CN112288338B - User activity monitoring method, device, equipment and medium - Google Patents
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Abstract
The invention relates to the field of artificial intelligence, and provides a user activity monitoring method, a device, equipment and a medium, which can intuitively quantify the activity effect based on a constructed overall evaluation model so as to track the exhibition result of an agent, effectively monitor the activity process by combining a behavior evaluation model, accurately locate the problem of the agent in the activity, gradually disassemble from link to sub-index, clarify the pain point needing to be improved and improved by the agent, and give an early warning in time so as to promote the agent to make good use of the advantages and avoid the disadvantages. The invention also relates to a block chain technology, and the early warning information can be stored in the block chain.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a user activity monitoring method, device, equipment and medium.
Background
In activity management in the sales field, the standard reaching situation of the current performance is usually calculated in a statistical manner to judge the exhibition effect of the activity and further evaluate an agent.
However, the above method cannot grasp the execution condition of the whole process, and is not in place for datamation, so that attention and guidance to the process are lacked, and a scheme for managing the active process by using data drive is also lacked. The sales agent cannot know the exhibition completion condition of the sales agent in time, and the superior management personnel cannot track and evaluate the process.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a device, and a medium for monitoring user activities, which can intuitively quantify the activity effect based on a constructed overall evaluation model to track the exhibition result of an agent, and effectively monitor the activity process by combining a behavior evaluation model, and can accurately locate the problem of the agent in the activity, and gradually disassemble from link to sub-index, thereby defining the pain point that the agent needs to promote and improve, and giving an early warning in time to promote the agent to make good use of the advantages and avoid the disadvantages.
A user activity monitoring method, the user activity monitoring method comprising:
responding to a user activity monitoring instruction, and acquiring historical activity data according to the user activity monitoring instruction;
processing and preprocessing the historical activity data to obtain sample data;
identifying behavior data and response result data in the sample data;
constructing an overall evaluation model according to the response result data;
acquiring at least one user type configured in advance, and respectively constructing a behavior evaluation model for the at least one user type according to the behavior data;
acquiring data to be processed, and inputting the data to be processed into the overall evaluation model to obtain a first numerical value;
when the first value is smaller than or equal to a first preset threshold value, analyzing the first value to obtain a target link;
acquiring a target behavior evaluation model corresponding to the data to be processed from the behavior evaluation model, and inputting the data to be processed into the target behavior evaluation model to obtain a second numerical value;
when the second numerical value is smaller than or equal to a second preset threshold value, analyzing the second numerical value to obtain a target behavior;
and generating early warning information according to the target link and the target behavior.
According to a preferred embodiment of the present invention, the acquiring historical activity data according to the user activity monitoring instruction includes:
analyzing the method body of the user activity monitoring instruction to obtain information carried by the user activity monitoring instruction;
acquiring a preset label;
searching data which is the same as the preset label in the information carried by the user activity monitoring instruction as a target address;
link to the target address, and crawl data of the target address as the historical activity data.
According to a preferred embodiment of the present invention, the preprocessing the historical activity data to obtain sample data includes:
calling a thread to calculate the saturation of each historical activity data in the historical activity data and calculate the correlation between every two historical activity data in the historical activity data;
acquiring data with saturation less than or equal to configuration saturation from the historical activity data, and deleting the acquired data to obtain first intermediate data;
acquiring two data with the correlation degree larger than or equal to the configuration correlation degree from the first intermediate data, acquiring data with lower saturation degree from the two data, and deleting the data with lower saturation degree to obtain second intermediate data;
detecting missing data in the second intermediate data, and performing filling processing on the missing data to obtain third intermediate data;
and performing expansion processing on the third intermediate data to obtain the sample data.
According to a preferred embodiment of the present invention, the constructing an overall evaluation model according to the response result data includes:
determining at least one link corresponding to the response result data;
calculating a weight of each of the at least one link;
determining at least one sub-index corresponding to the response result data, performing importance analysis on the at least one sub-index, and acquiring the sub-index with the importance degree greater than or equal to the configured importance degree as a target sub-index;
determining links corresponding to each target sub-index in the target sub-indexes, and calculating the weight of each target sub-index according to the weight of the links corresponding to each target sub-index;
obtaining a completion mean value of each target sub-index;
performing cluster analysis on the at least one sub-index according to the weight of each target sub-index and the completion mean value of each target sub-index to obtain at least one sub-index category;
calculating a reference value of each sub-index category;
and performing weighting processing according to the reference value of each sub-index type and the at least one sub-index to obtain the overall evaluation model.
According to a preferred embodiment of the present invention, the calculating the weight of each target sub-indicator according to the weight of the link corresponding to each target sub-indicator includes:
inputting the response result data into a LightGBM model, and acquiring output data of the LightGBM model;
acquiring the sub-weight of each target sub-index from the output data;
and calculating the product of the sub-weight of each target sub-index and the weight of the link corresponding to each target sub-index as the weight of each target sub-index.
According to a preferred embodiment of the present invention, the calculating the reference value of each sub-metric class includes:
combining the values of all the sub-indexes in each sub-index category to obtain each variable factor corresponding to each sub-index category;
acquiring a configuration value as a fitting target;
performing fitting training according to each variable factor and the fitting target to obtain a fitting model;
and acquiring parameters of the fitting model as reference values of each sub-index category.
According to a preferred embodiment of the present invention, the analyzing the first value to obtain the target link includes:
determining at least one current sub-index corresponding to the data to be processed;
splitting the first numerical value according to the at least one current sub-index to obtain a value of each current sub-index in the at least one current sub-index;
acquiring the current sub-index with the lowest value;
and determining a link corresponding to the obtained current sub-index as the target link.
A user activity monitoring device, the user activity monitoring device comprising:
the acquisition unit is used for responding to a user activity monitoring instruction and acquiring historical activity data according to the user activity monitoring instruction;
the preprocessing unit is used for processing and preprocessing the historical activity data to obtain sample data;
the identification unit is used for identifying behavior data and response result data in the sample data;
the construction unit is used for constructing an overall evaluation model according to the response result data;
the building unit is further configured to obtain at least one user type configured in advance, and respectively build a behavior evaluation model for the at least one user type according to the behavior data;
the input unit is used for acquiring data to be processed and inputting the data to be processed into the overall evaluation model to obtain a first numerical value;
the analysis unit is used for analyzing the first numerical value to obtain a target link when the first numerical value is smaller than or equal to a first preset threshold value;
the input unit is further configured to acquire a target behavior evaluation model corresponding to the to-be-processed data from the behavior evaluation model, and input the to-be-processed data to the target behavior evaluation model to obtain a second numerical value;
the analysis unit is further configured to analyze the second numerical value to obtain a target behavior when the second numerical value is less than or equal to a second preset threshold;
and the generating unit is used for generating early warning information according to the target link and the target behavior.
An electronic device, the electronic device comprising:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the user activity monitoring method.
A computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executable by a processor in an electronic device to implement the user activity monitoring method.
According to the technical scheme, the activity effect can be directly quantized based on the constructed integral evaluation model so as to track the exhibition result of the agent, the activity process is effectively monitored by combining the behavior evaluation model, the problem of the agent in the activity can be accurately positioned, the agent is gradually disassembled from link to sub-index, pain points needing to be improved and improved by the agent are determined, and early warning is timely carried out so as to promote the agent to make good use of the advantages and avoid the disadvantages.
Drawings
FIG. 1 is a flow chart of a user activity monitoring method according to a preferred embodiment of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of the user activity monitoring device of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device implementing a user activity monitoring method according to a preferred embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flow chart of a user activity monitoring method according to a preferred embodiment of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The user activity monitoring method is applied to one or more electronic devices, which are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware thereof includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive Internet Protocol Television (IPTV), an intelligent wearable device, and the like.
The electronic device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network servers.
The Network where the electronic device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
And S10, responding to the user activity monitoring instruction, and acquiring historical activity data according to the user activity monitoring instruction.
In this embodiment, the user activity monitoring instruction may be triggered by a designated staff member, such as: sales agents, sales managers, etc.
The historical activity data refers to behavior data of all sales agents in different exhibition links and response result data corresponding to the behavior data.
In at least one embodiment of the present invention, the obtaining of historical activity data according to the user activity monitoring instruction includes:
analyzing the method body of the user activity monitoring instruction to obtain information carried by the user activity monitoring instruction;
acquiring a preset label;
searching data which is the same as the preset label in the information carried by the user activity monitoring instruction as a target address;
link to the target address, and crawl data of the target address as the historical activity data.
Specifically, the user activity monitoring instruction is substantially a piece of code, and in the user activity monitoring instruction, according to the writing principle of the code, the content between { } is referred to as the method body.
The preset tag can be configured in a user-defined mode, such as add.
Through the embodiment, when the target address can be directly acquired by the preset tag, the data is directly acquired from the instruction, so that the efficiency is improved, and the data acquisition accuracy is also improved by acquiring the data by the tag.
And S11, processing and preprocessing the historical activity data to obtain sample data.
It should be noted that certain data defects may also exist in the historical activity data, such as repeated data, unsaturated data, and the like, and in order to improve the usability of the data, the data needs to be processed and preprocessed first.
Specifically, the processing and preprocessing the historical activity data to obtain sample data includes:
calling a thread to calculate the saturation of each historical activity data in the historical activity data and calculate the correlation between every two historical activity data in the historical activity data;
acquiring data with saturation less than or equal to configuration saturation from the historical activity data, and deleting the acquired data to obtain first intermediate data;
acquiring two data with the correlation degree larger than or equal to the configuration correlation degree from the first intermediate data, acquiring data with lower saturation degree from the two data, and deleting the data with lower saturation degree to obtain second intermediate data;
detecting missing data in the second intermediate data, and performing filling processing on the missing data to obtain third intermediate data;
and performing expansion processing on the third intermediate data to obtain the sample data.
The correlation between every two historical activity data in the historical activity data can be calculated by adopting a pearson algorithm, which is not limited in the present invention.
The configuration saturation and the configuration correlation can be configured in a user-defined mode.
Further, when the missing data is subjected to padding processing, a zero value or a mean value may be adopted. For example: mean filling is adopted for data such as age and income, and zero value filling is adopted for general behavior data.
Further, in the expanding process of the third intermediate data, a time-derived manner (for example, deriving the data to about 1 month, 3 months, etc.) may be adopted, and a summation manner may also be adopted to construct more features.
In the embodiment, by deleting data with low saturation, it is possible to avoid inaccurate data analysis due to lack of factors.
By deleting the data with high correlation, the repeated data can be deleted, and unnecessary operation burden of the system caused by data redundancy is avoided.
By filling up missing data, the availability of the data can be further ensured.
Through carrying out extension processing to data, can effectively improve sample quantity to improve the precision of follow-up model training, and then promote the training effect of model.
And S12, identifying the behavior data and the response result data in the sample data.
For example: the behavior data can be forwarding information, and the corresponding response result data is information whether the user reads the forwarded information.
Specifically, the behavior data and the response result data in the sample data may be identified according to a keyword or a tag in the sample data, which is not limited in the present invention.
And S13, constructing an overall evaluation model according to the response result data.
It should be noted that the overall evaluation model may be used to evaluate the overall effect of the activity, and a superior manager may track and judge the exhibition effect of the agent in time according to the overall evaluation model.
In this embodiment, the constructing an overall evaluation model according to the response result data includes:
determining at least one link corresponding to the response result data;
calculating a weight of each of the at least one link;
determining at least one sub-index corresponding to the response result data, performing importance analysis on the at least one sub-index, and acquiring the sub-index with the importance degree greater than or equal to the configured importance degree as a target sub-index;
determining links corresponding to each target sub-index in the target sub-indexes, and calculating the weight of each target sub-index according to the weight of the links corresponding to each target sub-index;
obtaining a completion mean value of each target sub-index;
performing cluster analysis on the at least one sub-index according to the weight of each target sub-index and the completion mean value of each target sub-index to obtain at least one sub-index category;
calculating a reference value of each sub-index category;
and performing weighting processing according to the reference value of each sub-index type and the at least one sub-index to obtain the overall evaluation model.
And the configuration importance can be configured in a user-defined manner.
The sub-indicators refer to a generic concept of the response result data, such as: when one of the response result data is "the reading behavior is detected by the client", the corresponding sub-index is "the reading behavior of the client".
It can be understood that the sales agent mainly performs the following links in the exhibition industry activity: accumulation of customers, interactive exploitation, life insurance promotion and service management. Each link can be divided into a plurality of sub-processes, and each sub-process can generate a large amount of activity data.
Specifically, the weight of each link may be calculated based on the accumulated sum of the sub-weights of all sub-indices contained in each link.
For example: the accumulated sum of the sub-weights of the customer accumulation link is 30%, the accumulated sum of the sub-weights of the interactive development link is 35%, the weight of the customer accumulation link is 30%, and the weight of the interactive development link is 35%.
It should be noted that the weight of each target sub-indicator can measure the importance of each target sub-indicator, and a higher weight represents a higher value of the corresponding target sub-indicator.
The completion mean value of each target sub-index can reflect the completion difficulty of each target sub-index, and the higher the mean value is, the easier the representative person completes the process. If the values of all the agents on each target sub-index are accumulated and then divided by the total number of all the agents, the completion average value of each target sub-index can be obtained.
Specifically, kmeans or kmeans + +, clustering analysis is performed on at least one sub-indicator according to the weight of each target sub-indicator and the completion mean value of each target sub-indicator, and the importance degree and the completion difficulty degree of the sub-indicators are considered comprehensively, so that the classification result has higher availability.
For example: the importance degree can be divided into high, medium and low according to the weight of each target sub-index, the difficulty degree is divided into difficult, flat and easy according to the completion average value of each target sub-index, and 9 clusters are obtained after intersection, namely 9 sub-index categories.
Specifically, the calculating the weight of each target sub-indicator according to the weight of the link corresponding to each target sub-indicator includes:
inputting the response result data into a LightGBM model, and acquiring output data of the LightGBM model;
acquiring the sub-weight of each target sub-index from the output data;
and calculating the product of the sub-weight of each target sub-index and the weight of the link corresponding to each target sub-index as the weight of each target sub-index.
Wherein the LightGBM model needs to be pre-trained.
For example: and adopting the result index data of the month 6 as a variable factor, and outputting whether the performance of the user in the month 7 can reach the standard, wherein the standard is a positive sample, and the standard which is not reached is a negative sample. And dividing the result index data of the month 6 into training data and test data according to the proportion of 7:3, and performing multiple rounds of training on the LightGBM network by using the training data. And the LightGBM generates a weak classifier through multiple iterations, each iteration generates a weak classifier, and each weak classifier is trained on the basis of the residual error of the last classifier until the residual error is smaller than a certain threshold value or no longer changes, and the LightGBM model is obtained by stopping training.
Of course, in other embodiments, a random forest model, an XGBoost model, etc. may also be used, which is not limited in the present invention.
Through the implementation mode, the weight of each target sub-index is determined by combining the sub-weight of each target sub-index and the weight of the link to which the target sub-index belongs, and the importance degree of each target sub-index can be reflected more accurately.
Further, the calculating the reference value of each sub-index category includes:
combining the values of all the sub-indexes in each sub-index category to obtain each variable factor corresponding to each sub-index category;
acquiring a configuration value as a fitting target;
performing fitting training according to each variable factor and the fitting target to obtain a fitting model;
and acquiring parameters of the fitting model as reference values of each sub-index category.
Wherein, the configuration value can be configured by self-definition, such as 200.
For example: and (3) performing fitting training by adopting a linear regression model, integrating the indexes into 9 types according to the clustering by y = w 1X 1+ w 2X 2+ … wn X n, adding the similar factors, finally, equivalently, 9 groups of X factors exist, the sample is data of a representative who can reach the standard, directly fitting y =200 according to the linear regression, solving a corresponding coefficient wn, and obtaining a reference value of each sub-index type after the rounding.
According to the embodiment, the overall scoring model is obtained by combining weight and clustering, so that the evaluation dimensionality is more comprehensive, and meanwhile, the importance of different indexes is considered, so that the overall scoring model has higher generalization capability.
S14, obtaining at least one user type configured in advance, and respectively constructing a behavior evaluation model for the at least one user type according to the behavior data.
For example: the agents are divided into 8 types through clustering algorithms of kmeans, kmeans + + and the like, two annual resources of more than one year and less than one year are crossed at the same time, and 16 agent groups are obtained in total.
It should be noted that the behavior evaluation model is constructed in a similar manner as the overall evaluation model is constructed (e.g., the behavior data of different user types can be used as factors of input, and whether the performance is qualified or not can be used as output for training). However, it is necessary to construct a model, filter sub-indicators, and calculate weights for at least one user type, which is not described herein.
When the benchmark value is set, the number of indexes of the behavior data is more than that of the whole indexes, and meanwhile, the values of the behavior data are mainly used for tracking the performance of the activity process, and the values among different behavior data are not suitable to be excessively differentiated, so that the actions with high value and high difficulty can be simply set as a first value (such as 2), the actions with low value and low difficulty can be set as a second value (such as 1) by controlling the number of screening actions, and finally, the standard total score is about a third value (such as 800). Alternatively, the fitting method described above may be used, and the present invention is not limited thereto.
And S15, acquiring data to be processed, and inputting the data to be processed into the overall evaluation model to obtain a first numerical value.
The data to be processed is behavior data and response result data generated by any user. By determining the user generating the data to be processed, the user type corresponding to the data to be processed can be determined.
Through the embodiment, the scoring of the exhibition activities can be realized based on the overall evaluation model, and the effect of the exhibition activities can be reflected visually in a quantitative mode so as to assist superior management personnel to know the conditions of the exhibition activities in time.
And S16, when the first value is smaller than or equal to a first preset threshold value, analyzing the first value to obtain a target link.
The first preset threshold may be configured by a user, for example, 150.
In this embodiment, the analyzing the first value to obtain the target link includes:
determining at least one current sub-index corresponding to the data to be processed;
splitting the first numerical value according to the at least one current sub-index to obtain a value of each current sub-index in the at least one current sub-index;
acquiring the current sub-index with the lowest value;
and determining a link corresponding to the obtained current sub-index as the target link.
Through the implementation mode, the weak link of the agent can be accurately positioned, so that the weak link can be timely improved in the following process.
And S17, acquiring a target behavior evaluation model corresponding to the data to be processed from the behavior evaluation model, and inputting the data to be processed into the target behavior evaluation model to obtain a second numerical value.
Specifically, the target behavior evaluation model corresponding to the data to be processed may be determined according to the user type corresponding to the data to be processed.
In the embodiment, the effect of each behavior is quantified by using the behavior evaluation model, so that each behavior in the activity process is effectively monitored, and the completion degree of the exhibition industry and the completion condition of each behavior are determined by each agent.
S18, when the second numerical value is smaller than or equal to a second preset threshold, analyzing the second numerical value to obtain a target behavior.
The second preset threshold may be configured in a customized manner, such as 750.
It should be noted that the manner of analyzing the second numerical value to obtain the target behavior is similar to the manner of analyzing the first numerical value to obtain the target link, and details are not repeated here.
Through the implementation mode, after the weak link is determined, the behavior needing to be improved and promoted can be further positioned, and the method is beneficial for the deputy to make good use of the advantages and avoid the disadvantages.
And S19, generating early warning information according to the target link and the target behavior.
Specifically, a configuration template may be obtained, the content to be filled of each module to be filled in the configuration template is identified, the content to be filled is obtained as the module to be filled of the target link and the target behavior, and the target link and the target behavior are filled to the obtained module to be filled, so as to generate the early warning information.
Of course, in order to ensure the security of data, the warning information may also be encrypted.
Through the embodiment, the weak point of the agent can be reminded in time on the basis of monitoring activities, the warning effect is achieved, and the agent is supervised to improve the pain point as soon as possible.
In this embodiment, to further ensure the security of the data, the warning information may be stored in the blockchain.
According to the technical scheme, the activity effect can be directly quantized based on the constructed integral evaluation model so as to track the exhibition result of the agent, the activity process is effectively monitored by combining the behavior evaluation model, the problem of the agent in the activity can be accurately positioned, the agent is gradually disassembled from link to sub-index, pain points needing to be improved and improved by the agent are determined, and early warning is timely carried out so as to promote the agent to make good use of the advantages and avoid the disadvantages.
Fig. 2 is a functional block diagram of a user activity monitoring device according to a preferred embodiment of the present invention. The user activity monitoring device 11 comprises an acquisition unit 110, a preprocessing unit 111, a recognition unit 112, a construction unit 113, an input unit 114, an analysis unit 115, and a generation unit 116. The module/unit referred to in the present invention refers to a series of computer program segments that can be executed by the processor 13 and that can perform a fixed function, and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
In response to the user activity monitoring instruction, the obtaining unit 110 obtains historical activity data according to the user activity monitoring instruction.
In this embodiment, the user activity monitoring instruction may be triggered by a designated staff member, such as: sales agents, sales managers, etc.
The historical activity data refers to behavior data of all sales agents in different exhibition links and response result data corresponding to the behavior data.
In at least one embodiment of the present invention, the obtaining unit 110 obtains the historical activity data according to the user activity monitoring instruction includes:
analyzing the method body of the user activity monitoring instruction to obtain information carried by the user activity monitoring instruction;
acquiring a preset label;
searching data which is the same as the preset label in the information carried by the user activity monitoring instruction as a target address;
link to the target address, and crawl data of the target address as the historical activity data.
Specifically, the user activity monitoring instruction is substantially a piece of code, and in the user activity monitoring instruction, according to the writing principle of the code, the content between { } is referred to as the method body.
The preset tag can be configured in a user-defined mode, such as add.
Through the embodiment, when the target address can be directly acquired by the preset tag, the data is directly acquired from the instruction, so that the efficiency is improved, and the data acquisition accuracy is also improved by acquiring the data by the tag.
The preprocessing unit 111 performs processing preprocessing on the historical activity data to obtain sample data.
It should be noted that certain data defects may also exist in the historical activity data, such as repeated data, unsaturated data, and the like, and in order to improve the usability of the data, the data needs to be processed and preprocessed first.
Specifically, the preprocessing unit 111 performs processing preprocessing on the historical activity data to obtain sample data, including:
calling a thread to calculate the saturation of each historical activity data in the historical activity data and calculate the correlation between every two historical activity data in the historical activity data;
acquiring data with saturation less than or equal to configuration saturation from the historical activity data, and deleting the acquired data to obtain first intermediate data;
acquiring two data with the correlation degree larger than or equal to the configuration correlation degree from the first intermediate data, acquiring data with lower saturation degree from the two data, and deleting the data with lower saturation degree to obtain second intermediate data;
detecting missing data in the second intermediate data, and performing filling processing on the missing data to obtain third intermediate data;
and performing expansion processing on the third intermediate data to obtain the sample data.
The correlation between every two historical activity data in the historical activity data can be calculated by adopting a pearson algorithm, which is not limited in the present invention.
The configuration saturation and the configuration correlation can be configured in a user-defined mode.
Further, when the missing data is subjected to padding processing, a zero value or a mean value may be adopted. For example: mean filling is adopted for data such as age and income, and zero value filling is adopted for general behavior data.
Further, in the expanding process of the third intermediate data, a time-derived manner (for example, deriving the data to about 1 month, 3 months, etc.) may be adopted, and a summation manner may also be adopted to construct more features.
In the embodiment, by deleting data with low saturation, it is possible to avoid inaccurate data analysis due to lack of factors.
By deleting the data with high correlation, the repeated data can be deleted, and unnecessary operation burden of the system caused by data redundancy is avoided.
By filling up missing data, the availability of the data can be further ensured.
Through carrying out extension processing to data, can effectively improve sample quantity to improve the precision of follow-up model training, and then promote the training effect of model.
The identifying unit 112 identifies behavior data and response result data in the sample data.
For example: the behavior data can be forwarding information, and the corresponding response result data is information whether the user reads the forwarded information.
Specifically, the behavior data and the response result data in the sample data may be identified according to a keyword or a tag in the sample data, which is not limited in the present invention.
The construction unit 113 constructs an overall evaluation model from the response result data.
It should be noted that the overall evaluation model may be used to evaluate the overall effect of the activity, and a superior manager may track and judge the exhibition effect of the agent in time according to the overall evaluation model.
In this embodiment, the constructing unit 113 constructing the overall evaluation model according to the response result data includes:
determining at least one link corresponding to the response result data;
calculating a weight of each of the at least one link;
determining at least one sub-index corresponding to the response result data, performing importance analysis on the at least one sub-index, and acquiring the sub-index with the importance degree greater than or equal to the configured importance degree as a target sub-index;
determining links corresponding to each target sub-index in the target sub-indexes, and calculating the weight of each target sub-index according to the weight of the links corresponding to each target sub-index;
obtaining a completion mean value of each target sub-index;
performing cluster analysis on the at least one sub-index according to the weight of each target sub-index and the completion mean value of each target sub-index to obtain at least one sub-index category;
calculating a reference value of each sub-index category;
and performing weighting processing according to the reference value of each sub-index type and the at least one sub-index to obtain the overall evaluation model.
And the configuration importance can be configured in a user-defined manner.
The sub-indicators refer to a generic concept of the response result data, such as: when one of the response result data is "the reading behavior is detected by the client", the corresponding sub-index is "the reading behavior of the client".
It can be understood that the sales agent mainly performs the following links in the exhibition industry activity: accumulation of customers, interactive exploitation, life insurance promotion and service management. Each link can be divided into a plurality of sub-processes, and each sub-process can generate a large amount of activity data.
Specifically, the weight of each link may be calculated based on the accumulated sum of the sub-weights of all sub-indices contained in each link.
For example: the accumulated sum of the sub-weights of the customer accumulation link is 30%, the accumulated sum of the sub-weights of the interactive development link is 35%, the weight of the customer accumulation link is 30%, and the weight of the interactive development link is 35%.
It should be noted that the weight of each target sub-indicator can measure the importance of each target sub-indicator, and a higher weight represents a higher value of the corresponding target sub-indicator.
The completion mean value of each target sub-index can reflect the completion difficulty of each target sub-index, and the higher the mean value is, the easier the representative person completes the process. If the values of all the agents on each target sub-index are accumulated and then divided by the total number of all the agents, the completion average value of each target sub-index can be obtained.
Specifically, kmeans or kmeans + +, clustering analysis is performed on at least one sub-indicator according to the weight of each target sub-indicator and the completion mean value of each target sub-indicator, and the importance degree and the completion difficulty degree of the sub-indicators are considered comprehensively, so that the classification result has higher availability.
For example: the importance degree can be divided into high, medium and low according to the weight of each target sub-index, the difficulty degree is divided into difficult, flat and easy according to the completion average value of each target sub-index, and 9 clusters are obtained after intersection, namely 9 sub-index categories.
Specifically, the calculating, by the constructing unit 113, the weight of each target sub-indicator according to the weight of the link corresponding to each target sub-indicator includes:
inputting the response result data into a LightGBM model, and acquiring output data of the LightGBM model;
acquiring the sub-weight of each target sub-index from the output data;
and calculating the product of the sub-weight of each target sub-index and the weight of the link corresponding to each target sub-index as the weight of each target sub-index.
Wherein the LightGBM model needs to be pre-trained.
For example: and adopting the result index data of the month 6 as a variable factor, and outputting whether the performance of the user in the month 7 can reach the standard, wherein the standard is a positive sample, and the standard which is not reached is a negative sample. And dividing the result index data of the month 6 into training data and test data according to the proportion of 7:3, and performing multiple rounds of training on the LightGBM network by using the training data. And the LightGBM generates a weak classifier through multiple iterations, each iteration generates a weak classifier, and each weak classifier is trained on the basis of the residual error of the last classifier until the residual error is smaller than a certain threshold value or no longer changes, and the LightGBM model is obtained by stopping training.
Of course, in other embodiments, a random forest model, an XGBoost model, etc. may also be used, which is not limited in the present invention.
Through the implementation mode, the weight of each target sub-index is determined by combining the sub-weight of each target sub-index and the weight of the link to which the target sub-index belongs, and the importance degree of each target sub-index can be reflected more accurately.
Further, the calculating the reference value of each sub-index category by the constructing unit 113 includes:
combining the values of all the sub-indexes in each sub-index category to obtain each variable factor corresponding to each sub-index category;
acquiring a configuration value as a fitting target;
performing fitting training according to each variable factor and the fitting target to obtain a fitting model;
and acquiring parameters of the fitting model as reference values of each sub-index category.
Wherein, the configuration value can be configured by self-definition, such as 200.
For example: and (3) performing fitting training by adopting a linear regression model, integrating the indexes into 9 types according to the clustering by y = w 1X 1+ w 2X 2+ … wn X n, adding the similar factors, finally, equivalently, 9 groups of X factors exist, the sample is data of a representative who can reach the standard, directly fitting y =200 according to the linear regression, solving a corresponding coefficient wn, and obtaining a reference value of each sub-index type after the rounding.
According to the embodiment, the overall scoring model is obtained by combining weight and clustering, so that the evaluation dimensionality is more comprehensive, and meanwhile, the importance of different indexes is considered, so that the overall scoring model has higher generalization capability.
The constructing unit 113 obtains at least one user type configured in advance, and constructs a behavior evaluation model for the at least one user type according to the behavior data.
For example: the agents are divided into 8 types through clustering algorithms of kmeans, kmeans + + and the like, two annual resources of more than one year and less than one year are crossed at the same time, and 16 agent groups are obtained in total.
It should be noted that the behavior evaluation model is constructed in a similar manner as the overall evaluation model is constructed (e.g., the behavior data of different user types can be used as factors of input, and whether the performance is qualified or not can be used as output for training). However, it is necessary to construct a model, filter sub-indicators, and calculate weights for at least one user type, which is not described herein.
When the benchmark value is set, the number of indexes of the behavior data is more than that of the whole indexes, and meanwhile, the values of the behavior data are mainly used for tracking the performance of the activity process, and the values among different behavior data are not suitable to be excessively differentiated, so that the actions with high value and high difficulty can be simply set as a first value (such as 2), the actions with low value and low difficulty can be set as a second value (such as 1) by controlling the number of screening actions, and finally, the standard total score is about a third value (such as 800). Alternatively, the fitting method described above may be used, and the present invention is not limited thereto.
The input unit 114 obtains data to be processed, and inputs the data to be processed to the overall evaluation model to obtain a first numerical value.
The data to be processed is behavior data and response result data generated by any user. By determining the user generating the data to be processed, the user type corresponding to the data to be processed can be determined.
Through the embodiment, the scoring of the exhibition activities can be realized based on the overall evaluation model, and the effect of the exhibition activities can be reflected visually in a quantitative mode so as to assist superior management personnel to know the conditions of the exhibition activities in time.
When the first value is smaller than or equal to a first preset threshold, the analysis unit 115 analyzes the first value to obtain a target link.
The first preset threshold may be configured by a user, for example, 150.
In this embodiment, the analyzing unit 115 analyzes the first value to obtain the target link includes:
determining at least one current sub-index corresponding to the data to be processed;
splitting the first numerical value according to the at least one current sub-index to obtain a value of each current sub-index in the at least one current sub-index;
acquiring the current sub-index with the lowest value;
and determining a link corresponding to the obtained current sub-index as the target link.
Through the implementation mode, the weak link of the agent can be accurately positioned, so that the weak link can be timely improved in the following process.
The input unit 114 obtains a target behavior evaluation model corresponding to the data to be processed from the behavior evaluation model, and inputs the data to be processed to the target behavior evaluation model to obtain a second numerical value.
Specifically, the target behavior evaluation model corresponding to the data to be processed may be determined according to the user type corresponding to the data to be processed.
In the embodiment, the effect of each behavior is quantified by using the behavior evaluation model, so that each behavior in the activity process is effectively monitored, and the completion degree of the exhibition industry and the completion condition of each behavior are determined by each agent.
When the second value is less than or equal to a second preset threshold, the analysis unit 115 analyzes the second value to obtain a target behavior.
The second preset threshold may be configured in a customized manner, such as 750.
It should be noted that the manner of analyzing the second numerical value to obtain the target behavior is similar to the manner of analyzing the first numerical value to obtain the target link, and details are not repeated here.
Through the implementation mode, after the weak link is determined, the behavior needing to be improved and promoted can be further positioned, and the method is beneficial for the deputy to make good use of the advantages and avoid the disadvantages.
The generating unit 116 generates the early warning information according to the target link and the target behavior.
Specifically, a configuration template may be obtained, the content to be filled of each module to be filled in the configuration template is identified, the content to be filled is obtained as the module to be filled of the target link and the target behavior, and the target link and the target behavior are filled to the obtained module to be filled, so as to generate the early warning information.
Of course, in order to ensure the security of data, the warning information may also be encrypted.
Through the embodiment, the weak point of the agent can be reminded in time on the basis of monitoring activities, the warning effect is achieved, and the agent is supervised to improve the pain point as soon as possible.
In this embodiment, to further ensure the security of the data, the warning information may be stored in the blockchain.
According to the technical scheme, the activity effect can be directly quantized based on the constructed integral evaluation model so as to track the exhibition result of the agent, the activity process is effectively monitored by combining the behavior evaluation model, the problem of the agent in the activity can be accurately positioned, the agent is gradually disassembled from link to sub-index, pain points needing to be improved and improved by the agent are determined, and early warning is timely carried out so as to promote the agent to make good use of the advantages and avoid the disadvantages.
Fig. 3 is a schematic structural diagram of an electronic device implementing a user activity monitoring method according to a preferred embodiment of the present invention.
The electronic device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as a user activity monitoring program, stored in the memory 12 and executable on the processor 13.
It will be understood by those skilled in the art that the schematic diagram is merely an example of the electronic device 1, and does not constitute a limitation to the electronic device 1, the electronic device 1 may have a bus-type structure or a star-type structure, the electronic device 1 may further include more or less hardware or software than those shown in the figures, or different component arrangements, for example, the electronic device 1 may further include an input and output device, a network access device, and the like.
It should be noted that the electronic device 1 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.
The memory 12 includes at least one type of readable storage medium, which includes flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 1, for example a removable hard disk of the electronic device 1. The memory 12 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 12 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of a user activity monitoring program, etc., but also to temporarily store data that has been output or is to be output.
The processor 13 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 13 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the electronic device 1 by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (for example, executing a user activity monitoring program and the like) stored in the memory 12 and calling data stored in the memory 12.
The processor 13 executes an operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in the various user activity monitoring method embodiments described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 12 and executed by the processor 13 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into an acquisition unit 110, a preprocessing unit 111, a recognition unit 112, a construction unit 113, an input unit 114, an analysis unit 115, a generation unit 116.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute the parts of the user activity monitoring method according to the embodiments of the present invention.
The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), random-access Memory, or the like.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus. The bus is arranged to enable connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 13 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
Fig. 3 only shows the electronic device 1 with components 12-13, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
With reference to fig. 1, the memory 12 of the electronic device 1 stores a plurality of instructions to implement a user activity monitoring method, and the processor 13 executes the plurality of instructions to implement:
responding to a user activity monitoring instruction, and acquiring historical activity data according to the user activity monitoring instruction;
processing and preprocessing the historical activity data to obtain sample data;
identifying behavior data and response result data in the sample data;
constructing an overall evaluation model according to the response result data;
acquiring at least one user type configured in advance, and respectively constructing a behavior evaluation model for the at least one user type according to the behavior data;
acquiring data to be processed, and inputting the data to be processed into the overall evaluation model to obtain a first numerical value;
when the first value is smaller than or equal to a first preset threshold value, analyzing the first value to obtain a target link;
acquiring a target behavior evaluation model corresponding to the data to be processed from the behavior evaluation model, and inputting the data to be processed into the target behavior evaluation model to obtain a second numerical value;
when the second numerical value is smaller than or equal to a second preset threshold value, analyzing the second numerical value to obtain a target behavior;
and generating early warning information according to the target link and the target behavior.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A user activity monitoring method, the user activity monitoring method comprising:
responding to a user activity monitoring instruction, and acquiring historical activity data according to the user activity monitoring instruction, wherein the historical activity data comprises behavior data of a user in different user activity links and response result data corresponding to the behavior data;
processing and preprocessing the historical activity data to obtain sample data;
identifying behavior data in the sample data and response result data corresponding to the behavior data;
constructing an overall evaluation model according to the response result data;
acquiring at least one user type configured in advance, and respectively constructing a behavior evaluation model for the at least one user type according to the behavior data;
acquiring data to be processed, and inputting the data to be processed into the overall evaluation model to obtain a first numerical value, wherein the data to be processed comprises behavior data and response result data generated by any user;
when the first value is smaller than or equal to a first preset threshold value, analyzing the first value to obtain a target link;
acquiring a target behavior evaluation model corresponding to the data to be processed from the behavior evaluation model, wherein the target behavior evaluation model comprises the following steps: determining a target behavior evaluation model corresponding to the data to be processed according to the user type corresponding to the data to be processed, and inputting the data to be processed into the target behavior evaluation model to obtain a second numerical value;
when the second numerical value is smaller than or equal to a second preset threshold value, analyzing the second numerical value to obtain a target behavior;
and generating early warning information according to the target link and the target behavior.
2. The user activity monitoring method of claim 1, wherein the obtaining historical activity data according to the user activity monitoring instructions comprises:
analyzing the method body of the user activity monitoring instruction to obtain information carried by the user activity monitoring instruction;
acquiring a preset label;
searching data which is the same as the preset label in the information carried by the user activity monitoring instruction as a target address;
link to the target address, and crawl data of the target address as the historical activity data.
3. The method according to claim 1, wherein the preprocessing the historical activity data to obtain sample data comprises:
calling a thread to calculate the saturation of each historical activity data in the historical activity data and calculate the correlation between every two historical activity data in the historical activity data;
acquiring data with saturation less than or equal to configuration saturation from the historical activity data, and deleting the acquired data to obtain first intermediate data;
acquiring two data with the correlation degree larger than or equal to the configuration correlation degree from the first intermediate data, acquiring data with lower saturation degree from the two data, and deleting the data with lower saturation degree to obtain second intermediate data;
detecting missing data in the second intermediate data, and performing filling processing on the missing data to obtain third intermediate data;
and performing expansion processing on the third intermediate data to obtain the sample data.
4. The method for monitoring user activity according to claim 1, wherein said constructing an overall evaluation model from said response result data comprises:
determining at least one link corresponding to the response result data;
calculating a weight of each of the at least one link;
determining at least one sub-index corresponding to the response result data, performing importance analysis on the at least one sub-index, and acquiring the sub-index with the importance degree greater than or equal to the configured importance degree as a target sub-index;
determining links corresponding to each target sub-index in the target sub-indexes, and calculating the weight of each target sub-index according to the weight of the links corresponding to each target sub-index;
obtaining a completion mean value of each target sub-index;
performing cluster analysis on the at least one sub-index according to the weight of each target sub-index and the completion mean value of each target sub-index to obtain at least one sub-index category;
calculating a reference value of each sub-index category;
and performing weighting processing according to the reference value of each sub-index type and the at least one sub-index to obtain the overall evaluation model.
5. The method of claim 4, wherein the calculating the weight of each target sub-indicator according to the weight of the link corresponding to each target sub-indicator comprises:
inputting the response result data into a LightGBM model, and acquiring output data of the LightGBM model;
acquiring the sub-weight of each target sub-index from the output data;
and calculating the product of the sub-weight of each target sub-index and the weight of the link corresponding to each target sub-index as the weight of each target sub-index.
6. The method of claim 4, wherein calculating the baseline value for each sub-metric category comprises:
combining the values of all the sub-indexes in each sub-index category to obtain each variable factor corresponding to each sub-index category;
acquiring a configuration value as a fitting target;
performing fitting training according to each variable factor and the fitting target to obtain a fitting model;
and acquiring parameters of the fitting model as reference values of each sub-index category.
7. The method of claim 1, wherein analyzing the first value for a target segment comprises:
determining at least one current sub-index corresponding to the data to be processed;
splitting the first numerical value according to the at least one current sub-index to obtain a value of each current sub-index in the at least one current sub-index;
acquiring the current sub-index with the lowest value;
and determining a link corresponding to the obtained current sub-index as the target link.
8. A user activity monitoring device, the user activity monitoring device comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for responding to a user activity monitoring instruction and acquiring historical activity data according to the user activity monitoring instruction, and the historical activity data comprises behavior data of a user in different user activity links and response result data corresponding to the behavior data;
the preprocessing unit is used for processing and preprocessing the historical activity data to obtain sample data;
the identification unit is used for identifying behavior data in the sample data and response result data corresponding to the behavior data;
the construction unit is used for constructing an overall evaluation model according to the response result data;
the building unit is further configured to obtain at least one user type configured in advance, and respectively build a behavior evaluation model for the at least one user type according to the behavior data;
the system comprises an input unit, a data processing unit and a data processing unit, wherein the input unit is used for acquiring data to be processed and inputting the data to be processed into the overall evaluation model to obtain a first numerical value, and the data to be processed comprises behavior data and response result data generated by any user;
the analysis unit is used for analyzing the first numerical value to obtain a target link when the first numerical value is smaller than or equal to a first preset threshold value;
the input unit is further configured to obtain a target behavior evaluation model corresponding to the to-be-processed data from the behavior evaluation model, and includes: determining a target behavior evaluation model corresponding to the data to be processed according to the user type corresponding to the data to be processed, and inputting the data to be processed into the target behavior evaluation model to obtain a second numerical value;
the analysis unit is further configured to analyze the second numerical value to obtain a target behavior when the second numerical value is less than or equal to a second preset threshold;
and the generating unit is used for generating early warning information according to the target link and the target behavior.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement a user activity monitoring method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer-readable storage medium has stored therein at least one instruction that is executable by a processor in an electronic device to implement the user activity monitoring method of any one of claims 1-7.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110675017A (en) * | 2019-08-13 | 2020-01-10 | 平安科技（深圳）有限公司 | Performance evaluation method and device based on artificial intelligence |
| CN111062573A (en) * | 2019-11-19 | 2020-04-24 | 平安金融管理学院(中国·深圳) | Staff performance data determination method, device, medium and computer equipment |
| CN111695759A (en) * | 2020-04-23 | 2020-09-22 | 贵州乌江水电开发有限责任公司 | Operation and maintenance service management method and device |
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