Labor management system based on big data

Abstract

The invention relates to the field of data management, in particular to a labor management system based on big data, comprising a user supervision module used for periodically responding to a target evaluation condition to determine whether to perform access behavior analysis for each target service user; the access analysis module is used for responding to the evaluation analysis condition to determine a behavior analysis strategy of each to-be-evaluated user; the user evaluation module is used for executing the behavior analysis strategy determined by the access analysis module to obtain behavior abnormal parameters of the to-be-evaluated users; the limitation analysis module is connected with the user evaluation module and used for responding to limitation judgment conditions so as to determine whether information access limitation is carried out on the to-be-evaluated users determined by the completion behavior abnormal parameters or not; and the execution restriction module is used for responding to the execution restriction condition and carrying out display information compression on various alternative employment information of the access restriction user, and the safety of the employment information stored in the employment management system is improved.

Description

Technical Field

[0001] The present invention relates to the field of data management, and in particular to an employment management system based on big data. Background Art

[0002] Through big data analysis, the employment management system can effectively provide reliable and effective employment information recommendations to employers, effectively improving the efficiency of personnel recruitment for employers. For employment data managers who provide employment management services, if the sources and detailed information of a large number of workers they have are completely open to employers, they can neither guarantee the efficiency of personnel retrieval for employers nor their own data security. Therefore, how to monitor the actual behavior of employers in personnel retrieval in real time and adjust the access scope of employers in a timely manner to ensure the security of the labor data of employment data managers is an urgent problem to be solved by technical personnel in this field.

[0003] Chinese patent publication number CN118172029A discloses a blockchain-based employment information sharing platform, comprising several nodes that jointly build an equal-identity alliance chain network. Each node runs a labor information sharing system, which includes a flexible task crowdsourcing subsystem, a flexible salary settlement subsystem, a user identity management subsystem, an IPFS distributed file system, and a MongoDB distributed database. This system can complete the entire process of flexible employment, salary settlement, batch payment, invoice provision, and legal tax payment in one stop. However, the above solution has the following flaws: it fails to analyze the actual behavioral information of the labor demander during personnel search and timely adjust the access scope of the labor demander, resulting in the inability to guarantee the security of the labor data management party's employee data. Summary of the Invention

[0004] To this end, the present invention provides an employment management system based on big data to overcome the problem that the existing technology fails to analyze the actual behavioral information of the employment demander when searching for personnel and timely adjust the access scope of the employment demander, resulting in low security of the employee data of the employment data manager.

[0005] To achieve the above objectives, the present invention provides a big data-based employment management system, comprising:

[0006] A user supervision module is used to periodically determine the abnormality of demand changes of each target service user based on the access tendency change coefficient and the access difference coefficient, and respond to the target evaluation conditions to determine whether to perform access behavior analysis for each target service user;

[0007] an access analysis module connected to the user supervision module and configured to respond to the evaluation and analysis conditions to determine a behavior analysis strategy for each user to be evaluated, which is a setting method for determining the behavior abnormality parameters of the user to be evaluated based on the user access stability coefficient, or determining the behavior abnormality parameters of the user to be evaluated based on the execution interval stability coefficient and the execution depth stability coefficient;

[0008] A user evaluation module, connected to the access analysis module, comprising a first user evaluation unit and a second user evaluation unit, configured to execute the behavior analysis strategy determined by the access analysis module to obtain behavior abnormality parameters of each user to be evaluated;

[0009] a restriction analysis module connected to the user evaluation module and configured to respond to restriction determination conditions to determine whether to impose information access restrictions on each user to be evaluated who has completed determination of abnormal behavior parameters;

[0010] The restriction execution module is connected to the restriction analysis module and is used to respond to the restriction execution conditions and compress the display information of various alternative job selection information of the restricted access users.

[0011] Furthermore, if the target evaluation condition responded by the user supervision module is that the abnormality of the demand change of the target service user is greater than the preset abnormality of the demand change, the access analysis module is determined to perform access behavior analysis on the target service user;

[0012] The demand change abnormality is positively correlated with the access tendency change coefficient and the access difference coefficient.

[0013] Furthermore, the access analysis module responds to the behavior analysis conditions and determines a behavior analysis strategy for each user to be evaluated based on the richness of the user's access tendency and the reference tendency change coefficient;

[0014] The behavior analysis condition is that there is a target service user who needs to undergo access behavior analysis, and the target service user who needs to undergo access behavior analysis is recorded as a user to be evaluated.

[0015] Furthermore, if the evaluation and analysis condition responded by the access analysis module is that the access tendency richness of the user to be evaluated is less than or equal to the preset access tendency richness and the reference tendency change coefficient is less than or equal to the preset reference tendency change coefficient, then it is determined that the first user evaluation unit determines the setting method of the behavior abnormality parameter of the user to be evaluated according to the user access stability coefficient;

[0016] The user access stability coefficient is determined according to an access depth difference index and an access duration difference index.

[0017] Furthermore, the abnormal setting condition responded by the first user evaluation unit is that there is a user access stability coefficient of the user to be evaluated that is greater than a preset user access stability coefficient, then the behavioral abnormality parameter of the user to be evaluated is determined according to the access preference change index and the access parameter change index;

[0018] If the abnormal setting condition responded by the first user evaluation unit is that the user access stability coefficient of the user to be evaluated is less than or equal to the user access stability coefficient, the behavior abnormality parameter of the user to be evaluated is determined according to the access overlap index and the overlap conflict index.

[0019] Furthermore, if the evaluation analysis condition responded by the access analysis module is that the access tendency richness of the user to be evaluated is greater than a preset access tendency richness or the reference tendency change coefficient is greater than a preset reference tendency change coefficient, then it is determined that the second user evaluation unit determines the behavior abnormality parameter of the user to be evaluated based on the execution interval stability coefficient and the execution depth stability coefficient;

[0020] The behavioral abnormality parameters are positively correlated with the execution interval stability coefficient and the execution depth stability coefficient respectively.

[0021] Furthermore, the restriction analysis module responds to the evaluation completion condition and determines whether to restrict information access for each user to be evaluated who has completed the determination of the behavior abnormality parameter according to the behavior abnormality parameter and the trend abnormality parameter;

[0022] For a single user to be evaluated who has completed determination of abnormal behavior parameters, if the restriction determination condition responded by the restriction analysis module is that the abnormal behavior parameter is greater than a preset abnormal behavior parameter or the abnormal trend parameter is greater than a preset abnormal trend parameter, then it is determined that information access restriction is imposed on the user to be evaluated;

[0023] The evaluation completion condition is that abnormal behavior parameters of the user to be evaluated are determined.

[0024] Furthermore, the restriction execution module compresses display information for various candidate job information of the restricted access user in response to the restriction execution condition, including:

[0025] Filter the associated stage information of each candidate recruitment information, and test the adaptation recommendation coefficient of each candidate recruitment information;

[0026] Restrict access to users, allowing them to view only the associated stage information and adaptation recommendation coefficients of their candidate recruitment information;

[0027] The restriction execution condition is that there is a user to be evaluated who needs to be restricted in information access, and the user to be evaluated who needs to be restricted in information access is recorded as a restricted access user.

[0028] Furthermore, the restriction execution module determines the associated stage information of each candidate recruitment information based on the employment scenario association degree and the employment task association degree;

[0029] An adaptation recommendation coefficient of each candidate job information is determined according to the associated operation parameters and the evaluation effectiveness parameters, and the adaptation recommendation coefficient is positively correlated with the associated operation parameters and the evaluation effectiveness parameters respectively.

[0030] Furthermore, the restriction execution module determines the evaluation validity parameters of each associated stage information based on the valid evaluation ratio and the evaluation conflict parameter;

[0031] If the associated restriction condition of the restriction execution module response is that the evaluation effective parameter of the associated stage information is less than or equal to the preset evaluation effective parameter, then it is determined that the associated stage information is not used in the determination process of the adaptation recommendation coefficient of the corresponding alternative job information, and the relevant content of the associated stage information is not displayed.

[0032] Compared with the prior art, the beneficial effect of the present invention lies in that the technical solution of the present invention determines a targeted behavior analysis strategy based on the access tendency richness and the reference tendency change coefficient when the demand change abnormality of the target service user is large, ensuring that the behavior analysis process of the user to be evaluated is more in line with its actual access status. While ensuring the reliability of the determination results of the behavior abnormality parameters, the analysis efficiency of the behavior analysis process is improved, and information access restrictions are imposed on the users to be evaluated with large behavior abnormality parameters or large trend abnormality parameters, thereby improving the security of the employment information stored in the employment management system.

[0033] Furthermore, the present invention determines the abnormality of demand changes of each target service user based on the access tendency change coefficient and the access difference coefficient, so as to preliminarily determine whether there is a sudden change in employment demand in the recent period, and thereby determine whether there is a risk of abnormal access behavior. The abnormality of demand change is used to determine whether to further analyze the access behavior, thereby avoiding unnecessary data analysis process while ensuring the security of the employment information stored in the employment management system.

[0034] Furthermore, in the present invention, for the user to be evaluated who needs to undergo access behavior analysis, a targeted behavior analysis strategy is determined based on the richness of his access tendency and the reference tendency change coefficient. The access tendency richness and the reference tendency change coefficient are used to characterize whether the user to be evaluated has obvious access tendency, and the behavior analysis strategy is determined based on this. While ensuring the accuracy of the judgment of the abnormal access behavior of the user to be evaluated, the analysis efficiency of the behavior analysis process of the user to be evaluated is guaranteed.

[0035] Furthermore, the present invention aims at the users to be evaluated whose access tendency richness and reference tendency change coefficient are both small. Since such users to be evaluated often have relatively obvious access tendency characteristics for the accessed information, the access content is analyzed to determine whether there is a possibility of malicious crawler behavior in the access process, and the access depth difference index and the access time difference index are used to determine whether there is obvious regularity in the access behavior of the users to be evaluated, so as to determine the setting method of targeted behavior anomaly parameters, thereby further ensuring the reliability of the evaluation results of the behavioral anomalies of the users to be evaluated.

[0036] Furthermore, in the present invention, for users to be evaluated with a large access tendency richness or a large reference tendency change coefficient, since such users to be evaluated do not have more obvious access tendency characteristics, the execution interval stability coefficient and the execution depth stability coefficient during their access process are analyzed to determine whether there is a risk of malicious crawlers, so as to ensure the efficiency of the analysis of abnormal behavior of users to be evaluated and the reliability of the evaluation results.

[0037] Furthermore, the present invention determines whether to impose information access restrictions on each user to be evaluated whose behavior abnormality parameters have been determined based on the behavior abnormality parameters and the trend abnormality parameters. Not only is information access restricted only for users to be evaluated whose behavior abnormality parameters are larger, but information access restrictions are also imposed on users to be evaluated whose behavior abnormality parameters continue to rise, thereby further improving the security of the information of workers in the employment management system.

[0038] Furthermore, when restricting information access for the user to be evaluated, the present invention processes the alternative job information accessible to the user, compresses the completeness of the information displayed to the user, and only displays the information content corresponding to the work experience that matches the user's current access needs during the compression process, and outputs the adaptation recommendation parameters through the corresponding evaluation content. While ensuring the security of the worker information of the employment management system, the matching efficiency of the user to be evaluated when conducting employment is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a module connection diagram of the big data-based employment management system of the present invention;

[0040] Figure 2 This is a module structure diagram of the user evaluation module of the present invention;

[0041] Figure 3 A flowchart of the access analysis module of the present invention responding to the evaluation analysis conditions to determine the behavior analysis strategy of each user to be evaluated;

[0042] Figure 4This is a flowchart of the restriction analysis module of the present invention responding to restriction determination conditions to determine whether to restrict information access for a user to be evaluated. DETAILED DESCRIPTION

[0043] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0044] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0045] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0046] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0047] See also Figures 1 to 4 As shown, the present invention provides a big data-based employment management system, comprising:

[0048] A user supervision module is used to periodically determine the abnormality of demand changes of each target service user based on the access tendency change coefficient and the access difference coefficient, and respond to the target evaluation conditions to determine whether to perform access behavior analysis for each target service user;

[0049] an access analysis module connected to the user supervision module and configured to respond to the evaluation and analysis conditions to determine a behavior analysis strategy for each user to be evaluated, which is a setting method for determining the behavior abnormality parameters of the user to be evaluated based on the user access stability coefficient, or determining the behavior abnormality parameters of the user to be evaluated based on the execution interval stability coefficient and the execution depth stability coefficient;

[0050] A user evaluation module, connected to the access analysis module, comprising a first user evaluation unit and a second user evaluation unit, configured to execute the behavior analysis strategy determined by the access analysis module to obtain behavior abnormality parameters of each user to be evaluated;

[0051] a restriction analysis module connected to the user evaluation module and configured to respond to restriction determination conditions to determine whether to impose information access restrictions on each user to be evaluated who has completed determination of abnormal behavior parameters;

[0052] The restriction execution module is connected to the restriction analysis module and is used to respond to the restriction execution conditions and compress the display information of various alternative job selection information of the restricted access users.

[0053] Among them, the present invention is used to ensure the security of the worker information of the employment management system. The target service user is a user for whom the employment management system of the present invention provides employment recommendation services. The employment management system can open the access rights of the preliminary matched worker information to the target service user according to the actual employment needs of the target service user, so as to save the screening efficiency of the target service user, and record the worker information determined by the preliminary matching as the alternative employment information. Before the display information is compressed, how to perform preliminary matching of the worker information according to the actual needs of the target service user is already known to those skilled in the art and will not be elaborated here. The target service user can normally access the complete worker information, including but not limited to: basic personal information, work experience and work evaluation content corresponding to each work stage;

[0054] The present invention applies several service monitoring records, and any service monitoring record records the access stay duration, tendency change index, demand change abnormality, access tendency richness, reference tendency change coefficient, user access stability coefficient, abnormal parameter difference, behavior abnormality parameter, trend abnormality parameter, associated operation parameter and evaluation effectiveness parameter in the process of at least one access behavior monitoring of the target service user, and each service monitoring record corresponds to a qualified mark, which records whether the security of the worker information stored in the employment system meets user requirements.

[0055] Specifically, if the target evaluation condition responded by the user supervision module is that the abnormality of the demand change of the target service user is greater than the preset abnormality of the demand change, the access analysis module is determined to perform access behavior analysis on the target service user;

[0056] The demand change abnormality is positively correlated with the access tendency change coefficient and the access difference coefficient.

[0057] Among them, the present invention applies a cyclic user supervision cycle, and the duration of the user supervision cycle can be determined by the user. The higher the user's requirements for the security of the worker information stored in the employment system, the shorter the user supervision cycle. A user supervision cycle is provided, and the user supervision cycle is 24 hours. At the end of each user supervision cycle, the abnormality of the demand change of the target service user is detected, and it is determined whether to perform access behavior analysis on each target service user;

[0058] For a single target service user, the demand change abnormality is the sum of the access tendency change coefficient and the access difference coefficient. The access tendency change coefficient = the number of changes in access information of the target service user in the current user supervision cycle / the number of reference access information of the target service user in the current user supervision cycle. The reference access information is the worker information whose access stay duration is greater than the preset access stay duration. The changed access information is the reference access information whose tendency change index is greater than the preset tendency change index. For the information of a single worker, the access stay duration is the sum of the length of stay on each page where the worker information is located in the current user supervision cycle.

[0059] For a single target service user, at the end of the current user supervision cycle, the demand keywords contained in the worker information accessed by the target service user in the current user supervision cycle are obtained and extracted. For a single worker information, the tendency change index = the number of demand keywords contained in the worker information / the number of changed demand keywords contained in the worker information, the access difference coefficient = the number of different demand keywords contained in each changed access information / the number of overlapping demand keywords. For a single demand keyword, if the demand keyword does not exist in any reference access information in the access evaluation stage, the demand keyword is recorded as a changed demand keyword. If the number of changed access information with the demand keyword is greater than the preset overlap index, the demand keyword is recorded as an overlap. Demand keywords, the value of the preset overlap index, can be determined by the user according to the actual work scenario. For example, the user can set it according to the service monitoring record. The higher the user's requirements for the security of the worker information stored in the employment system, the smaller the value of the preset overlap index. A value of the preset overlap index is provided, and the value of the preset overlap index is 5. How to identify the demand keywords contained in the worker information is a content that those skilled in the art have mastered and will not be elaborated here. The user can set the content of the demand keywords according to the actual work scenario. For example, the target service user is a construction company, and the demand keywords include but are not limited to: electric drill, cutting machine, electric welding, bricklaying, concrete pouring, bricklaying, scaffolding construction, construction-related certificates, welder certificate, safety officer certificate and special operation operation certificate;

[0060] The values ​​of the preset access stay time and the preset tendency change index can be determined by the user according to the actual work scenario. For example, the user can set them according to the service monitoring record. The higher the user's requirements for the security of the migrant worker information stored in the employment system, the larger the value of the preset access stay time and the larger the value of the preset tendency change index. A method for determining the value of the preset access stay time is provided, and the minimum value of the access stay time of each changed reference access information in the service monitoring record that meets the user's requirements for the security of the migrant worker information stored in the employment system is recorded as the preset access stay time. A method for determining the value of the preset tendency change index is provided, and the average value of the tendency change index of each changed access information in the service monitoring record that meets the user's requirements for the security of the migrant worker information stored in the employment system is recorded as the preset tendency change index.

[0061] The value of the preset demand change abnormality can be determined by the user according to the actual work scenario. For example, the user can set it according to the service monitoring record. The higher the user's requirements for the security of the worker information stored in the employment system, the smaller the value of the preset demand change abnormality and the larger the value of the preset access stay time. A method for determining the value of the preset demand change abnormality is provided, and the average value of the demand change abnormality of each user to be evaluated in the service monitoring record that meets the user's requirements for the security of the worker information stored in the employment system is recorded as the preset demand change abnormality.

[0062] Specifically, the access analysis module responds to the behavior analysis conditions and determines the behavior analysis strategy of each user to be evaluated based on the access tendency richness of the user to be evaluated and the reference tendency change coefficient;

[0063] The behavior analysis condition is that there is a target service user who needs to undergo access behavior analysis, and the target service user who needs to undergo access behavior analysis is recorded as a user to be evaluated.

[0064] Among them, for a single user to be evaluated, the access tendency richness is the number of different demand keywords contained in the worker information accessed by the user to be evaluated during the access evaluation stage, the reference tendency change coefficient is the average value of the access tendency change coefficient of the user to be evaluated determined by each user supervision cycle during the access evaluation stage, the end time of the access evaluation stage is the end time of the current user supervision cycle, and the duration of the access evaluation stage can be set by the user according to the actual work scenario. The higher the user's requirements for the security of the worker information stored in the employment system, the longer the duration of the access evaluation stage. A value for the duration of the access evaluation stage is provided, and the duration of the access evaluation stage is 6 times the user supervision cycle.

[0065] Specifically, the evaluation and analysis condition responded by the access analysis module is that the access tendency richness of the user to be evaluated is less than or equal to the preset access tendency richness and the reference tendency change coefficient is less than or equal to the preset reference tendency change coefficient, then it is determined that the first user evaluation unit determines the setting method of the behavior abnormality parameter of the user to be evaluated according to the user access stability coefficient;

[0066] The user access stability coefficient is determined according to an access depth difference index and an access duration difference index.

[0067] Among them, the values ​​of the preset access tendency richness and the preset reference tendency change coefficient can be determined by the user according to the actual work scenario. For example, the user can set them according to the service monitoring record, and a method for determining the value of the preset access tendency richness is provided, and the service monitoring record for determining the setting method of the abnormal behavior parameters of the user to be evaluated according to the user access stability coefficient is recorded as the evaluation reference record, and the maximum value of the access tendency richness of the user to be evaluated in the evaluation reference record that meets the user's security requirements for the worker information stored in the employment system is recorded as the preset access tendency richness, and a method for determining the value of the preset reference tendency change coefficient is provided, and the maximum value of the reference tendency change coefficient of the user to be evaluated in the evaluation reference record that meets the user's security requirements for the worker information stored in the employment system is recorded as the preset reference tendency change coefficient;

[0068] For a single user to be evaluated, if the access tendency richness of the user to be evaluated is less than or equal to the preset access tendency richness and the reference tendency change coefficient is less than or equal to the preset reference tendency change coefficient, then the setting method of the behavior abnormality parameter of the user to be evaluated is determined according to the user access stability coefficient. The small access tendency richness and the reference tendency change coefficient can indicate that the user to be evaluated has a more obvious tendency in accessing the information of migrant workers. Therefore, by analyzing and evaluating the situation of the migrant worker information accessed by the subject to be evaluated during the user supervision period, there is a risk of malicious crawler behavior.

[0069] For a single user to be evaluated, the user access stability coefficient = 1 / (access depth difference index + access time difference index), the access depth difference index a is the number of worker information accessed by the user to be evaluated during the access evaluation phase, fj is the access depth parameter corresponding to the j-th access to the worker information by the user to be evaluated during the access evaluation phase, f0 is the average value of the access depth parameter corresponding to each access to the worker information by the user to be evaluated during the access evaluation phase. For a single worker information, the access depth parameter is the number of pages accessed during the access to the worker information, and the access time difference value is b is the number of pages visited by the user to be evaluated during the access evaluation stage, te is the length of time the user to be evaluated stays on the e-th page visited during the access evaluation stage, and t0 is the average length of time the user to be evaluated stays on each page visited during the access evaluation stage.

[0070] Specifically, the abnormal setting condition responded by the first user evaluation unit is that the user access stability coefficient of the user to be evaluated is greater than the preset user access stability coefficient, and the behavioral abnormality parameter of the user to be evaluated is determined according to the access preference change index and the access parameter change index;

[0071] The abnormal setting condition responded by the first user evaluation unit is that the user access stability coefficient of the user to be evaluated is less than or equal to the user access stability coefficient, and the behavior abnormality parameter of the user to be evaluated is determined according to the access overlap index and the overlap conflict index.

[0072] The value of the preset user access stability coefficient can be determined by the user according to the actual work scenario. For example, the user can set it according to the service monitoring record, and a method for determining the value of the preset user access stability coefficient is provided. The service monitoring record for determining the abnormal behavior parameters of the user to be evaluated according to the access overlap index and the overlap conflict index is recorded as a stable reference record. The minimum value of the user access stability coefficient of the user to be evaluated in the stable reference record that meets the user's security requirements for the worker information stored in the employment system is recorded as the preset user access stability coefficient.

[0073] For a single user to be evaluated, if the user access stability coefficient of the user to be evaluated is greater than the preset user access stability coefficient, it indicates that the user to be evaluated has a relatively regular access behavior to the accessed information of the migrant workers in the recent access process. Therefore, by combining the access behavior and the abnormalities in the access content, it is determined that the user to be evaluated has the risk of malicious crawler behavior. The behavior abnormality parameter is positively correlated with the access change index. The access change index is the product of the access preference change index and the access parameter change index. The access preference change index = the number of overlapping demand keywords in the change demand keywords determined in the current user supervision cycle / the number of change demand keywords determined in the current user supervision cycle. The access parameter change index is the access depth change. The sum of the access depth and the access duration change degree, the access depth change degree = | the average value of the access depth parameters corresponding to each time the user to be evaluated accesses the migrant worker information during the access evaluation phase - the average value of the access depth parameters corresponding to each time the user to be evaluated accesses the migrant worker information during the current employment supervision cycle | / the average value of the access depth parameters corresponding to each time the user to be evaluated accesses the migrant worker information during the access evaluation phase; the access duration change degree = | the average value of the stay time of each page visited by the user to be evaluated during the access evaluation phase - the average value of the stay time of each page visited by the user to be evaluated during the current user supervision cycle | / the average value of the stay time of each page visited by the user to be evaluated during the access evaluation phase;

[0074] For a single user to be evaluated, if the user access stability coefficient of the user to be evaluated is less than or equal to the preset user access stability coefficient, it indicates that there is no obvious pattern in the access behavior of the user to be evaluated to the migrant worker information visited in the recent access process. Therefore, only by analyzing the content of the migrant worker information visited, the risk of malicious crawler behavior of the user to be evaluated is determined. The behavioral abnormality parameter is negatively correlated with the access overlap quality coefficient, overlap quality coefficient = access overlap index / overlap conflict index, access overlap index = the number of overlapping keywords obtained in the current user supervision cycle / the number of different demand keywords in the migrant worker information visited in the current user supervision cycle, the overlap conflict index = the number of conflicting overlapping keywords / the number of overlapping keywords obtained in the current user supervision cycle, for a single demand keyword, if the number of migrant worker information with the demand keyword visited by the user to be evaluated in the current user supervision cycle is greater than the preset overlap index, then the demand keyword is recorded as an overlapping keyword, for a single overlapping keyword, if it does not exist in any migrant worker information in the access evaluation stage, then the overlapping keyword is recorded as a conflicting overlapping keyword.

[0075] Specifically, if the evaluation analysis condition responded by the access analysis module is that the access tendency richness of the user to be evaluated is greater than the preset access tendency richness or the reference tendency change coefficient is greater than the preset reference tendency change coefficient, then the second user evaluation unit determines the behavior abnormality parameter of the user to be evaluated based on the execution interval stability coefficient and the execution depth stability coefficient;

[0076] The behavioral abnormality parameters are positively correlated with the execution interval stability coefficient and the execution depth stability coefficient respectively.

[0077] Among them, for a single user to be evaluated, if the access tendency richness of the user to be evaluated is greater than the preset access tendency richness or the reference tendency change coefficient is greater than the preset reference tendency change coefficient, then the behavioral abnormality parameter of the user to be evaluated is determined according to the execution interval stability coefficient and the execution depth stability coefficient. A larger access tendency richness or a larger reference tendency change coefficient indicates that the user to be evaluated does not have a more obvious tendency in the process of accessing the migrant workers' information, that is, it is impossible to judge whether there is an access abnormality by the content of the accessed information. Therefore, the execution interval stability coefficient and the execution depth stability coefficient are used to characterize whether there is a malicious crawler risk in the access process of the user to be evaluated;

[0078] For the user to be evaluated whose single access tendency richness is greater than the preset access tendency richness or whose reference tendency change coefficient is greater than the preset reference tendency change coefficient, the behavior abnormality parameter is positively correlated with the operation abnormality coefficient, and the operation abnormality coefficient = execution interval stability coefficient + execution depth stability coefficient. The moment when the user to be evaluated performs the access operation within the current user supervision cycle is obtained, and the moment when the access operation is performed is recorded as the access execution moment. For any two access execution moments, if there is no other access execution moment between the above two access execution moments, the above two access execution moments are recorded as a group of adjacent access moments. The interval length between each group of adjacent access moments in the user supervision cycle is detected and recorded as the execution interval length corresponding to each group of adjacent access moments. The execution interval stability coefficient n is the number of times the user to be evaluated performs access operations in the current user supervision cycle, zi is the execution interval length of the i-th group of adjacent access moments in the current user supervision cycle, the average execution interval length of each group of adjacent access moments in the current user supervision cycle, and the execution depth stability coefficient m is the number of migrant worker information accessed by the user to be evaluated during the current user supervision cycle, sk is the access depth parameter corresponding to the kth access to the migrant worker information during the current user supervision cycle, and s0 is the average value of the access depth parameters corresponding to each access to the migrant worker information during the current user supervision cycle. The access operation in the present invention is the access page switching by the user to be evaluated.

[0079] Specifically, the restriction analysis module responds to the evaluation completion condition and determines whether to restrict information access for each user to be evaluated who has completed the determination of the behavior abnormality parameter based on the behavior abnormality parameter and the trend abnormality parameter;

[0080] For a single user to be evaluated who has completed determination of abnormal behavior parameters, if the restriction determination condition responded by the restriction analysis module is that the abnormal behavior parameter is greater than a preset abnormal behavior parameter or the abnormal trend parameter is greater than a preset abnormal trend parameter, then it is determined that information access restriction is imposed on the user to be evaluated;

[0081] The evaluation completion condition is that abnormal behavior parameters of the user to be evaluated are determined.

[0082] Among them, for a single user to be evaluated, the trend abnormality parameter is determined based on the behavioral abnormality parameters determined for the user to be evaluated each time during the trend evaluation phase. The trend abnormality parameter is the product of the abnormal parameter change degree and the change duration ratio. The abnormal parameter change degree = the absolute value of the difference between the maximum and minimum values ​​of the behavioral abnormality parameter determined for the user to be evaluated during the trend evaluation phase / the minimum value of the behavioral abnormality parameter determined for the user to be evaluated during the trend evaluation phase. The change duration ratio = the number of change supervision cycles during the trend evaluation phase / the number of user supervision cycles during the trend evaluation phase. The abnormal parameter difference of each user supervision cycle during the trend evaluation phase is detected, and the user supervision cycle with an abnormal parameter difference greater than a preset abnormal parameter difference is recorded as a change supervision cycle. For a single user supervision cycle, the abnormal parameter difference = (the behavioral abnormality parameter determined at the end time of the user supervision cycle - the behavioral abnormality parameter determined at the end time of the user supervision cycle before the user supervision cycle) / the behavioral abnormality parameter determined at the end time of the user supervision cycle before the user supervision cycle;

[0083] The value of the preset abnormal parameter difference can be determined by the user according to the actual work scenario. For example, the user can set it according to the service monitoring record. The higher the user's requirement for the security of the worker information stored in the employment system, the smaller the value of the preset abnormal parameter difference. A method for determining the value of the preset abnormal parameter difference is provided. The average value of the abnormal parameter difference of each change supervision cycle in the service monitoring record that meets the user's security requirement for the worker information stored in the employment system is recorded as the preset abnormal parameter difference. The end time of the trend evaluation phase is the end time of the current user supervision cycle. The value of the duration of the trend evaluation phase can be set by the user according to the actual work scenario. The higher the user's requirement for the security of the worker information stored in the employment system, the longer the value of the duration of the trend evaluation phase. A value of the duration of the trend evaluation phase is provided. The duration of the trend evaluation phase is 8 times the user supervision cycle.

[0084] For a single user to be evaluated, if the behavior anomaly parameter is greater than the preset behavior anomaly parameter, it directly indicates that the user to be evaluated is more likely to have malicious crawler behavior, and information access restrictions will be imposed on the user to be evaluated. However, for users to be evaluated whose behavior anomaly parameter is less than or equal to the preset behavior anomaly parameter, it is necessary to further analyze whether the behavior anomaly parameter of the user to be evaluated has increased significantly or continuously, that is, to obtain the trend anomaly parameter of the user to be evaluated. If the trend anomaly parameter is greater than the preset trend anomaly parameter, information access restrictions will also be imposed on the user to be evaluated. If the behavior anomaly parameter of the user to be evaluated is less than or equal to the preset behavior anomaly parameter and the trend anomaly parameter is less than or equal to the preset trend anomaly parameter, information access restrictions will not be imposed on the user to be evaluated.

[0085] The values ​​of the preset behavior abnormality parameters and the preset trend abnormality parameters can be determined by the user according to the actual work scenario. For example, the user can set them according to the service monitoring records. The higher the user's requirements for the security of the worker information stored in the employment system, the smaller the value of the preset behavior abnormality parameters and the smaller the value of the preset trend abnormality parameters. A method for determining the values ​​of the preset behavior abnormality parameters is provided, and the service monitoring records that do not restrict information access for the user to be evaluated are recorded as restriction reference records, and the maximum value of the behavior abnormality parameters of each person to be evaluated in the restriction reference records that meet the user's requirements for the security of the worker information stored in the employment system is recorded as the preset behavior abnormality parameters. A method for determining the values ​​of the preset trend abnormality parameters is provided, and the maximum value of the trend abnormality parameters of each person to be evaluated in the restriction reference records that meet the user's requirements for the security of the worker information stored in the employment system is recorded as the preset trend abnormality parameters.

[0086] Specifically, the restriction execution module responds to the restriction execution condition and compresses the display information of various candidate job selection information of the restricted access user, including:

[0087] Filter the associated stage information of each candidate recruitment information, and test the adaptation recommendation coefficient of each candidate recruitment information;

[0088] Restrict access to users, allowing them to view only the associated stage information and adaptation recommendation coefficients of their candidate recruitment information;

[0089] The restriction execution condition is that there is a user to be evaluated who needs to be restricted in information access, and the user to be evaluated who needs to be restricted in information access is recorded as a restricted access user.

[0090] Specifically, the restriction execution module determines the associated stage information of each candidate recruitment information based on the employment scenario association degree and the employment task association degree;

[0091] An adaptation recommendation coefficient of each candidate job information is determined according to the associated operation parameters and the evaluation effectiveness parameters, and the adaptation recommendation coefficient is positively correlated with the associated operation parameters and the evaluation effectiveness parameters respectively.

[0092] Among them, for any alternative employment information of a single restricted access user, the associated operation parameters of each working stage in the alternative employment information are determined according to the employment scenario correlation and the employment operation correlation, and the work evaluation content corresponding to the working stage whose associated operation parameter is greater than the preset associated operation parameter is recorded as the associated stage information. The adaptation recommendation coefficient is the sum of the products of the associated operation parameters of each associated stage information of the alternative employment information and their corresponding evaluation weight indexes. For a single associated stage information, the evaluation weight index is positively correlated with its evaluation validity parameter;

[0093] For a single working stage, the associated operation parameter is the sum of the working scene correlation and the working operation correlation of the working stage. If the working scene corresponding to the working stage and the working scene corresponding to the working demand of the restricted access user are in the same scene category, the working scene correlation is recorded as 1; if the working scene corresponding to the working stage and the working scene corresponding to the working demand of the restricted access user are not in the same scene category, the working scene correlation is recorded as 0; if the operation content corresponding to the working stage and the operation content corresponding to the working demand of the restricted access user are in the same operation category, the working operation correlation is recorded as 1; if the operation content corresponding to the working stage and the operation content corresponding to the working demand of the restricted access user are not in the same operation category, the working operation correlation is recorded as 0;

[0094] How to determine whether the work scenes are in the same scene category and how to determine whether the job content is in the same job category are contents that are easy to understand for technical personnel in this field and will not be elaborated here. Users can determine the division results of scene categories and job categories according to actual needs. The present invention provides a method for dividing scene categories and job categories. For example, outdoor construction scenes include construction site scenes, road and bridge construction scenes, and demolition construction scenes. Production and manufacturing scenes include workshop operation scenes and assembly line operation scenes. Production and manufacturing operation categories include mechanical processing, assembly and quality inspection.

[0095] The value of the preset associated operation parameter can be determined by the user according to the actual work scenario. For example, the user can set it according to the service monitoring record. The higher the user's requirement for the security of the worker information stored in the employment system, the larger the value of the preset associated operation parameter. A method for determining the value of the preset associated operation parameter is provided, and the average value of the associated operation parameters of the associated stage information in the service monitoring record that meets the user's requirement for the security of the worker information stored in the employment system is recorded as the preset associated operation parameter.

[0096] Specifically, the restriction execution module determines the evaluation validity parameters of each associated stage information based on the valid evaluation ratio and the evaluation conflict parameter;

[0097] The associated restriction condition responded by the restriction execution module is that the evaluation valid parameter of the associated stage information is less than or equal to the preset evaluation valid parameter, then it is determined that the associated stage information is not used in the determination process of the adaptation recommendation coefficient of the corresponding alternative job information, and the relevant content of the associated stage information is not displayed.

[0098] For a single associated stage information, the evaluation validity parameter = ln(valid evaluation ratio / evaluation conflict parameter), the valid evaluation ratio = / the number of evaluation information output by the target service user corresponding to the associated stage information / the number of evaluation information output by the target service user corresponding to the associated stage information, and the evaluation conflict parameter = |the associated evaluation parameter of the associated stage information - the average value of the associated evaluation parameters of each employment stage information of the alternative recruitment information corresponding to the associated stage information| / the average value of the associated evaluation parameters of each employment stage information of the alternative recruitment information corresponding to the associated stage information;

[0099] For a single associated stage information, if the evaluation effective parameter is less than or equal to the preset evaluation effective parameter, it is determined that the associated stage information is not used in the process of determining the adaptation recommendation coefficient of the corresponding alternative recruitment information, and the relevant content of the associated stage information is not displayed. If the evaluation effective parameter is greater than the preset evaluation effective parameter, it is determined that the associated stage information is used in the process of determining the adaptation recommendation coefficient of the corresponding alternative recruitment information. The value of the preset evaluation effective parameter can be determined by the user according to the actual work scenario. For example, the user can set it according to the service monitoring record. The higher the user's requirement for the security of the worker information stored in the employment system, the larger the value of the preset evaluation effective parameter. A method for determining the value of the preset evaluation effective parameter is provided, and the maximum value of the evaluation effective parameter of each associated stage information that is not displayed in the service monitoring record that meets the user's requirement for the security of the worker information stored in the employment system is recorded as the preset evaluation effective parameter.

[0100] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0101] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A big data-based employment management system, characterized in that: include: A user supervision module is used to periodically determine the abnormality of demand changes of each target service user based on the access tendency change coefficient and the access difference coefficient, and respond to the target evaluation conditions to determine whether to perform access behavior analysis for each target service user; an access analysis module connected to the user supervision module and configured to respond to the evaluation and analysis conditions to determine a behavior analysis strategy for each user to be evaluated, which is a setting method for determining the behavior abnormality parameters of the user to be evaluated based on the user access stability coefficient, or determining the behavior abnormality parameters of the user to be evaluated based on the execution interval stability coefficient and the execution depth stability coefficient; A user evaluation module, connected to the access analysis module, comprising a first user evaluation unit and a second user evaluation unit, configured to execute the behavior analysis strategy determined by the access analysis module to obtain behavior abnormality parameters of each user to be evaluated; a restriction analysis module connected to the user evaluation module and configured to respond to restriction determination conditions to determine whether to impose information access restrictions on each user to be evaluated who has completed determination of abnormal behavior parameters; The restriction execution module is connected to the restriction analysis module and is used to respond to the restriction execution conditions and compress the display information of various alternative job selection information of the restricted access users.

2. The big data-based employment management system according to claim 1, characterized in that: If the target evaluation condition responded by the user supervision module is that the abnormality degree of demand change of the target service user is greater than the preset abnormality degree of demand change, the access analysis module is determined to perform access behavior analysis on the target service user; The demand change abnormality is positively correlated with the access tendency change coefficient and the access difference coefficient.

3. The big data-based employment management system according to claim 2, characterized in that: The access analysis module responds to the behavior analysis conditions and determines the behavior analysis strategy of each user to be evaluated based on the access tendency richness of the user to be evaluated and the reference tendency change coefficient; The behavior analysis condition is that there is a target service user who needs to undergo access behavior analysis, and the target service user who needs to undergo access behavior analysis is recorded as a user to be evaluated.

4. The big data-based employment management system according to claim 3, characterized in that: If the evaluation analysis condition responded by the access analysis module is that the access tendency richness of the user to be evaluated is less than or equal to the preset access tendency richness and the reference tendency change coefficient is less than or equal to the preset reference tendency change coefficient, then it is determined that the first user evaluation unit determines the setting method of the behavior abnormality parameter of the user to be evaluated according to the user access stability coefficient; The user access stability coefficient is determined according to an access depth difference index and an access duration difference index.

5. The big data-based employment management system according to claim 4, characterized in that: The abnormal setting condition responded by the first user evaluation unit is that the user access stability coefficient of the user to be evaluated is greater than the preset user access stability coefficient, and the behavior abnormality parameter of the user to be evaluated is determined according to the access preference change index and the access parameter change index; If the abnormal setting condition responded by the first user evaluation unit has a user access stability coefficient of the user to be evaluated that is less than or equal to the user access stability coefficient, the behavior abnormality parameter of the user to be evaluated is determined according to the access overlap index and the overlap conflict index.

6. The big data-based employment management system according to claim 5, characterized in that: If the evaluation analysis condition responded by the access analysis module is that the access tendency richness of the user to be evaluated is greater than the preset access tendency richness or the reference tendency change coefficient is greater than the preset reference tendency change coefficient, then the second user evaluation unit determines the behavior abnormality parameter of the user to be evaluated based on the execution interval stability coefficient and the execution depth stability coefficient; The behavioral abnormality parameters are positively correlated with the execution interval stability coefficient and the execution depth stability coefficient respectively.

7. The big data-based employment management system according to claim 6, characterized in that: The restriction analysis module responds to the evaluation completion condition and determines whether to restrict information access for each user to be evaluated who has completed the evaluation of the abnormal behavior parameter and the abnormal trend parameter according to the abnormal behavior parameter; For a single user to be evaluated who has completed determination of abnormal behavior parameters, if the restriction determination condition responded by the restriction analysis module is that the abnormal behavior parameter is greater than a preset abnormal behavior parameter or the abnormal trend parameter is greater than a preset abnormal trend parameter, then it is determined that information access restriction is imposed on the user to be evaluated; The evaluation completion condition is that abnormal behavior parameters of the user to be evaluated are determined.

8. The big data-based employment management system according to claim 7, characterized in that: The restriction execution module responds to the restriction execution condition and compresses the display information of various candidate job selection information of the restricted access user, including: Filter the associated stage information of each candidate recruitment information, and test the adaptation recommendation coefficient of each candidate recruitment information; Restrict access to users, allowing them to view only the associated stage information and adaptation recommendation coefficients of their candidate recruitment information; The restriction execution condition is that there is a user to be evaluated who needs to be restricted in information access, and the user to be evaluated who needs to be restricted in information access is recorded as a restricted access user.

9. The big data-based employment management system according to claim 8, characterized in that: The restriction execution module determines the associated stage information of each candidate recruitment information based on the employment scenario association degree and the employment operation association degree; An adaptation recommendation coefficient of each candidate job information is determined according to the associated operation parameters and the evaluation effectiveness parameters, and the adaptation recommendation coefficient is positively correlated with the associated operation parameters and the evaluation effectiveness parameters respectively.

10. The big data-based employment management system according to claim 9, characterized in that: The restriction execution module determines the evaluation validity parameters of each associated stage information based on the valid evaluation ratio and the evaluation conflict parameter; The associated restriction condition responded by the restriction execution module is that the evaluation valid parameter of the associated stage information is less than or equal to the preset evaluation valid parameter, then it is determined that the associated stage information is not used in the determination process of the adaptation recommendation coefficient of the corresponding alternative job information, and the relevant content of the associated stage information is not displayed.

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