Associated information prediction method and device, computer equipment and storage medium

Abstract

The invention relates to an associated information prediction method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining multiple pieces of user information and multiple pieces of case information, then classifying the multiple pieces of user information to obtain the user information of each category, and classifying the multiple pieces of case information to obtain the case information of each category. And finally, predicting an association relationship between the user information of each category and the case information of each category to obtain target association information. In the method, the problem of low prediction efficiency caused by the fact that a large amount of data processing and calculation processes are involved due to the fact that a whole data set of user information and case information is directly analyzed in a traditional method is solved, the data set is classified to obtain a plurality of data subsets with the same features, and the prediction efficiency is improved. And then association relationship prediction is carried out according to the data subsets, the whole data set does not need to be predicted, and the prediction efficiency can be greatly improved.

Description

Technical Field

[0001] The present application relates to the field of computer technologies, and particularly to a method, apparatus, computer device, and storage medium for predicting associated information. Background Art

[0002] With the rapid development of network devices and information transmission devices, many application devices or software for collecting information, retrieving information, and predicting information have emerged, which can be applied in various technical fields. For example, an information management system can collect and analyze big data based on real-time updated network information, and in particular, it is particularly important to analyze the association relationship between users and various types of cases.

[0003] However, it is difficult to efficiently analyze the association relationship between users and various types of cases using existing information management systems. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, apparatus, computer device, and storage medium for predicting associated information that can improve the efficiency of analyzing association relationships.

[0005] In a first aspect, the present application provides a method for predicting associated information, the method comprising: Obtaining a plurality of user information and a plurality of case information; Classifying the plurality of user information to obtain user information of each category, and classifying the plurality of case information to obtain case information of each category; Predicting the association relationship between the user information of each category and the case information of each category to obtain target associated information.

[0006] The method for predicting associated information provided by the embodiments of the present application obtains a plurality of user information and a plurality of case information, then classifies the plurality of user information to obtain user information of each category, and classifies the plurality of case information to obtain case information of each category. Finally, it predicts the association relationship between the user information of each category and the case information of each category to obtain target associated information. In the above method, since the traditional method directly analyzes the entire data set of user information and case information, involving a large amount of data processing and calculation processes, resulting in low prediction efficiency, the above method classifies the data set to obtain multiple data subsets with the same features, and then predicts the association relationship according to the data subsets, without predicting the entire data set, which can greatly improve the prediction efficiency.

[0007] In one of the embodiments, predicting the association relationship between the user information of each category and the case information of each category to obtain target associated information includes: For each category of user information, determine the support degree between the user information of this category and the case information of each category; Associate the case information and user information corresponding to the support degree that meets the support degree threshold, and generate target association information.

[0008] In one embodiment, determining the support degree between the user information of this category and the case information of each category includes: For each category of case information, determine the case information that matches the user information of this category; According to the number of the matched case information and the number of multiple case information, determine the support degree between the user information of this category and the case information of each category.

[0009] The method described in the embodiments of the present application, by using the support degree threshold as a screening condition to predict the feature combinations with a higher occurrence frequency in the dataset, and then obtaining the target association information, can not only improve the prediction efficiency but also improve the prediction accuracy.

[0010] In one embodiment, the method further includes: For each category of user information, determine the confidence degree between the user information of this category and the case information of each category; Associate the case information and user information corresponding to the confidence degree that meets the first preset condition, and generate target association information; the first preset condition includes that the support degree is greater than the support degree threshold and the confidence degree is much greater than the confidence degree threshold.

[0011] In one embodiment, determining the confidence degree between the user information of this category and the case information of each category includes: Screen out multiple target case information from multiple case information; the category of the target case information matches the category of the user information; For each category of case information, determine the case information that matches the user information of this category; According to the number of the matched case information and the number of multiple target case information, determine the confidence degree between the user information of this category and the case information of each category.

[0012] The method described in the embodiments of the present application, by using the support degree threshold, confidence degree threshold and lift degree threshold as a screening condition to predict the feature combinations with a higher occurrence frequency in the dataset, and then obtaining the target association information, can not only improve the prediction efficiency but also improve the prediction accuracy.

[0013] In one embodiment, the method further includes: Determine the lift between the user information of this category and the case information of various categories according to the confidence between the user information of this category and the case information of various categories, and the support between the user information of this category and the case information of various categories; Associate the case information and user information corresponding to the lift that meets the second preset condition, and generate target association information; the second preset condition includes that the support is greater than the support threshold, the confidence is greater than the confidence threshold, and the lift is greater than the lift threshold.

[0014] The method described in the embodiments of the present application, by using the support threshold, confidence threshold, and lift threshold as a screening condition to predict the feature combinations with a higher frequency of occurrence in the dataset, and then obtaining the target association information, can not only improve the prediction efficiency but also improve the prediction accuracy.

[0015] In one of the embodiments, the method further includes: Process the multiple user information and multiple case information according to the preset key fields to obtain the processed multiple user information and the processed multiple case information; Classify the multiple user information to obtain the user information of various categories, and classify the multiple case information to obtain the case information of various categories, including: Classify the processed multiple user information to obtain the user information of various categories, and classify the processed multiple case information to obtain the case information of various categories.

[0016] In the embodiments of the present application, after the computer device obtains the processed multiple user information and the processed multiple case information, it can classify the processed multiple user information to obtain the user information of various categories, and classify the processed multiple case information to obtain the case information of various categories.

[0017] In a second aspect, the present application further provides a prediction device for association information, and the device includes: An acquisition module, configured to acquire multiple user information and multiple case information; A classification module, configured to classify the multiple user information to obtain the user information of various categories, and classify the multiple case information to obtain the case information of various categories; A prediction module, configured to predict the association relationship between the user information of various categories and the case information of various categories to obtain the target association information.

[0018] In a third aspect, the present application further provides a computer device, and the computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Acquire multiple user information and multiple case information; Classify multiple user information to obtain user information of each category, and classify multiple case information to obtain case information of each category; Predict the correlation between the user information of each category and the case information of each category to obtain target correlation information.

[0019] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Obtain multiple user information and multiple case information; Classify multiple user information to obtain user information of each category, and classify multiple case information to obtain case information of each category; Predict the correlation between the user information of each category and the case information of each category to obtain target correlation information.

[0020] In a fifth aspect, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the following steps are implemented: Obtain multiple user information and multiple case information; Classify multiple user information to obtain user information of each category, and classify multiple case information to obtain case information of each category; Predict the correlation between the user information of each category and the case information of each category to obtain target correlation information.

[0021] In the above method for predicting correlation information, by obtaining multiple user information and multiple case information, then classifying the multiple user information to obtain user information of each category, and classifying the multiple case information to obtain case information of each category, and finally predicting the correlation between the user information of each category and the case information of each category to obtain target correlation information. In the above method, since the traditional method directly analyzes the entire data set of user information and case information, involving a large amount of data processing and calculation processes, resulting in the problem of low prediction efficiency. The above method classifies the data set to obtain multiple data subsets with the same features, and then predicts the correlation relationship according to the data subsets, without predicting the entire data set, which can greatly improve the prediction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is the internal structure diagram of a computer device in an embodiment; Figure 2 It is the flowchart of the method for predicting correlation information in an embodiment; Figure 3 It is a schematic diagram of the relationship between unemployed person information and person categories in an embodiment; Figure 4 It is a schematic diagram of the relationship between case information and case categories in an embodiment; Figure 5 It is a schematic flowchart of a method for predicting associated information in another embodiment; Figure 6 It is a schematic flowchart of a method for predicting associated information in another embodiment; Figure 7 It is a schematic flowchart of a method for predicting associated information in another embodiment; Figure 8 It is a schematic flowchart of a method for predicting associated information in another embodiment; Figure 9 It is a schematic flowchart of a method for predicting associated information in another embodiment; Figure 10 It is a schematic flowchart of a method for predicting associated information in another embodiment; Figure 11 It is a schematic flowchart of a method for predicting associated information in another embodiment; Figure 12 It is a structural block diagram of a device for predicting associated information in an embodiment; Figure 13 It is a structural block diagram of a device for predicting associated information in another embodiment. Detailed implementation manners

[0023] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0024] With the rapid development of network devices and information transmission devices, many application devices or software for collecting information, retrieving information, and predicting information have emerged, which can be applied in various technical fields. For example, an information management system can perform big data collection and analysis based on real-time updated network information. In particular, it is particularly important to analyze the association relationship between users and various types of cases. For example, analyzing the association relationship between unemployed persons and certain cases. However, it is difficult to efficiently analyze the association relationship between users and various types of cases using existing information management systems. The present application provides a method for predicting associated information, aiming to solve the above technical problems. The following embodiments will specifically illustrate the method for predicting associated information described in the present application.

[0025] The method for predicting associated information provided by the embodiments of the present application can be applied to such asFigure 1 In the computer device shown, the computer device can be a terminal or a server, and its internal structure diagram can be as Figure 1 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements a method for predicting associated information. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0026] Those skilled in the art can understand that Figure 1 the structure shown in

[0027] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout. Figure 2 In one embodiment, as Figure 1 shown, a method for predicting associated information is provided. Taking the method applied to the computer device in

[0028] Among them, the user information includes at least one of the user's name, ID number, working time, unemployment time, name of the original unit, occupation category, reason for unemployment, education level, home address, and contact phone number. The case information includes at least one of the case number, case name, brief case description, case-filing time, case-filing unit, time of the incident, place of the incident, handling result, and case status. The user information and the case information are corresponding. The user information is the user information of each user in each case information, and the case information is the case information of each user in each user information.

[0029] In the embodiment of the present application, when it is necessary to analyze the correlation between the user information and the case information, the computer device can obtain a plurality of user information and a plurality of case information from various legally public data sources by means of web crawling. Specifically, a large amount of user information and case information can be obtained from data sources such as social media, news reports, and industry reports. Optionally, the computer device can also obtain a plurality of user information and a plurality of case information through appropriate channels.

[0030] S102, classify the plurality of user information to obtain user information of each category, and classify the plurality of case information to obtain case information of each category.

[0031] Among them, the user information of each category includes user information of the gender category and user information of the age category. The case information of each category includes different-level codes, different-level names, and different-level descriptions.

[0032] In the embodiment of the present application, after the computer device obtains a plurality of user information and a plurality of case information, it can classify the plurality of user information into the gender category and the age category to obtain user information of each category. Specifically, as Figure 3 shown, the user information can be classified into the gender category according to the gender code and gender name, and the user information can be classified into the age category according to the age and age range, so as to obtain the correlation between gender, age and case information. For example, the gender name corresponding to gender code 1 is male, the gender name corresponding to gender code 2 is female, the age range corresponding to age 1 is 10-20 years old, the age range corresponding to age 2 is 20-30 years old, or the age range corresponding to age 1 is the juvenile stage, and the age range corresponding to age 2 is the youth stage. Optionally, the user information can also be classified according to other classification rules. For example, classifying the user information according to the unemployment time category can obtain the correlation between the unemployment time and the case information, which can reflect the relationship between the reasons and results of unemployed people in different types of cases, and can provide a reference for relevant departments to prevent and respond to the occurrence of cases.

[0033] In addition, the computer device can classify multiple case information according to case coding categories, case name categories, and case description categories to obtain case information of each category. Specifically, as Figure 4 shown, the case information can be classified at the first level according to the first-level coding of the case category, the first-level name of the case category, and the first-level description of the case category, classified at the second level according to the second-level coding of the case category, the second-level name of the case category, and the second-level description of the case category, and classified at the third level according to the third-level coding of the case category, the third-level name of the case category, and the third-level description of the case category. Among them, the third-level classification result is a subset of the second-level classification result, and the second-level classification result is a subset of the first-level classification result, that is, the first-level classification result contains multiple second-level classification results, and the second-level classification result includes multiple third-level classification results. For example, the first-level number 10 indicates that the case is case A, the second-level number 05 indicates that the case is case a in case A, and the third-level number 14 indicates that the case is case a1 in case a in case A. Correspondingly, the first-level name is case A, the second-level name is case a, and the third-level name is case a-i. Accordingly, the case number of this case information can be 100514, and the case name can be A-a-a1. Optionally, the case information can also be classified according to other classification rules. For example, classifying the case information according to the place where the case occurred can reflect the correlation of the case in terms of time and space, and can provide a reference for relevant departments to prevent and respond to the occurrence of cases.

[0034] S103. Predict the association relationship between user information of each category and case information of each category to obtain target association information.

[0035] Among them, the target association information includes the characteristics of user information and the characteristics of case information. The target association information is used to represent the association relationship between users and cases. For example, the target association information is: male, under 24 years old, case a1. This target association information indicates that male unemployed people with an age range under 24 years old have a relatively high probability of committing case a1. Another example, the target association information is: female, 24 - 35 years old, case a2. This target association information indicates that female unemployed people with an age range between 24 and 35 years old have a relatively high probability of committing case a2.

[0036] In the embodiments of the present application, after the computer device obtains user information of various categories and case information of various categories, it may input the user information of various categories and the case information of various categories into a preset prediction model for correlation relationship prediction to obtain target correlation information. Optionally, a correlation degree calculation function may also be used to calculate the correlation degree of the user information of various categories and the case information of various categories to obtain target correlation information. Optionally, statistics may also be used to analyze the correlation relationship between the user information of various categories and the case information of various categories to obtain target correlation information. Further, after the computer device obtains the target correlation information, it may display the target correlation information in a visual manner, which can more intuitively display the correlation relationship between the user information and the case information. Specifically, the target correlation information may be displayed in ways such as tables, charts, maps, etc. For example, a histogram may be used to show the proportional distribution of various case types among unemployed men and women of different age groups, and a line chart may be used to reflect the relationship trend between the length of unemployment time and the number of cases of different categories, etc.

[0037] The prediction method for correlation information provided by the embodiments of the present application obtains multiple user information and multiple case information, then classifies the multiple user information to obtain user information of various categories, and classifies the multiple case information to obtain case information of various categories. Finally, the correlation relationship between the user information of various categories and the case information of various categories is predicted to obtain target correlation information. In the above method, since the traditional method directly analyzes the entire data set of user information and case information, which involves a large amount of data processing and calculation processes, resulting in the problem of low prediction efficiency. The above method classifies the data set to obtain multiple data subsets with the same characteristics, and then predicts the correlation relationship based on the data subsets, without predicting the entire data set, which can greatly improve the prediction efficiency.

[0038] In one embodiment, a specific implementation manner for obtaining target correlation information is also provided, as Figure 5 shown. The "predicting the correlation relationship between the user information of various categories and the case information of various categories to obtain target correlation information" in step S103 above includes: S201, for each category of user information, determine the support degree between the user information of this category and the case information of various categories.

[0039] Among them, the support degree represents the probability or quantity of the simultaneous occurrence of user information and case information in all combinations of user information and case information.

[0040] For example, the support represents the possibility that user information X and case information Y appear simultaneously in the combination D of all user information and case information. That is, among all combinations D, the probability of simultaneously containing X and Y is: Support(X Y) = P(X,Y) = |{R|X Y R,R D}| / |D|. The support is the primary condition for mining association rules, and it filters out the generally existing association rules from the "quantity" dimension.

[0041] In the embodiments of the present application, after the computer device obtains multiple user information and multiple case information, for any category of user information, the support between the user information of this category and various categories of case information can be calculated respectively. For example, multiple user information includes user information A, user information B, and user information C, and multiple case information includes case information a, case information b, and case information c. Then, for user information A, the Apriori algorithm can be used to calculate the support between user information A and case information a, the support between user information A and case information b, and the support between user information A and case information c.

[0042] Further, as Figure 6 shown, "for each category of user information, determine the support between the user information of this category and various categories of case information" in the above step S201 includes: S2011, for each category of case information, determine the case information that matches the user information of this category.

[0043] In the embodiments of the present application, the computer device can, for each category of user information, determine the case information that matches the user information of this category, that is, the case information that exists simultaneously with the user information of this category. For example, the case information that matches user information A is case information a and case information b.

[0044] S2012, according to the quantity of the matching case information and the quantity of multiple case information, determine the support between the user information of this category and various categories of case information.

[0045] Among them, the number of matching case information is the combination number of case information existing simultaneously with the user information of this category. The number of multiple case information is the combination number of the user information of each category and the case information of each category respectively. For example, multiple user information includes user information A, user information B, and user information C, and multiple case information includes case information a, case information b, and case information c. For user information A, the case information occurring simultaneously with user information A is case information a and case information b, that is, Aa, Ab. Then the multiple case information is Aa, Ab, Ac, Ba, Bb, Bc.

[0046] In the embodiment of the present application, after the computer device determines the case information matching the user information of this category, it can further determine the combination of the user information of each category and the case information of each category respectively, and then can perform probability calculation on the number of matching case information and the number of multiple case information to obtain the support degree between the user information of this category and the case information of each category. For example, the number of matching case information is 2, and the number of multiple case information is 6, then the support degree is 1 / 3.

[0047] S202, associate the case information and user information corresponding to the support degree that meets the support degree threshold, and generate target association information.

[0048] Among them, the support degree threshold is set with the goal of obtaining frequent item sets. The frequent item set is a combination of user information and case information that meets the support degree threshold. According to the structural characteristics of all combinations D, the support degree threshold is used to eliminate rules with relatively low occurrence frequencies, and the remaining non-empty subsets are called frequent item sets.

[0049] In the embodiment of the present application, after the computer device determines the support degree between the user information of each category and the case information of each category based on the above steps, it can obtain the user information and case information that meet the requirements of the support degree threshold by adjusting the support degree threshold. Specifically, the user information and case information greater than the support degree threshold can be used as the user information and case information that meet the requirements of the support degree threshold. Then the computer device can extract features from the user information that meets the requirements of the support degree threshold to obtain user features and case features, and generate target association information based on the user features and case features. Among them, the process of adjusting the support degree threshold is as follows: if no frequent item set is output when the support degree threshold is the first value, then the support degree threshold can be adjusted smaller until a frequent item set is output. If the number of output frequent item sets does not meet the preset number, the size of the support degree threshold can be continuously adjusted until the number of output frequent item sets meets the preset number.

[0050] The method described in the embodiments of the present application predicts feature combinations with high frequencies in a dataset by using the support threshold as a screening condition, and then obtains target association information, which can not only improve the prediction efficiency but also improve the prediction accuracy.

[0051] In one embodiment, a method for predicting association information is also provided. As Figure 7 shown, Figure 5 the method described in the embodiment further includes: S203. For user information of each category, determine the confidence between the user information of this category and case information of each category.

[0052] Among them, the confidence represents the probability or quantity that, in the combination D of all user information and case information, when a certain user information X appears, a certain case information Y appears. The confidence refers to the probability that Y appears under the condition that the prerequisite X appears: Confidence(X Y) = P(Y|X) = P(X, Y) / P(X) = Support(X Y) / Support(X). The confidence is a secondary condition for mining association rules and screens out required association rules from the "quality" dimension.

[0053] In the embodiments of the present application, after the computer device obtains multiple user information and multiple case information, for user information of any category, the confidence between the user information of this category and case information of each category can be calculated respectively. For example, if the multiple user information includes user information A, user information B, and user information C, and the multiple case information includes case information a, case information b, and case information c, then for user information A, the Apriori algorithm can be used to calculate the confidence between user information A and case information a, the confidence between user information A and case information b, and the confidence between user information A and case information c.

[0054] Further, as Figure 8 shown, "determine the confidence between user information of each category and case information of each category" in the above step S203 includes: S2031. Screen out multiple target case information from the multiple case information.

[0055] Among them, the category of the target case information matches the category of the user information.

[0056] In the embodiments of the present application, after the computer device obtains multiple case information, it can screen out multiple target case information from the multiple case information. For example, a1 information, a2 case information, a3 case information, etc. can be screened out from the A case information.

[0057] S2032. For the case information of each category, determine the case information that matches the user information of that category.

[0058] In the embodiment of the present application, after the computer device obtains multiple case information, in the multiple case information, when a certain user information appears, multiple target case information can be screened out from the multiple case information. For example, from case information A, a1 case information that matches males, a2 case information that matches males, a3 case information that matches males, etc. can be screened out.

[0059] S2033. According to the number of the matched case information and the number of the multiple target case information, determine the confidence level between the user information of this category and the case information of each category.

[0060] Among them, the number of the matched case information is the combination number of the case information that coexists with the user information of this category.

[0061] In the embodiment of the present application, after the computer device determines the target case information and the matched case information based on the above steps, the number of the target case information and the matched case information can be further determined respectively. Then, the computer device can use the user information of this category as a condition according to the number of the matched case information and the number of the multiple target case information, further determine the conditional probability between the user information of this category and the case information of each category, and use the conditional probability between the user information of this category and the case information of each category as the confidence level between the user information of this category and the case information of each category.

[0062] S204. Associate the case information and the user information corresponding to the confidence level that meets the first preset condition, and generate target association information.

[0063] Among them, the confidence level threshold is set with the goal of obtaining frequent item sets. The first preset condition includes that the support degree is greater than the support degree threshold and the confidence level is much greater than the confidence level threshold.

[0064] In the embodiment of the present application, after the computer device determines the support degree and the confidence level between the user information of each category and the case information of each category based on the above steps, by adjusting the support degree and the confidence level, user information and case information that meet the requirements of the support degree threshold and the confidence level threshold can be obtained. Specifically, the user information and the case information that are greater than the support degree threshold and greater than the confidence level threshold can be used as the user information and the case information that meet the requirements of the support degree threshold and the confidence level threshold. Then the computer device can perform feature extraction on the user information that meets the requirements of the support degree threshold and the confidence level threshold to obtain user features and case features, and generate target association information according to the user features and the case features.

[0065] The method described in the embodiments of the present application predicts the feature combinations with a high occurrence frequency in the dataset by using the support threshold, confidence threshold, and lift threshold as a screening condition, and then obtains the target association information, which can not only improve the prediction efficiency but also improve the prediction accuracy.

[0066] In one embodiment, a method for predicting association information is further provided. As Figure 9 shown, Figure 5 and Figure 7 the method described in the embodiment further includes: S205. Determine the lift between the user information of this category and the case information of various categories according to the confidence between the user information of this category and the case information of various categories, and the support between the user information of this category and the case information of various categories.

[0067] Among them, the lift is used to reflect the reliability of the association rule and is used to evaluate the degree of association between the user information and the case information under the condition that the user information is a certain specific user information. When the lift > 1, it indicates a strong association between the two; when the lift = 1, it is considered that the two are independent of each other; when the lift < 1, it is considered that the correlation between the two is weak.

[0068] In the embodiments of the present application, for the user information of each category, after the computer device obtains the support and confidence between the user information of this category and the case information of various categories, it can determine the lift between the user information of this category and the case information of various categories according to the confidence between the user information of this category and the case information of various categories, and the support between the user information of this category and the case information of various categories. Specifically, the support and confidence can be multiplied to obtain the lift.

[0069] S206. Associate the case information and user information corresponding to the lift that meets the second preset condition, and generate the target association information.

[0070] Among them, the second preset condition includes that the support is greater than the support threshold, the confidence is greater than the confidence threshold, and the lift is greater than the lift threshold.

[0071] In the embodiments of the present application, after the computer device determines the support degree, confidence degree, and lift degree between the user information of each category and the case information of each category based on the above steps, it can adjust the support degree, confidence degree, and lift degree to obtain user information and case information that meet the requirements of the support degree, confidence degree, and lift degree. Specifically, the user information and case information that are greater than the support degree threshold, greater than the confidence degree threshold, and greater than the lift degree threshold can be used as the user information and case information that meet the requirements of the support degree threshold, confidence degree threshold, and lift degree threshold. Then, the computer device can extract features from the user information that meets the requirements of the support degree threshold, confidence degree threshold, and lift degree threshold to obtain user features and case features, and generate target association information based on the user features and case features.

[0072] The method described in the embodiments of the present application predicts the feature combinations with higher frequencies in the dataset by using the support degree threshold, confidence degree threshold, and lift degree threshold as a screening condition, and then obtains the target association information. This can not only improve the prediction efficiency but also improve the prediction accuracy.

[0073] In one embodiment, a method for predicting association information is also provided, as Figure 10 shown, Figure 2 The method described in the embodiment further includes: S104, processing multiple user information and multiple case information according to preset key fields to obtain the processed multiple user information and the processed multiple case information.

[0074] Among them, the preset key fields include the preset key fields of user information and the key fields of case information. The preset key fields of user information include at least one of name, ID number, work start time, unemployment time, original unit name, occupation category, unemployment reason, education level, home address, and contact phone number. The key fields of case information include at least one of case number, case name, brief case description, case filing time, case filing unit, case occurrence time, case occurrence location, handling result, and case status. The user information and case information are corresponding. The user information is the user information of each user in each case information, and the case information is the case information of each user in each user information.

[0075] In the embodiments of the present application, after the computer device obtains multiple user information and multiple case information, it may first process the multiple user information and multiple case information. Specifically, it may perform operations such as aggregation, processing, governance, extraction, cleaning, transformation, and loading on the multiple user information and multiple case information to obtain the processed multiple user information and the processed multiple case information. Optionally, after the computer device obtains the processed multiple user information and the processed multiple case information, it may construct a model database based on the multiple user information and multiple case information. When it is necessary to predict the association relationship between the multiple user information and multiple case information, the model database can be directly called to obtain it.

[0076] Correspondingly, when the computer device executes step S102, "classify the multiple user information to obtain user information of each category, and classify the multiple case information to obtain case information of each category", the specific execution steps are: classify the processed multiple user information to obtain user information of each category, and classify the processed multiple case information to obtain case information of each category.

[0077] In the embodiments of the present application, after the computer device obtains the processed multiple user information and the processed multiple case information, it may classify the processed multiple user information to obtain user information of each category, and classify the processed multiple case information to obtain case information of each category.

[0078] The method described in the embodiments of the present application improves the accuracy of prediction by performing preprocessing operations on the collected data.

[0079] Combining all the above embodiments, a method for predicting association information is further provided, as Figure 11 shown. The method includes: S401, obtain multiple user information and multiple case information.

[0080] S402, process the multiple user information and multiple case information according to preset key fields to obtain the processed multiple user information and the processed multiple case information.

[0081] S403, for the processed user information of each category, and for the processed case information of each category, determine the case information that matches the user information of this category.

[0082] S404, determine the support degree between the user information of this category and the case information of each category according to the number of the matching case information and the number of the multiple case information.

[0083] S405, predict the association relationship between the user information of each category and the case information of each category to obtain the target association information.

[0084] S406. For each category of user information, filter out multiple target case information from multiple case information. The category of the target case information matches the category of the user information.

[0085] S407. For each category of case information, determine the case information that matches the user information of this category.

[0086] S408. According to the quantity of the matched case information and the quantity of multiple target case information, determine the confidence level between the user information of the category and the case information of each category.

[0087] S409. According to the confidence level between the user information of this category and the case information of each category, and the support degree between the user information of this category and the case information of each category, determine the lift degree between the user information of this category and the case information of each category.

[0088] S410. Associate the case information and user information corresponding to the lift degree that meets the second preset condition, and generate target association information. The second preset condition includes that the support degree is greater than the support degree threshold, the confidence level is much greater than the confidence level threshold, and the lift degree is greater than the lift degree threshold.

[0089] The methods described in the above steps have been described in the foregoing embodiments. For detailed content, please refer to the foregoing description and will not be elaborated here.

[0090] The method described in the embodiment of the present application can, through the correlation analysis of various types of case behaviors of unemployed persons, count the high-frequency case behaviors occurring to unemployed persons, as well as the case behavior characteristics of unemployed persons with different attributes, and can analyze problems such as whether there is a causal relationship between the unemployment period and a certain case behavior. Moreover, analyze the causal relationship between the length of unemployment of unemployed persons and a certain case behavior, accurately divide unemployed persons according to the length of unemployment time, provide more effective assistance measures, help unemployed persons re-employ as soon as possible, and avoid the occurrence of case behaviors due to long-term unemployment. Furthermore, through the case behavior characteristics of unemployed persons of different ages and genders, accurately identify the unemployed persons existing in the jurisdiction, prevent possible case behaviors that may occur and appear in advance, and conduct precise control and early warning.

[0091] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless specifically stated herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0092] Based on the same inventive concept, an embodiment of the present application further provides an apparatus for predicting associated information for implementing the method for predicting associated information described above. The solution provided by this apparatus for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the apparatus for predicting associated information provided below can refer to the limitations on the method for predicting associated information in the above text, and will not be repeated here.

[0093] In one embodiment, as Figure 12 shown, an apparatus for predicting associated information is provided, including: An acquisition module 10, configured to acquire a plurality of user information and a plurality of case information.

[0094] A classification module 11, configured to classify the plurality of user information to obtain user information of each category, and classify the plurality of case information to obtain case information of each category.

[0095] A prediction module 12, configured to predict the association relationship between the user information of each category and the case information of each category to obtain target associated information.

[0096] In one embodiment, the above prediction module 12 includes: A first determination unit, configured to determine, for the user information of each category, the degree of support between the user information of this category and the case information of each category.

[0097] A generation unit, configured to associate the case information and user information corresponding to the degree of support that meets the support threshold, and generate target associated information.

[0098] In one embodiment, the above first determination unit includes: A first determination subunit, configured to determine, for the case information of each category, the case information that matches the user information of this category.

[0099] A second determination subunit, configured to determine the support degree between the user information of this category and the case information of each category according to the number of the matched case information and the number of the multiple case information.

[0100] In one embodiment, the above first determination unit further includes: A third determination subunit, configured to determine the confidence degree between the user information of each category and the case information of each category for each category of user information.

[0101] A first generation subunit, configured to associate the case information and the user information corresponding to the confidence degree that meets the first preset condition, and generate target association information; the first preset condition includes that the support degree is greater than the support degree threshold, and the confidence degree is much greater than the confidence degree threshold.

[0102] In one embodiment, the above third determination subunit is specifically configured to screen out multiple target case information from the multiple case information; the category of the target case information matches the category of the user information; for the case information of each category, determine the case information that matches the user information of this category; and determine the confidence degree between the user information of this category and the case information of each category according to the number of the matched case information and the number of the multiple target case information.

[0103] In one embodiment, the above first determination unit further includes: A fourth determination subunit, configured to determine the promotion degree between the user information of this category and the case information of each category according to the confidence degree between the user information of this category and the case information of each category, and the support degree between the user information of this category and the case information of each category.

[0104] A second generation subunit, configured to associate the case information and the user information corresponding to the promotion degree that meets the second preset condition, and generate target association information; the second preset condition includes that the support degree is greater than the support degree threshold, the confidence degree is much greater than the confidence degree threshold, and the promotion degree is greater than the promotion degree threshold.

[0105] In one embodiment, as Figure 13 shown, the above prediction device for the association information further includes: A processing module 13, configured to process the multiple user information and the multiple case information according to a preset keyword field, and obtain the processed multiple user information and the processed multiple case information.

[0106] Correspondingly, the above classification module 11 is specifically configured to classify the processed multiple user information to obtain the user information of each category, and classify the processed multiple case information to obtain the case information of each category. Each module in the above-mentioned prediction device for associated information can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0107] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented: Obtain a plurality of user information and a plurality of case information; Classify the plurality of user information to obtain user information of each category, and classify the plurality of case information to obtain case information of each category; Predict the association relationship between the user information of each category and the case information of each category to obtain target associated information.

[0108] In one embodiment, when the processor executes the computer program, the following steps are further implemented: For each category of user information, determine the degree of support between the user information of this category and the case information of each category; Associate the case information and user information corresponding to the degree of support that meets the support threshold, and generate target associated information.

[0109] In one embodiment, when the processor executes the computer program, the following steps are further implemented: For each category of case information, determine the case information that matches the user information of this category; According to the number of the matching case information and the number of the plurality of case information, determine the degree of support between the user information of this category and the case information of each category.

[0110] In one embodiment, when the processor executes the computer program, the following steps are further implemented: For each category of user information, determine the confidence level between the user information of this category and the case information of each category; Associate the case information and user information corresponding to the confidence level that meets the first preset condition, and generate target associated information; the first preset condition includes that the degree of support is greater than the support threshold and the confidence level is much greater than the confidence threshold.

[0111] In one embodiment, when the processor executes the computer program, the following steps are further implemented: Screen out a plurality of target case information from the plurality of case information; the category of the target case information matches the category of the user information; For each category of case information, determine the case information that matches the user information of this category; Determine the confidence level between the user information of each category and the case information of each category according to the number of matched case information and the number of multiple target case information.

[0112] In one embodiment, when the processor executes the computer program, the following steps are further implemented: Determine the lift between the user information of each category and the case information of each category according to the confidence level between the user information of each category and the case information of each category, and the support degree between the user information of each category and the case information of each category; Associate the case information and user information corresponding to the lift that meets the second preset condition, and generate target association information; the second preset condition includes that the support degree is greater than the support degree threshold, the confidence level is much greater than the confidence level threshold, and the lift is greater than the lift threshold.

[0113] In one embodiment, when the processor executes the computer program, the following steps are further implemented: Process the multiple user information and multiple case information according to the preset key fields to obtain the processed multiple user information and the processed multiple case information; Classify the multiple user information to obtain the user information of each category, and classify the multiple case information to obtain the case information of each category, including: Classify the processed multiple user information to obtain the user information of each category, and classify the processed multiple case information to obtain the case information of each category.

[0114] For a computer device provided in the above embodiment, its implementation principle and technical effect are similar to those of the above method embodiment, and will not be elaborated here.

[0115] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Obtain multiple user information and multiple case information; Classify the multiple user information to obtain the user information of each category, and classify the multiple case information to obtain the case information of each category; Predict the association relationship between the user information of each category and the case information of each category to obtain target association information.

[0116] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: For each category of user information, determine the support degree between the user information of each category and the case information of each category; Associate the case information and user information corresponding to the support degree that meets the support degree threshold, and generate target association information.

[0117] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: For each category of case information, determine the case information that matches the user information of that category; According to the number of the matched case information and the number of multiple case information, determine the support degree between the user information of that category and the case information of each category.

[0118] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: For each category of user information, determine the confidence degree between the user information of that category and the case information of each category; Associate the case information and user information corresponding to the confidence degree that meets the first preset condition, and generate target association information; the first preset condition includes that the support degree is greater than the support degree threshold, and the confidence degree is much greater than the confidence degree threshold.

[0119] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: Screen out multiple target case information from multiple case information; the category of the target case information matches the category of the user information; For each category of case information, determine the case information that matches the user information of that category; According to the number of the matched case information and the number of multiple target case information, determine the confidence degree between the user information of the category and the case information of each category.

[0120] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: According to the confidence degree between the user information of that category and the case information of each category, and the support degree between the user information of that category and the case information of each category, determine the promotion degree between the user information of that category and the case information of each category; Associate the case information and user information corresponding to the promotion degree that meets the second preset condition, and generate target association information; the second preset condition includes that the support degree is greater than the support degree threshold, the confidence degree is much greater than the confidence degree threshold, and the promotion degree is greater than the promotion degree threshold.

[0121] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: Process multiple user information and multiple case information according to preset keyword fields to obtain processed multiple user information and processed multiple case information; Classify multiple user information to obtain user information of each category, and classify multiple case information to obtain case information of each category, including: Classify multiple processed user information to obtain user information of each category, and classify multiple processed case information to obtain case information of each category.

[0122] A computer-readable storage medium provided by the above embodiment has the same implementation principle and technical effect as the above method embodiment, and will not be elaborated here.

[0123] In one embodiment, a computer program product is provided, including a computer program, which when executed by a processor, implements the following steps: Obtain multiple user information and multiple case information; Classify multiple user information to obtain user information of each category, and classify multiple case information to obtain case information of each category; Predict the association relationship between the user information of each category and the case information of each category to obtain target association information.

[0124] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: For each category of user information, determine the support degree between the user information of this category and the case information of each category; Associate the case information and user information corresponding to the support degree that meets the support degree threshold, and generate target association information.

[0125] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: For each category of case information, determine the case information that matches the user information of this category; According to the number of the matching case information and the number of multiple case information, determine the support degree between the user information of this category and the case information of each category.

[0126] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: For each category of user information, determine the confidence degree between the user information of this category and the case information of each category; Associate the case information and user information corresponding to the confidence degree that meets the first preset condition, and generate target association information; the first preset condition includes that the support degree is greater than the support degree threshold and the confidence degree is much greater than the confidence degree threshold.

[0127] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: Screen out multiple target case information from multiple case information; the category of the target case information matches the category of the user information; For the case information of each category, determine the case information that matches the user information of that category; Based on the number of the matched case information and the number of multiple target case information, determine the confidence level between the user information of the category and the case information of each category.

[0128] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: Based on the confidence level between the user information of the category and the case information of each category, and the support degree between the user information of the category and the case information of each category, determine the lift degree between the user information of the category and the case information of each category; Associate the case information and user information corresponding to the lift degree that meets the second preset condition, and generate target association information; the second preset condition includes that the support degree is greater than the support degree threshold, the confidence level is much greater than the confidence level threshold, and the lift degree is greater than the lift degree threshold.

[0129] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: Process the multiple user information and the multiple case information according to the preset key fields, and obtain the processed multiple user information and the processed multiple case information; Classify the multiple user information to obtain the user information of each category, and classify the multiple case information to obtain the case information of each category, including: Classify the processed multiple user information to obtain the user information of each category, and classify the processed multiple case information to obtain the case information of each category.

[0130] The implementation principle and technical effect of a computer program product provided by the above embodiment are similar to those of the above method embodiment, and will not be described in detail here.

[0131] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memories can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0132] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0133] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for predicting associated information, characterized in that The method includes: Obtaining a plurality of user information and a plurality of case information; Classifying the plurality of user information to obtain user information of each category, and classifying the plurality of case information to obtain case information of each category; Predicting the association relationship between the user information of each category and the case information of each category to obtain target association information.

2. The method according to claim 1, characterized in that, The predicting the association relationship between the user information of each category and the case information of each category to obtain target association information includes: For the user information of each category, determining the support degree between the user information of the category and the case information of each category; Associating the case information and user information corresponding to the support degree that meets the support degree threshold, and generating the target association information.

3. The method according to claim 2, wherein The determining the support degree between the user information of the category and the case information of each category includes: For the case information of each category, determining the case information that matches the user information of the category; According to the number of the matching case information and the number of the plurality of case information, determining the support degree between the user information of the category and the case information of each category.

4. The method according to claim 2 or 3, characterized in that, The method further includes: For the user information of each category, determining the confidence degree between the user information of the category and the case information of each category; Associating the case information and user information corresponding to the confidence degree that meets the first preset condition, and generating the target association information; the first preset condition includes that the support degree is greater than the support degree threshold and the confidence degree is much greater than the confidence degree threshold.

5. The method according to claim 4, characterized in that The determining the confidence degree between the user information of each category and the case information of each category includes: Screening out a plurality of target case information from the plurality of case information; the category of the target case information matches the category of the user information; For the case information of each category, determining the case information that matches the user information of each category; According to the number of the matching case information and the number of the plurality of target case information, determining the confidence degree between the user information of the category and the case information of each category.

6. The method according to claim 3, wherein The method further includes: According to the confidence degree between the user information of the category and the case information of each category, and the support degree between the user information of the category and the case information of each category, determining the promotion degree between the user information of the category and the case information of each category; Associating the case information and user information corresponding to the promotion degree that meets the second preset condition, and generating the target association information; the second preset condition includes that the support degree is greater than the support degree threshold, the confidence degree is much greater than the confidence degree threshold, and the promotion degree is greater than the promotion degree threshold.

7. The method according to claim 1, characterized in that, The method further includes: Processing the plurality of user information and the plurality of case information according to preset keyword fields to obtain processed plurality of user information and processed plurality of case information; The classifying the plurality of user information to obtain user information of each category, and classifying the plurality of case information to obtain case information of each category includes: Classify the processed multiple user information to obtain user information of each category, and classify the processed multiple case information to obtain case information of each category.

8. A prediction device for associated information, characterized in that, The device includes: An acquisition module, configured to acquire multiple user information and multiple case information; A classification module, configured to classify the multiple user information to obtain user information of each category, and classify the multiple case information to obtain case information of each category; A prediction module, configured to predict the association relationship between the user information of each category and the case information of each category to obtain target association information.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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