Insurance task theme data association analysis method

Abstract

The invention provides a guarantee task theme data association analysis method. The method comprises the following steps: S1, establishing a task theme data set; s2, establishing a theme data item set combination; s3, generating a topic data frequent item set; s4, mining subject data association rules; and S5, forming a subject data association report. According to the association analysis method for the topic data of the guarantee task, the scheme is clear and intuitive, and extraction, fusion and analysis can be carried out in combination with related business data of the guarantee task to form a topic data set; the item set combination support degree and the association rule confidence degree of the subject data are used as important judgment bases of data attribute relations and data association rules, main data attribute relations meeting guarantee requirements are classified, sorted and optimized, and unnecessary data attribute relations are screened out. The data quality and the application value of task theme data are improved and guaranteed, the threshold setting and research and judgment effects of human factors are considered, and the method has a considerable degree of flexibility.

Description

Technical Field

[0001] The present invention relates to the field of security mission data, and in particular to a security mission subject data association analysis method. Background Art

[0002] The data assurance needs of the security mission not only need to clarify the business scope, source method, data format, and data scale of the data, but also need to clarify the data subject, data flow, and data relationship under the security mission conditions; the data composition structure and data relationship of the external data that is usually accessed mainly follow the application requirements of the business system to which the external data belongs, and there are certain differences between the data composition requirements, data business requirements, and data relationship requirements of the security mission. According to the security mission requirements, it is necessary to sort out the access data, classify the subject data, and analyze the data association according to the security mission requirements, clarify the data classification composition and data association relationship under the security mission conditions, and form special data that meets the security mission requirements to support the comprehensive display, product production, and service application of the security mission data. Based on data statistics and data clustering, the association analysis of the security mission subject data introduces data frequent item set calculation and association rule mining methods to conduct association analysis of the security mission subject data, establish association rules and association maps for the subject data, and form a subject data association analysis report. The subject data of the security mission is expressed in tables and graphics to visualize the flow process of the security mission data, providing technical support for data product customization and service application.

[0003] At present, the technical solution closest to the present invention is provided by Chinese patent "CN117172489A A task allocation method, device and medium based on association analysis", which belongs to the field of data processing technology. The method obtains historical task information from a task record database; the historical task information at least includes a number of historical execution tasks and a corresponding execution attribute set of each historical execution task, each historical execution task corresponds to at least one execution object, and based on the historical task information, the conditional pattern base of each corresponding execution attribute is determined to generate a conditional frequent pattern FP tree of each execution attribute and its corresponding historical frequent item set, and the task matching information corresponding to the reallocated task is obtained, wherein the task matching information at least includes an execution attribute set for executing the reallocated task, and based on the matching results of the task matching information and each historical frequent item set, the pending execution object corresponding to the task matching information is determined to generate task reallocation information corresponding to the pending execution object, and the task reallocation information is sent to the corresponding user terminal. The main steps are as follows:

[0004] Step 1: Import historical task information;

[0005] Step 2: Based on the historical task information, determine the conditional pattern base of each execution attribute to generate the conditional frequent pattern FP tree of each execution attribute and its corresponding historical frequent item set;

[0006] Step 3: Obtain task matching information corresponding to the reallocated task;

[0007] Step 4: Based on the matching results between the task matching information and each historical frequent item set, determine the pending execution object corresponding to the task matching information to generate task reallocation information corresponding to the pending execution object, and send the task reallocation information to the corresponding user terminal.

[0008] The existing technologies mainly include Apriori algorithm, FP-tree algorithm, SETM algorithm, Partition algorithm and the like.

[0009] The Apriori algorithm is a breadth-first algorithm based on horizontal data distribution. It mines frequent item sets of Boolean association rules. It is a recursive algorithm for mining frequent item sets in two steps. Its main idea is to find the largest frequent item set in the transaction data set, and then use the obtained largest frequent item set and the preset minimum confidence to generate strong association rules; the FP-tree algorithm obtains the frequent item set and support after the first scan, arranges them in descending order, creates the root node and frequent item table, and adds transactions in sequence to build a tree structure; the SETM algorithm uses SQL statements to calculate frequent sets and generate candidate sets. The confidence of the association rules increases linearly with the increase of the database, and is not suitable for multiple databases; the Partition algorithm divides the entire data set into data blocks that can be stored in memory for processing to save I / O overhead, and is suitable for parallel association.

[0010] The purpose of the present invention is to overcome the shortcomings of the prior art that the data traversal calculation time is long and the data association rules are fuzzy.

[0011] Therefore, it is necessary to provide a task-assurance subject data association analysis method to solve the above technical problems. Summary of the invention

[0012] The present invention provides a method for analyzing association of task subject data, which solves the problems of long data traversal calculation time and fuzzy data association rules in the prior art.

[0013] In order to solve the above technical problems, the present invention provides a method for analyzing the association of task subject data, comprising the following steps:

[0014] S1. Establishing a task subject data set;

[0015] S11. According to the technical requirements of the task-related data association analysis, access the task-related business data as input data for the data association analysis;

[0016] S12. Extract entities, attributes, and relationship-related data items from business data, clarify the main objects and main features of business data, take human resources, equipment, materials, facilities, etc. as themes, perform statistical analysis on business data feature attributes, classify and grade entity objects of business data, align entity granularity, and integrate data feature attributes and feature relationships;

[0017] S13. Taking human resources, equipment, materials and facilities as the themes, clarify the entity objects and attribute relationships within the subject data, form a collection of support task subject data, and preliminarily establish the ontology relationship of the subject data;

[0018] S2, establish the combination of subject data item sets;

[0019] S21. According to the data ontology relationship in the task support subject data set, select the main attributes of the data, combine the data attribute relationships, establish the item set combination of the subject data, and describe the possible attribute combinations of the subject data;

[0020] S3, generate frequent itemsets of topic data;

[0021] S31. Perform support analysis on the item set combination of the task subject data. The support represents the probability of occurrence of an item set combination of a certain attribute relationship in the total item set. The support formula is as follows:

[0022] Sp(x,y)=P(x,y)=Num(x,y) / Num(I)

[0023] In the formula, Sp(x,y) represents the support of the relationship item set combination between attributes x and y;

[0024] P(x,y) represents the probability of occurrence of the relationship item set combination between x and y;

[0025] Num(x,y) represents the frequency of occurrence of the relationship item set combination between x and y;

[0026] Num(I) represents the total number of attribute combination relationships of frequent item sets in topic data;

[0027] S32, traverse and calculate the support of all attribute relationship combinations in the topic data item set combination (i.e., the occurrence probability of the relationship combination), set the item set support threshold (value range 0 to 1, percentage), filter out attribute combinations with low occurrence probability, and generate frequent item sets of the topic data;

[0028] S4, mining association rules of topic data;

[0029] From the frequent item set of the topic data, list all possible association rules. Association rules refer to expressions that describe the association relationship between attributes. Calculate the confidence of each association rule. The confidence represents the confidence probability of the association rule when the prerequisite occurs. The confidence formula of the association rule is as follows:

[0030] Con(x,y)=P(x / y)=P(x,y) / P(y);

[0031] In the formula, Con(x,y) represents the confidence of the association rule between attributes x and y;

[0032] P(x / y) represents the probability of containing attribute x in the frequent item set containing attribute y;

[0033] P(x,y) represents the probability of occurrence of the relationship item set combination between attributes x and y;

[0034] P(y) represents the occurrence probability of a combination of relation itemsets containing attribute y.

[0035] Set the confidence threshold of the association rule (value range 0-1, percentage), remove all low-confidence association rules, and select high-confidence association rules as the association rules of the subject data;

[0036] S5. Form a subject data association report;

[0037] S51. Based on the association rules formed by mining, a relationship topology diagram of the attribute characteristics of the subject data is constructed to intuitively express the association relationship between the main attributes of the subject data;

[0038] S52. Based on the subject data association rules, conduct a special analysis on the association relationship of the subject data to form a task support subject data association analysis report, which mainly includes: comparative relationship analysis, abstract relationship analysis, temporal relationship analysis, and progressive relationship analysis.

[0039] Preferably, the formation of subject data association in S5 mainly includes: classification hierarchy relationship, data inclusion relationship, data dependency relationship, and data interaction relationship.

[0040] Preferably, the specific analysis content in S52 is as follows:

[0041] S521. Comparative relationship analysis: Analyze certain differences between data through data comparison;

[0042] S522. Abstract relationship analysis: Analyze the data combination relationship, summarize and express the abstract relationship of the data;

[0043] S523, Time series relationship analysis: the time sequence of data generation, highlighting the time sequence between data;

[0044] S524. Progressive relationship analysis: the continuity between data objects, data generation, management and maintenance objects.

[0045] Preferably, the establishment of the task subject data set in S1 will use a data storage device, and the data storage device includes a server body, and heat dissipation nets are provided on both sides of the left and right sides of the surface of the server body, and pushing devices are provided on the left and right sides of the server body, and the pushing device includes a double-headed hydraulic telescopic rod, and both ends of the double-headed hydraulic telescopic rod are connected to fixed blocks, one side of the two fixed blocks is connected to a pushing frame, a dust suction component is provided between the two pushing frames, and a suction device is provided at the bottom of the server body.

[0046] Preferably, a fixing frame is provided at the center position of the surface of the double-headed hydraulic telescopic rod, and the fixing frame is connected to the surface of the server body.

[0047] Preferably, the dust suction assembly comprises a suction pipe, both ends of the suction pipe are connected to dust suction hoods, and one side of the two dust suction hoods is connected to a suction head.

[0048] Preferably, the suction device comprises a delivery pump, an input end and an output end of the delivery pump are both connected to delivery pipes, and one end of two delivery pipes are respectively connected to a first connecting pipe and a second connecting pipe.

[0049] Preferably, box bodies are installed on both sides of the bottom of the server body, and a collection box is arranged inside the box bodies.

[0050] Preferably, a protection component is provided on the surface of the server body, and the protection component includes a plurality of mounting seats, a plurality of mounting seats are each provided with a mounting block inside, and a plurality of mounting blocks are each connected to a circular rod on the surface.

[0051] Preferably, protective covers are connected between the plurality of circular rods.

[0052] Compared with the related art, the method for analyzing the association of task subject data provided by the present invention has the following beneficial effects:

[0053] The present invention provides a method for analyzing the association of security mission subject data. The scheme is clear and intuitive, and can be combined with relevant business data of the security mission to extract, fuse and analyze to form a subject data set. The item set combination support and association rule confidence of the subject data are used as important judgment bases for data attribute relationships and data association rules. The main data attribute relationships that meet the security needs are classified, sorted out and optimized, and unnecessary data attribute relationships are screened out, thereby improving the data quality and application value of the security mission subject data. The threshold setting and judgment role of human factors are taken into consideration, and the method has a considerable degree of flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of the structure of a first embodiment of a method for analyzing association of task subject data provided by the present invention;

[0055] Figure 2 Construct a process diagram for the task subject data set;

[0056] Figure 3 Schematic diagram of the process of generating frequent itemsets for topic data;

[0057] Figure 4 This is a flowchart of topic data association rule mining process;

[0058] Figure 5 To ensure the task subject data association analysis algorithm flow chart;

[0059] Figure 6 A schematic diagram of the structure of a second embodiment of a method for analyzing association of task subject data provided by the present invention;

[0060] Figure 7 for Figure 6 An enlarged schematic diagram of part A is shown;

[0061] Figure 8 for Figure 6 A three-dimensional schematic diagram of the bottom of the device as a whole is shown;

[0062] Fig. 9 for Figure 8 An enlarged schematic diagram of part B is shown;

[0063] Fig.10 A schematic diagram of the structure of a third embodiment of a method for analyzing association of task subject data provided by the present invention;

[0064] Fig.11 for Fig.10 An enlarged schematic diagram of part C is shown.

[0065] Numbers in the figure: 1. Server body; 2. Heat dissipation network;

[0066] 3. Pushing device; 31. Double-head hydraulic telescopic rod; 32. Fixed block; 33. Pushing frame; 34. Fixed frame;

[0067] 4. Dust collection assembly; 41. Suction pipe; 42. Dust collection hood; 43. Suction head;

[0068] 5. Suction device; 51. Delivery pump; 52. Delivery pipe; 53. First connecting pipe; 54. Second connecting pipe;

[0069] 6. Box body; 7. Collection box;

[0070] 8. Protective assembly; 81. Mounting seat; 82. Mounting block; 83. Round rod; 84. Protective cover. DETAILED DESCRIPTION

[0071] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0072] First embodiment

[0073] Please refer to Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 ,in, Figure 1 A schematic diagram of the structure of a first embodiment of a method for analyzing association of task subject data provided by the present invention; Figure 2 Construct a process diagram for the task subject data set; Figure 3 Schematic diagram of the process of generating frequent itemsets for topic data;

[0074] Figure 4 This is a flowchart of topic data association rule mining process; Figure 5 A method for associating and analyzing task-related subject data includes the following steps:

[0075] S1. Establishing a task subject data set;

[0076] S11. According to the technical requirements of the task-related data association analysis, access the task-related business data as input data for the data association analysis;

[0077] S12. Extract entities, attributes, and relationship-related data items from business data, clarify the main objects and main features of business data, take human resources, equipment, materials, facilities, etc. as themes, perform statistical analysis on business data feature attributes, classify and grade entity objects of business data, align entity granularity, and integrate data feature attributes and feature relationships;

[0078] S13. Taking human resources, equipment, materials and facilities as the themes, clarify the entity objects and attribute relationships within the subject data, form a collection of support task subject data, and preliminarily establish the ontology relationship of the subject data;

[0079] S2, establish the combination of subject data item sets;

[0080] S21. According to the data ontology relationship in the task support subject data set, select the main attributes of the data, combine the data attribute relationships, establish the item set combination of the subject data, and describe the possible attribute combinations of the subject data;

[0081] S3, generate frequent itemsets of topic data;

[0082] S31. Perform support analysis on the item set combination of the task subject data. The support represents the probability of occurrence of an item set combination of a certain attribute relationship in the total item set. The support formula is as follows:

[0083] Sp(x,y)=P(x,y)=Num(x,y) / Num(I)

[0084] In the formula, Sp(x,y) represents the support of the relationship item set combination between attributes x and y;

[0085] P(x,y) represents the probability of occurrence of the relationship item set combination between x and y;

[0086] Num(x,y) represents the frequency of occurrence of the relationship item set combination between x and y;

[0087] Num(I) represents the total number of attribute combination relationships of frequent item sets in topic data;

[0088] S32, traverse and calculate the support of all attribute relationship combinations in the topic data item set combination (i.e., the occurrence probability of the relationship combination), set the item set support threshold (value range 0 to 1, percentage), filter out attribute combinations with low occurrence probability, and generate frequent item sets of the topic data;

[0089] S4, mining association rules of topic data;

[0090] From the frequent item set of the topic data, list all possible association rules. Association rules refer to expressions that describe the association relationship between attributes. Calculate the confidence of each association rule. The confidence represents the confidence probability of the association rule when the prerequisite occurs. The confidence formula of the association rule is as follows:

[0091] Con(x,y)=P(x / y)=P(x,y) / P(y);

[0092] In the formula, Con(x,y) represents the confidence of the association rule between attributes x and y;

[0093] P(x / y) represents the probability of containing attribute x in the frequent item set containing attribute y;

[0094] P(x,y) represents the probability of occurrence of the relationship item set combination between attributes x and y;

[0095] P(y) represents the occurrence probability of a combination of relation itemsets containing attribute y.

[0096] Set the confidence threshold of the association rule (value range 0-1, percentage), remove all low-confidence association rules, and select high-confidence association rules as the association rules of the subject data;

[0097] S5. Form a subject data association report;

[0098] S51. Based on the association rules formed by mining, a relationship topology diagram of the attribute characteristics of the subject data is constructed to intuitively express the association relationship between the main attributes of the subject data;

[0099] S52. Based on the subject data association rules, conduct a special analysis on the association relationship of the subject data to form a task support subject data association analysis report, which mainly includes: comparative relationship analysis, abstract relationship analysis, temporal relationship analysis, and progressive relationship analysis.

[0100] The formation of subject data association in S5 mainly includes: classification hierarchy relationship, data inclusion relationship, data dependency relationship, and data interaction relationship.

[0101] The specific analysis content in S52 is as follows:

[0102] S521. Comparative relationship analysis: Analyze certain differences between data through data comparison;

[0103] S522. Abstract relationship analysis: Analyze the data combination relationship, summarize and express the abstract relationship of the data;

[0104] S523, Time series relationship analysis: the time sequence of data generation, highlighting the time sequence between data;

[0105] S524. Progressive relationship analysis: the continuity between data objects, data generation, management and maintenance objects.

[0106] Compared with the related art, the method for analyzing the association of task subject data provided by the present invention has the following beneficial effects:

[0107] The present invention provides a method for analyzing the association of security mission subject data. The scheme is clear and intuitive, and can be combined with relevant business data of the security mission to extract, fuse and analyze to form a subject data set. The item set combination support and association rule confidence of the subject data are used as important judgment bases for data attribute relationships and data association rules. The main data attribute relationships that meet the security needs are classified, sorted out and optimized, and unnecessary data attribute relationships are screened out, thereby improving the data quality and application value of the security mission subject data. The threshold setting and judgment role of human factors are taken into consideration, and the method has a considerable degree of flexibility.

[0108] Second embodiment

[0109] Please refer to Figure 6 , Figure 7 , Figure 8 and Fig. 9 Based on a method for analyzing the association of task subject data provided by the first embodiment of the present application, another method for analyzing the association of task subject data is proposed in the second embodiment of the present application. The second embodiment is only a preferred method of the first embodiment, and the implementation of the second embodiment will not affect the independent implementation of the first embodiment.

[0110] Specifically, the difference of the method for analyzing the association of task subject data provided by the second embodiment of the present application is that, in a method for analyzing the association of task subject data, the establishment of the task subject data set in S1 will use a data storage device, and the data storage device includes a server body 1, and heat dissipation nets 2 are provided on both sides of the left and right sides of the surface of the server body 1, and a pushing device 3 is provided on both sides of the left and right sides of the server body 1, and the pushing device 3 includes a double-headed hydraulic telescopic rod 31, and both ends of the double-headed hydraulic telescopic rod 31 are connected to fixed blocks 32, and one side of the two fixed blocks 32 is connected to a pushing frame 33, and a dust suction component 4 is provided between the two pushing frames 33, and a suction device 5 is provided at the bottom of the server body 1.

[0111] A fixing frame 34 is disposed at the center of the surface of the double-headed hydraulic telescopic rod 31 , and the fixing frame 34 is connected to the surface of the server body 1 .

[0112] The dust suction assembly 4 includes a suction pipe 41 , both ends of the suction pipe 41 are connected to dust suction covers 42 , and one side of the two dust suction covers 42 is connected to a suction head 43 .

[0113] The suction device 5 includes a delivery pump 51 , the input end and the output end of the delivery pump 51 are both connected to a delivery pipe 52 , and one end of the two delivery pipes 52 is respectively connected to a first connecting pipe 53 and a second connecting pipe 54 .

[0114] Please refer to Figure 7 It is known that two dust hoods 42 with suction heads 43 are attached to the surface of the side of the server body 1. The use of the double-headed hydraulic telescopic rod 31 can move the dust hoods 42 with suction heads 43 on the surface of the server body 1 through two pushing rods 32. One end of the first connecting tube 53 is connected to the suction tube 41, and the second connecting tube 54 is horizontally connected to one end of one of the delivery tubes 52. The two ends of the second connecting tube 54 are respectively connected to the two box bodies 6, and the collection box 7 is installed inside the box body 6 by plugging and unplugging.

[0115] Box bodies 6 are installed on both sides of the bottom of the server body 1 , and a collection box 7 is arranged inside the box body 6 .

[0116] The working principle of the task subject data association analysis method provided by the present invention is as follows:

[0117] During use, when cleaning the dust on the heat dissipation nets 2 on both sides of the surface of the server body 1, first start the double-headed hydraulic telescopic rod 31 to push the two pushing frames 33 to move through the two fixed blocks 32. When the two pushing frames 33 move, they drive the two dust hoods 42 with suction heads 43 to move to one side of the two heat dissipation nets 2. When the double-headed hydraulic telescopic rod 31 drives the two dust hoods 42 with suction heads 43 to move to contact the heat dissipation nets 2, stop the work of the hydraulic telescopic rod 31, and then start the conveying pump 51 to absorb the dust on the heat dissipation nets 2 through the dust hoods 42 with suction heads 43 to the inside of the suction pipe 41. After the dust is transported to the inside of the suction pipe 41, it is transported to the collection box 7 inside the box body 6 through the first connecting pipe 53, the two conveying pipes 52 and the second connecting pipe 54.

[0118] Compared with the related art, the method for analyzing the association of task subject data provided by the present invention has the following beneficial effects:

[0119] The present invention provides a method for associating and analyzing task-related subject data. Two pushing devices 3, a dust collection component 4, a suction device 5 and two box bodies 6 with collection boxes 7 are arranged on a server body 1 with multiple heat dissipation nets 2, and the two pushing devices 3, a dust collection component 4, a suction device 5 and two box bodies 6 with collection boxes 7 are used together. After the server body 1 is used for a long time, the dust adsorbed on the heat dissipation nets 2 can be cleaned and collected.

[0120] Third embodiment

[0121] Please refer to Fig.10 and Fig.11 Based on a method for analyzing the association of task subject data provided by the first embodiment of the present application, the third embodiment of the present application proposes another method for analyzing the association of task subject data. The third embodiment is only a preferred method of the first embodiment, and the implementation of the third embodiment will not affect the independent implementation of the first embodiment.

[0122] Specifically, the difference of a method for analyzing the association of task subject data provided in the third embodiment of the present application is that, in a method for analyzing the association of task subject data, a protection component 8 is provided on the surface of the server body 1, and the protection component 8 includes a plurality of mounting seats 81, and a plurality of mounting seats 81 are each provided with a mounting block 82 inside, and a circular rod 83 is connected to the surface of each of the plurality of mounting blocks 82.

[0123] A protective cover 84 is connected between the plurality of circular rods 83 .

[0124] Multiple mounting seats 81 are installed on the surface of the server body 1. The internal shape of the mounting seat 81 and the shape of the mounting block 82 are matched T-shaped. The protective cover 84 is connected to the server body 1 by sliding the mounting seat 81 and the mounting block 82.

[0125] The working principle of the task subject data association analysis method provided by the present invention is as follows:

[0126] During use, when installing the protective cover 84, first make the multiple mounting blocks 82 at the bottom of the protective cover 84 contact one side of the multiple mounting seats 81 on the surface of the server body 1. After the mounting blocks 82 contact the mounting seats 81, push the protective cover 84 to drive the mounting blocks 82 to connect with the mounting seats 81.

[0127] Compared with the related art, the method for analyzing the association of task subject data provided by the present invention has the following beneficial effects:

[0128] The present invention provides a method for analyzing the association of task subject data. A protection component 8 is arranged on the surface of a server body 1 to protect the components exposed on the outside of the server body 1 as a whole.

[0129] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for analyzing the association of task subject data, characterized in that: The following steps are involved: S1. Establishing task subject data set; S11. According to the technical requirements of the task-related data association analysis, access the task-related business data as input data for the data association analysis; S12. Extract entities, attributes, and relationship-related data items from business data, clarify the main objects and main features of business data, take human resources, equipment, materials, facilities, etc. as themes, perform statistical analysis on business data feature attributes, classify and grade entity objects of business data, align entity granularity, and integrate data feature attributes and feature relationships; S13. Taking human resources, equipment, materials and facilities as the themes, clarify the entity objects and attribute relationships within the subject data, form a collection of support task subject data, and preliminarily establish the ontology relationship of the subject data; S2, establish a combination of subject data item sets; S21. According to the data ontology relationship in the task support subject data set, select the main attributes of the data, combine the data attribute relationships, establish the item set combination of the subject data, and describe the possible attribute combinations of the subject data; S3, generate frequent itemsets of topic data; S31. Perform support analysis on the item set combination of the task subject data. The support represents the probability of occurrence of an item set combination of a certain attribute relationship in the total item set. The support formula is as follows: Sp(x,y)=P(x,y)=Num(x,y) / Num(I) In the formula, Sp(x,y) represents the support of the relationship item set combination between attributes x and y; P(x,y) represents the probability of occurrence of the relationship item set combination between x and y; Num(x,y) represents the frequency of occurrence of the relationship item set combination between x and y; Num(I) represents the total number of attribute combination relationships of frequent item sets in topic data; S32, traverse and calculate the support of all attribute relationship combinations in the topic data item set combination (i.e., the occurrence probability of the relationship combination), set the item set support threshold (value range 0 to 1, percentage), filter out attribute combinations with low occurrence probability, and generate frequent item sets of the topic data; S4, mining association rules of topic data; From the frequent item set of the topic data, list all possible association rules. Association rules refer to expressions that describe the association relationship between attributes. Calculate the confidence of each association rule. The confidence represents the confidence probability of the association rule when the prerequisite occurs. The confidence formula of the association rule is as follows: Con(x,y)=P(x / y)=P(x,y) / P(y); In the formula, Con(x,y) represents the confidence of the association rule between attributes x and y; P(x / y) represents the probability of containing attribute x in the frequent item set containing attribute y; P(x,y) represents the probability of occurrence of the relationship item set combination between attributes x and y; P(y) represents the occurrence probability of a combination of relation itemsets containing attribute y. Set the confidence threshold of the association rule (value range 0-1, percentage), remove all low-confidence association rules, and select high-confidence association rules as the association rules of the subject data; S5. Form a subject data association report; S51. Based on the association rules formed by mining, a relationship topology diagram of the attribute characteristics of the subject data is constructed to intuitively express the association relationship between the main attributes of the subject data; S52. Based on the subject data association rules, conduct a special analysis on the association relationship of the subject data to form a task support subject data association analysis report, which mainly includes: comparative relationship analysis, abstract relationship analysis, temporal relationship analysis, and progressive relationship analysis.

2. The method for analyzing the association of security mission subject data according to claim 1 is characterized in that: The formation of subject data association in S5 mainly includes: classification hierarchy relationship, data inclusion relationship, data dependency relationship, and data interaction relationship.

3. The method for analyzing the association of security mission subject data according to claim 1 is characterized in that: The specific analysis content in S52 is as follows: S521. Comparative relationship analysis: Analyze certain differences between data through data comparison; S522. Abstract relationship analysis: Analyze the data combination relationship, summarize and express the abstract relationship of the data; S523, Time series relationship analysis: the time sequence of data generation, highlighting the time sequence between data; S524. Progressive relationship analysis: the continuity between data objects, data generation, management and maintenance objects.

4. The method for analyzing the association of task subject data according to claim 1 is characterized in that: The establishment of the task subject data set in S1 will use a data storage device, and the data storage device includes a server body, and heat dissipation nets are set on both sides of the left and right sides of the surface of the server body, and pushing devices are set on both sides of the left and right sides of the server body. The pushing device includes a double-headed hydraulic telescopic rod, and both ends of the double-headed hydraulic telescopic rod are connected to fixed blocks, one side of the two fixed blocks is connected to a pushing frame, a dust suction component is set between the two pushing frames, and a suction device is set at the bottom of the server body.

5. The method for analyzing the association of task subject data according to claim 4 is characterized in that: A fixing frame is arranged at the center position of the surface of the double-head hydraulic telescopic rod, and the fixing frame is connected to the surface of the server body.

6. The method for analyzing the association of task subject data according to claim 4 is characterized in that: The dust suction component comprises a suction pipe, both ends of the suction pipe are connected with dust suction covers, and one side of the two dust suction covers is connected with a suction head.

7. The method for analyzing the association of task subject data according to claim 4 is characterized in that: The suction device comprises a delivery pump, the input end and the output end of the delivery pump are both connected to delivery pipes, and one end of the two delivery pipes is respectively connected to a first connecting pipe and a second connecting pipe.

8. The method for analyzing the association of task subject data according to claim 4 is characterized in that: Box bodies are installed on both sides of the bottom of the server body, and a collection box is arranged inside the box body.

9. The method for analyzing the association of task subject data according to claim 1, characterized in that: A protection component is arranged on the surface of the server body, and the protection component comprises a plurality of mounting seats, a plurality of mounting seats are each provided with a mounting block inside, and a plurality of mounting blocks are each connected with a round rod on the surface.

10. The method for analyzing the association of security mission subject data according to claim 9 is characterized in that: Protective covers are connected between the plurality of circular rods.

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