: The S8 step includes the following steps: S81. Set the specified task data set to NULL; S83, Update Q i , the maximum Q iy From Q i Delete and execute step S4097.

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