CN114818968A - Buried point data detection method and device, electronic equipment and storage medium - Google Patents
Buried point data detection method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of data analysis, and discloses a buried point data detection method, a buried point data detection device, electronic equipment and a readable storage medium, wherein the method comprises the following steps: determining a buried point event type corresponding to a buried point demand, and configuring target buried point parameters according to the buried point demand; generating a buried point data table according to the target buried point parameters, and issuing the buried point data table to a preset target client; running a buried point demand automation running script to obtain a buried point event; acquiring a preset rule, detecting the time of the buried point event and the abnormal condition of logic according to the preset rule, and outputting a time and logic abnormal data file; acquiring the buried point data table from the target client, detecting the abnormal condition of the parameters of the buried point event according to the buried point data table, and outputting a parameter abnormal data file; and sending the opportunity and logic abnormal data file and the parameter abnormal data file to corresponding research and development personnel. The invention can improve the accuracy of buried point data detection.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a buried point data detection method and device, electronic equipment and a readable storage medium.
Background
The buried point refers to some program codes added in a client platform, and the program codes can collect and count browsing, access data and application use conditions of a user in the client platform when triggered, wherein the collected and counted data is called buried point data.
At present, there are two common detection methods for data of buried points: the method comprises the following steps of firstly, manual detection is lack of intellectualization, and the detection of the data of the buried points is easy to cause errors due to a large number of the data of the buried points, so that the accuracy rate of the detection of the data of the buried points is low; secondly, automatic testing: reported data is collected through proxy packet capturing, whether the reporting time is correct or not cannot be confirmed when reported data inspection is carried out, and the accuracy rate of buried point data detection is easy to become low.
Disclosure of Invention
The invention provides a buried point data detection method and device, electronic equipment and a readable storage medium, and aims to improve the accuracy of buried point data detection.
In order to achieve the above object, the present invention provides a buried point data detection method, including:
acquiring buried point requirements, classifying the buried point requirements to obtain buried point event types, and configuring target buried point parameters according to the buried point requirements;
generating a buried point data table according to the target buried point parameters, and issuing the buried point data table to a preset target client;
running a preset automatic embedded point demand running script to obtain an embedded point event corresponding to the embedded point demand;
acquiring a preset rule of the type of the buried point event, detecting the time and logic abnormal conditions of the buried point event according to the preset rule, and outputting a time and logic abnormal data file;
acquiring the buried point data table from the target client, detecting the abnormal condition of the parameters of the buried point event according to the buried point data table, and outputting a parameter abnormal data file;
and adding the opportunity and logic abnormal data file and the parameter abnormal data file to a preset abnormal problem queue, and sending the abnormal problem queue to corresponding research personnel.
Optionally, the detecting, according to the preset rule, the time of the buried point event and the abnormal condition of the logic includes:
acquiring buried point data and an event type corresponding to the buried point event;
selecting corresponding opportunity and logic rule based on the event type;
judging whether the buried point data conforms to the opportunity and the logic rule;
when the buried point data accords with the opportunity and the logic rule, judging that the buried point event is a normal buried point event;
and when the buried point data does not accord with the opportunity and logic rules, judging that the buried point event is an opportunity and logic abnormal data file.
Optionally, the detecting, according to the buried point data table, an abnormal condition of a parameter of the buried point event includes:
acquiring the event type of the buried point event and the parameters of the buried point event;
finding a buried point event type consistent with the event type from the buried point data table;
based on the buried point event type, disassembling the buried point data table to obtain a target buried point parameter corresponding to the buried point event type;
comparing the parameters of the buried point event with the target buried point parameters one by one, and judging whether the parameters of the buried point event are completely consistent with the target buried point parameters;
when the parameters of the embedded point event are completely consistent with the target embedded point parameters, judging that the data file is a normal data file;
and when the parameters of the buried point event are not completely consistent with the target buried point parameters, judging that the data file is an abnormal data file.
Optionally, the generating a buried point data table according to the target buried point parameter includes:
carrying out blood margin tracking on the target buried point parameter to obtain a buried point requirement corresponding to the target buried point parameter;
classifying the target buried point parameters according to the buried point event type corresponding to the buried point demand to obtain classified target buried point parameters;
and storing the classified target buried point parameters into a data table according to the classification to obtain a buried point data table.
Optionally, the classifying the buried point demand to obtain a buried point event type includes:
performing feature extraction on the buried point requirement to obtain a feature vector set;
and classifying the feature vector set by using a pre-constructed naive Bayes classifier to obtain the buried point event type.
Optionally, the configuring target burial point parameters according to the burial point requirements includes:
acquiring a buried point event type and a service requirement corresponding to the buried point requirement,
and screening out the buried points corresponding to the target buried point parameter configuration which accords with the preset definition from the buried point parameters corresponding to the target buried point event type based on the service requirements.
Optionally, the sending the abnormal problem queue to a corresponding developer includes:
identifying the IP address of the research personnel subscribed in the abnormal problem queue;
and sending the opportunity and logic abnormal data files and parameter abnormal data files stored in the abnormal problem queue to the IP address according to the storage time sequence.
In order to solve the above problem, the present invention further provides a buried point data detection apparatus, including:
the embedded point data table issuing module is used for acquiring embedded point requirements, classifying the embedded point requirements to obtain embedded point event types, configuring target embedded point parameters according to the embedded point requirements, generating an embedded point data table according to the target embedded point parameters, and issuing the embedded point data table to a preset target client;
the embedded point event acquisition module is used for operating a preset embedded point demand automatic operation script to obtain an embedded point event corresponding to the embedded point demand;
and the abnormal data file collection module is used for acquiring a preset rule of the type of the buried point event, detecting the time and logic abnormal conditions of the buried point event according to the preset rule, outputting a time and logic abnormal data file, acquiring the buried point data table from the target client, detecting the abnormal conditions of the parameters of the buried point event according to the buried point data table, outputting a parameter abnormal data file, adding the time and logic abnormal data file and the parameter abnormal data file to a preset abnormal problem queue, and sending the abnormal problem queue to a corresponding research and development personnel.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
and the processor executes the computer program stored in the memory to realize the buried point data detection method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the above-mentioned buried point data detection method.
The embodiment of the invention firstly classifies the buried point requirements to obtain the buried point event type, is convenient to reduce the matching difficulty of the buried point data and improve the matching efficiency of the buried point data, configures target buried point parameters according to the buried point event type, and sends a buried point data table generated according to the target buried point parameters to a preset target client, thereby being capable of screening out the target buried point event in the buried point behaviors performed by a client user and ensuring the accuracy of the buried point data, secondly obtains the preset rule of the buried point event type, detects the time of the buried point event and the abnormal condition of logic according to the preset rule, outputs a time and logic abnormal data file to ensure that the abnormal buried point data is screened out, thereby improving the accuracy of the buried point data, and finally obtains the buried point data table from the target client and detects the abnormal condition of the parameters of the buried point event according to the buried point data table, and outputting a parameter abnormal data file, ensuring that the parameters of the buried point data are not increased, reduced or wrong from the parameter dimension, and further improving the accuracy of the buried point data. Therefore, the buried point data detection method, the buried point data detection device, the electronic equipment and the readable storage medium provided by the embodiment of the invention can improve the accuracy of the buried point data.
Drawings
Fig. 1 is a schematic flow chart of a buried point data detection method according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a buried point data detection apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device implementing the buried point data detection method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a buried point data detection method. The execution subject of the buried point data detection method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the buried point data detection method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server side can comprise an independent server, and can also comprise a cloud server which provides basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data and an artificial intelligence platform.
Referring to fig. 1, a flow chart of a buried point data detection method according to an embodiment of the present invention is shown, in the embodiment of the present invention, the buried point data detection method includes the following steps S1-S6:
s1, acquiring buried point requirements, classifying the buried point requirements to obtain buried point event types, and configuring target buried point parameters according to the buried point requirements.
In the embodiment of the present invention, the buried point requirement may be what behavior or event is captured, processed, and transmitted for a specific user, for example, a certain icon click number of the user, a duration of watching a certain video, and the like, where the buried point requirement may be related technologies and implementation processes thereof for capturing, processing, and transmitting for a specific user behavior or event. The buried point event types comprise exposure events, article reading completion events and the like. The target buried point parameter may be data that a user needs to collect when generating a buried point event.
In an optional embodiment of the present invention, the demand for embedding points may be selected according to the business demand provided by business personnel, for example, when the business demand of a company enterprise is that it is desired to know what articles the user is interested in, the demand for embedding points may be the article reading completion, speed, time, and the like of the user.
According to the embodiment of the invention, the buried point event type is obtained by classifying the buried point demands, so that the corresponding buried point event can be conveniently searched subsequently, the time for detecting the buried point data is reduced, and the efficiency for detecting the buried point data is improved.
Further, in an optional embodiment of the present invention, the classifying the embedded point demand to obtain an embedded point event type includes:
performing feature extraction on the buried point requirement to obtain a feature vector set;
and classifying the feature vector set by using a pre-constructed naive Bayes classifier to obtain the buried point event type.
In the embodiment of the invention, the naive Bayes classifier can be a series of simple probability classifiers based on strong (naive) independence between assumed features by applying Bayes theorem.
According to the embodiment of the invention, the target buried point parameters are configured according to the buried point requirements, so that the buried point data can be conveniently compared in the subsequent process, and redundant, missing or wrong buried point data can be found out, thereby improving the accuracy of buried point data detection.
Further, in an optional embodiment of the present invention, the configuring target burial point parameters according to the burial point requirements includes:
acquiring a buried point event type and a service requirement corresponding to the buried point requirement;
and screening out the buried points corresponding to the target buried point parameter configuration which accords with the preset definition from the buried point parameters corresponding to the buried point event types based on the service requirements.
In the embodiment of the invention, the service requirement can be the requirement of the company enterprise service on buried point data, for example, the song listening duration and the song listening type of a user need to be acquired to know what music the user is interested in. The preset definition may be a parameter that is helpful to address business needs.
In the optional embodiment of the invention, the specific buried point event corresponding to the buried point demand is determined by analyzing the buried point demand, and then the business demand is derived from the buried point event, so that research and development personnel can determine the target buried point parameter conveniently, and the accuracy of data analysis based on the buried point data is improved.
And S2, generating a buried point data table according to the target buried point parameters, and issuing the buried point data table to a preset target client.
In the embodiment of the invention, the buried point data table comprises buried point events corresponding to target buried point parameters. The preset target client may be a specific page or APP in the terminal used by the user.
According to the embodiment of the invention, the buried point data table is generated according to the target buried point parameters, a template is provided for the buried point data needing to be collected, and the accuracy of buried point data collection is improved.
Further, as an optional embodiment of the present invention, the generating a buried point data table according to the target buried point parameter includes:
carrying out blood margin tracking on the target buried point parameter to obtain a buried point requirement corresponding to the target buried point parameter;
classifying the target buried point parameters according to the buried point event type corresponding to the buried point demand to obtain classified target buried point parameters;
and storing the classified target buried point parameters into a data table according to the classification to obtain a buried point data table.
In the optional embodiment of the invention, the corresponding category of each target buried point is confirmed by tracking the origin of the target buried point parameter, and the target buried point parameter is classified and stored in a pre-constructed data table, so that the comparison between a subsequent buried point event and the target buried point parameter is facilitated, and the efficiency of buried point parameter comparison is improved.
According to the embodiment of the invention, the buried point data table is issued to the preset target client, so that the target client can acquire the buried point data required by the buried point data table, the accuracy of buried point data acquisition is improved, the number of the buried point data acquisition is reduced, and the buried point data detection efficiency is improved.
And S3, running a preset automatic embedded point demand running script to obtain an embedded point event corresponding to the embedded point demand.
In the embodiment of the invention, the preset automatic embedded point requirement running script can be a script which is written by research personnel and simulates the embedded point event of the user at the target client.
According to the optional embodiment of the invention, the automatic embedded point demand running script is compiled to realize the running of the automatic embedded point demand running script, so that a user is simulated to carry out an embedded point event at the target client, and the intelligent degree of embedded point data acquisition is improved.
In addition, in the optional embodiment of the invention, the acquisition of the buried point data can also be realized by adopting manual simulation.
S4, acquiring a preset rule of the buried point event type, detecting the time and logic abnormal conditions of the buried point event according to the preset rule, and outputting a time and logic abnormal data file.
In the embodiment of the present invention, the preset rule may be different rules defined according to event types, for example, when the buried point event type is an exposure event, the preset rule may be that the same event is exposed and reported for multiple times within a certain time, which may be regarded as an abnormality, when the buried point event type is an article reading event, the preset rule may be that when the length of the article exceeds one screen, whether a sliding bar of the article reading long control slides to more than 80% of the bottom of the article is determined, whether an effective reading speed per second is less than a preset speed, and when the length does not exceed one screen, only the effective reading speed per second is determined.
In an optional embodiment of the invention, the preset rule of the buried point event type can be obtained by defining the rule in advance and storing the rule into the target client, so that the efficiency and the accuracy of buried point data detection are improved.
According to the embodiment of the invention, the time of the embedded point event and the abnormal condition of the logic are detected according to the preset rule, the time and logic abnormal data file is output, and the embedded point data which accords with the preset rule is screened out, so that the accuracy of the embedded point data is improved, and the company loss caused by the embedded point data error is reduced.
Further, as an optional embodiment of the present invention, the detecting, according to the preset rule, the time of the buried point event and the abnormal condition of the logic includes:
acquiring buried point data and an event type corresponding to the buried point event;
selecting corresponding opportunity and logic rule based on the event type;
judging whether the buried point data conforms to the opportunity and the logic rule;
when the buried point data accords with the opportunity and the logic rule, judging that the buried point event is a normal buried point event;
and when the buried point data does not accord with the opportunity and logic rules, judging that the buried point event is an opportunity and logic abnormal data file.
In the embodiment of the present invention, the buried point data may be a specific numerical value of a parameter corresponding to the buried point event, for example, the number of exposures of the same event, the reading completion of an article, and the like.
According to the optional embodiment of the invention, the embedded point data corresponding to the embedded point event is specifically analyzed to judge whether the embedded point event is an opportunity and logic abnormal data file, so that the accuracy of the embedded point data is preliminarily improved.
S5, acquiring the buried point data table from the target client, detecting the abnormal condition of the parameters of the buried point event according to the buried point data table, and outputting a parameter abnormal data file.
In this embodiment of the present invention, the parameter of the buried point event may be the number of times, time, address, and the like of the buried point event, for example, when the buried point event is the opening of the wechat software, the parameter of the buried point event may be the number of times that the user opens the wechat APP, the time of opening the wechat, and the like.
In the optional embodiment of the invention, the embedded point data table can be obtained by searching the storage space of the target client, so that parameter comparison is carried out, the conditions of addition, deletion, errors and the like of the parameters of the embedded point event are ensured, and the accuracy of the embedded point data is improved.
According to the embodiment of the invention, the abnormal condition of the parameter of the buried point event is detected according to the buried point data table, the parameter abnormal data file is output, and the parameter abnormal buried point data is screened out, so that the accuracy of the buried point data is further ensured.
Further, as an optional embodiment of the present invention, the detecting an abnormal condition of the parameter of the buried point event according to the buried point data table includes:
acquiring the event type of the buried point event and the parameters of the buried point event;
finding a buried point event type consistent with the event type from the buried point data table;
based on the buried point event type, disassembling the buried point data table to obtain a target buried point parameter corresponding to the buried point event type;
comparing the parameters of the buried point event with the target buried point parameters one by one, and judging whether the parameters of the buried point event are completely consistent with the target buried point parameters;
when the parameters of the embedded point event are completely consistent with the target embedded point parameters, judging that the data file is a normal data file;
and when the parameters of the buried point event are not completely consistent with the target buried point parameters, judging that the data file is an abnormal data file.
In the optional embodiment of the invention, the embedded point event type consistent with the event type is searched from the embedded point data table through the event type, so that the time consumed by parameter comparison is reduced, and the embedded point data detection efficiency is improved.
And S6, adding the opportunity and logic abnormal data file and the parameter abnormal data file to a preset abnormal problem queue, and sending the abnormal problem queue to corresponding research personnel.
In the embodiment of the present invention, the preset abnormal problem queue may be a message queue for transmitting an abnormal problem to a developer.
According to the embodiment of the invention, the abnormal problem queue is sent to the corresponding research personnel, so that the problem of abnormal buried point data is solved, and the accuracy of the buried point data is improved.
Further, as an optional embodiment of the present invention, the sending the abnormal problem queue to a corresponding developer includes:
identifying the IP address of the research personnel subscribed in the abnormal problem queue;
and sending the opportunity and logic abnormal data files and parameter abnormal data files stored in the abnormal problem queue to the IP address according to the storage time sequence.
In the embodiment of the invention, a research and development staff for processing the abnormal problems acquires the time for automatically sending the abnormal message queue, the logic abnormal data file and the parameter abnormal data file by subscribing the abnormal problem queue, and ensures that the research and development staff can modify the target embedded point parameters according to the time, the logic abnormal data file and the parameter abnormal data file, thereby further improving the accuracy of the embedded point data.
The embodiment of the invention firstly classifies the buried point requirements to obtain the buried point event type, is convenient to reduce the matching difficulty of the buried point data and improve the matching efficiency of the buried point data, configures target buried point parameters according to the buried point event type, and sends a buried point data table generated according to the target buried point parameters to a preset target client, thereby being capable of screening out the target buried point event in the buried point behaviors performed by a client user and ensuring the accuracy of the buried point data, secondly obtains the preset rule of the buried point event type, detects the time of the buried point event and the abnormal condition of logic according to the preset rule, outputs a time and logic abnormal data file to ensure that the abnormal buried point data is screened out, thereby improving the accuracy of the buried point data, and finally obtains the buried point data table from the target client and detects the abnormal condition of the parameters of the buried point event according to the buried point data table, and outputting a parameter abnormal data file, ensuring that the parameters of the buried point data are not increased, reduced or wrong from the parameter dimension, and further improving the accuracy of the buried point data. Therefore, the buried point data detection method provided by the embodiment of the invention can improve the accuracy of the buried point data.
Fig. 2 is a functional block diagram of the buried point data detection apparatus according to the present invention.
The buried point data detection apparatus 100 according to the present invention may be installed in an electronic device. According to the implemented functions, the buried point data detection apparatus 100 may include a buried point data table issuing module 101, a buried point event collecting module 102, and an abnormal data file collecting module 103, where the modules of the present invention may also be referred to as units, which refer to a series of computer program segments capable of being executed by a processor of an electronic device and performing fixed functions, and the computer program segments are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the buried point data table issuing module 101 is configured to acquire buried point requirements, classify the buried point requirements to obtain buried point event types, configure target buried point parameters according to the buried point requirements, generate a buried point data table according to the target buried point parameters, and issue the buried point data table to a preset target client.
In the embodiment of the present invention, the buried point requirement may be what behavior or event is captured, processed, and sent for a specific user, for example, a certain icon click number of the user, a time duration for watching a certain video, and the like, where the buried point requirement may be related technologies and implementation processes thereof for capturing, processing, and sending for the specific user behavior or event. The buried point event types comprise exposure events, article reading completion events and the like. The target buried point parameter may be data that a user needs to collect when generating a buried point event.
In an optional embodiment of the present invention, the demand for embedding points may be selected according to the business demand provided by business personnel, for example, when the business demand of a company enterprise is that it is desired to know what articles the user is interested in, the demand for embedding points may be the article reading completion, speed, time, and the like of the user.
According to the embodiment of the invention, the buried point event type is obtained by classifying the buried point demands, so that the corresponding buried point event can be conveniently searched subsequently, the time for detecting the buried point data is reduced, and the efficiency for detecting the buried point data is improved.
Further, in an optional embodiment of the present invention, the classifying the embedded point demand to obtain an embedded point event type includes:
performing feature extraction on the buried point requirement to obtain a feature vector set;
and classifying the feature vector set by using a pre-constructed naive Bayes classifier to obtain the buried point event type.
In the embodiment of the invention, the naive Bayes classifier can be a series of simple probability classifiers based on strong (naive) independence between assumed features by applying Bayes theorem.
According to the embodiment of the invention, the target buried point parameters are configured according to the buried point requirements, so that the buried point data can be conveniently compared in the subsequent process, and redundant, missing or wrong buried point data can be found out, thereby improving the accuracy of buried point data detection.
Further, in an optional embodiment of the present invention, the configuring target burial point parameters according to the burial point requirements includes:
acquiring a buried point event type and a service requirement corresponding to the buried point requirement;
and screening out the buried points corresponding to the target buried point parameter configuration which accords with the preset definition from the buried point parameters corresponding to the buried point event types based on the service requirements.
In the embodiment of the invention, the service requirement can be the requirement of the company enterprise service on the buried point data, for example, the song listening time and the song listening type of a user need to be obtained when the user wants to know what music is interested. The preset definition may be a parameter that is helpful to address business needs.
In the optional embodiment of the invention, the specific buried point event corresponding to the buried point demand is determined by analyzing the buried point demand, and then the business demand is derived from the buried point event, so that research and development personnel can determine the target buried point parameter conveniently, and the accuracy of data analysis based on the buried point data is improved.
In the embodiment of the invention, the buried point data table comprises buried point events corresponding to target buried point parameters. The preset target client may be a specific page or APP in the terminal used by the user.
According to the embodiment of the invention, the buried point data table is generated according to the target buried point parameters, a template is provided for the buried point data needing to be collected, and the accuracy of buried point data collection is improved.
Further, as an optional embodiment of the present invention, the generating a buried point data table according to the target buried point parameter includes:
carrying out blood margin tracking on the target buried point parameter to obtain a buried point requirement corresponding to the target buried point parameter;
classifying the target buried point parameters according to the buried point event type corresponding to the buried point demand to obtain classified target buried point parameters;
and storing the classified target buried point parameters into a data table according to the classification to obtain a buried point data table.
In the optional embodiment of the invention, the corresponding category of each target buried point is confirmed by tracking the origin of the target buried point parameter, and the target buried point parameter is classified and stored in a pre-constructed data table, so that the comparison between a subsequent buried point event and the target buried point parameter is facilitated, and the efficiency of buried point parameter comparison is improved.
According to the embodiment of the invention, the buried point data table is issued to the preset target client, so that the target client can acquire the buried point data required by the buried point data table, the accuracy of buried point data acquisition is improved, the number of the buried point data acquisition is reduced, and the buried point data detection efficiency is improved.
The buried point event acquisition module 102 is configured to run a preset buried point demand automation running script to obtain a buried point event corresponding to the buried point demand.
In the embodiment of the invention, the preset automatic embedded point requirement running script can be a script which is written by research personnel and simulates the embedded point event of the user at the target client.
According to the optional embodiment of the invention, the automatic embedded point demand running script is compiled to realize the running of the automatic embedded point demand running script, so that a user is simulated to carry out an embedded point event at the target client, and the intelligent degree of embedded point data acquisition is improved.
In addition, in the optional embodiment of the invention, the acquisition of the buried point data can also be realized by adopting manual simulation.
The abnormal data file collection module 103 is configured to obtain a preset rule of the type of the buried point event, detect an opportunity and a logic abnormal situation of the buried point event according to the preset rule, output an opportunity and logic abnormal data file, obtain the buried point data table from the target client, detect an abnormal situation of a parameter of the buried point event according to the buried point data table, output a parameter abnormal data file, add the opportunity and logic abnormal data file and the parameter abnormal data file to a preset abnormal problem queue, and send the abnormal problem queue to a corresponding research and development staff.
In the embodiment of the present invention, the preset rule may be different rules defined according to event types, for example, when the buried point event type is an exposure event, the preset rule may be that the same event is exposed and reported for multiple times within a certain time, which may be regarded as an abnormality, when the buried point event type is an article reading event, the preset rule may be that when the length of the article exceeds one screen, whether a sliding bar of the article reading long control slides to more than 80% of the bottom of the article is determined, whether an effective reading speed per second is less than a preset speed, and when the length does not exceed one screen, only the effective reading speed per second is determined.
In an optional embodiment of the invention, the preset rule of the buried point event type can be obtained by defining the rule in advance and storing the rule into the target client, so that the efficiency and the accuracy of buried point data detection are improved.
According to the embodiment of the invention, the time of the embedded point event and the abnormal condition of the logic are detected according to the preset rule, the time and logic abnormal data file is output, and the embedded point data which accords with the preset rule is screened out, so that the accuracy of the embedded point data is improved, and the company loss caused by the embedded point data error is reduced.
Further, as an optional embodiment of the present invention, the detecting, according to the preset rule, an abnormal condition of a timing and a logic of the buried point event includes:
acquiring buried point data and an event type corresponding to the buried point event;
selecting corresponding opportunity and logic rule based on the event type;
judging whether the buried point data conforms to the opportunity and the logic rule;
when the buried point data accords with the opportunity and the logic rule, judging that the buried point event is a normal buried point event;
and when the buried point data does not accord with the opportunity and logic rules, judging that the buried point event is an opportunity and logic abnormal data file.
In the embodiment of the present invention, the buried point data may be a specific numerical value of a parameter corresponding to the buried point event, for example, the number of exposures of the same event, the reading completion of an article, and the like.
According to the optional embodiment of the invention, the embedded point data corresponding to the embedded point event is specifically analyzed to judge whether the embedded point event is an opportunity and logic abnormal data file, so that the accuracy of the embedded point data is preliminarily improved.
In this embodiment of the present invention, the parameter of the buried point event may be the number of times, time, address, and the like of the buried point event, for example, when the buried point event is the opening of the WeChat software, in this case, the parameter of the buried point event may be the number of times that the user opens the WeChat APP, the time of opening the WeChat, and the like.
In the optional embodiment of the invention, the embedded point data table can be obtained by searching the storage space of the target client, so that parameter comparison is carried out, the conditions of addition, deletion, errors and the like of the parameters of the embedded point event are ensured, and the accuracy of the embedded point data is improved.
According to the embodiment of the invention, the abnormal condition of the parameter of the buried point event is detected according to the buried point data table, the parameter abnormal data file is output, and the parameter abnormal buried point data is screened out, so that the accuracy of the buried point data is further ensured.
Further, as an optional embodiment of the present invention, the detecting an abnormal condition of the parameter of the buried point event according to the buried point data table includes:
acquiring the event type of the buried point event and the parameters of the buried point event;
finding a buried point event type consistent with the event type from the buried point data table;
based on the buried point event type, disassembling the buried point data table to obtain a target buried point parameter corresponding to the buried point event type;
comparing the parameters of the buried point event with the target buried point parameters one by one, and judging whether the parameters of the buried point event are completely consistent with the target buried point parameters;
when the parameters of the embedded point event are completely consistent with the target embedded point parameters, judging that the data file is a normal data file;
and when the parameters of the buried point event are not completely consistent with the target buried point parameters, judging that the data file is an abnormal data file.
In the optional embodiment of the invention, the embedded point event type consistent with the event type is searched from the embedded point data table through the event type, so that the time consumed by parameter comparison is reduced, and the embedded point data detection efficiency is improved.
In the embodiment of the present invention, the preset abnormal problem queue may be a message queue for transmitting an abnormal problem to a developer.
According to the embodiment of the invention, the abnormal problem queue is sent to the corresponding research personnel, so that the problem of abnormal buried point data is solved, and the accuracy of the buried point data is improved.
Further, as an optional embodiment of the present invention, the sending the abnormal problem queue to a corresponding developer includes:
identifying the IP address of the research personnel subscribed in the abnormal problem queue;
and sending the opportunity and logic abnormal data files and parameter abnormal data files stored in the abnormal problem queue to the IP address according to the storage time sequence.
In the embodiment of the invention, a research and development staff for processing the abnormal problems acquires the time for automatically sending the abnormal message queue, the logic abnormal data file and the parameter abnormal data file by subscribing the abnormal problem queue, and ensures that the research and development staff can modify the target embedded point parameters according to the time, the logic abnormal data file and the parameter abnormal data file, thereby further improving the accuracy of the embedded point data.
Fig. 3 is a schematic structural diagram of an electronic device implementing the buried point data detection method according to the present invention.
The electronic device may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further include a computer program, such as a buried point data detection program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device 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, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a buried point data detection program, but also to temporarily store data that has been output or is to be output.
The processor 10 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 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by operating or executing programs or modules (e.g., buried data detecting programs, etc.) stored in the memory 11 and calling data stored in the memory 11.
The communication bus 12 may be a PerIPheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
Fig. 3 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 3 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 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 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized 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 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Optionally, the communication interface 13 may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further include a user interface, which may be a Display (Display), an input unit (such as a Keyboard (Keyboard)), and optionally, a standard wired interface, or 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, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
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.
The buried point data detection program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, and when running in the processor 10, can realize:
acquiring buried point requirements, classifying the buried point requirements to obtain buried point event types, and configuring target buried point parameters according to the buried point requirements;
generating a buried point data table according to the target buried point parameters, and issuing the buried point data table to a preset target client;
running a preset automatic embedded point demand running script to obtain an embedded point event corresponding to the embedded point demand;
acquiring a preset rule of the type of the buried point event, detecting the time and logic abnormal conditions of the buried point event according to the preset rule, and outputting a time and logic abnormal data file;
acquiring the buried point data table from the target client, detecting the abnormal condition of the parameters of the buried point event according to the buried point data table, and outputting a parameter abnormal data file;
and adding the opportunity and logic abnormal data file and the parameter abnormal data file to a preset abnormal problem queue, and sending the abnormal problem queue to corresponding research personnel.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program may implement:
acquiring buried point requirements, classifying the buried point requirements to obtain buried point event types, and configuring target buried point parameters according to the buried point requirements;
generating a buried point data table according to the target buried point parameters, and issuing the buried point data table to a preset target client;
running a preset automatic embedded point demand running script to obtain an embedded point event corresponding to the embedded point demand;
acquiring a preset rule of the type of the buried point event, detecting the time and logic abnormal conditions of the buried point event according to the preset rule, and outputting a time and logic abnormal data file;
acquiring the buried point data table from the target client, detecting the abnormal condition of the parameters of the buried point event according to the buried point data table, and outputting a parameter abnormal data file;
and adding the opportunity and logic abnormal data file and the parameter abnormal data file to a preset abnormal problem queue, and sending the abnormal problem queue to corresponding research personnel.
Further, the computer usable 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.
In the embodiments provided in the present invention, it should be understood that the disclosed electronic device, 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.
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.
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 system claims may also be implemented by one unit or means in software or hardware. The terms 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 buried point data detection method is characterized by comprising the following steps:
acquiring buried point requirements, classifying the buried point requirements to obtain buried point event types, and configuring target buried point parameters according to the buried point requirements;
generating a buried point data table according to the target buried point parameters, and issuing the buried point data table to a preset target client;
running a preset automatic embedded point demand running script to obtain an embedded point event corresponding to the embedded point demand;
acquiring a preset rule of the type of the buried point event, detecting the time and logic abnormal conditions of the buried point event according to the preset rule, and outputting a time and logic abnormal data file;
acquiring the buried point data table from the target client, detecting the abnormal condition of the parameters of the buried point event according to the buried point data table, and outputting a parameter abnormal data file;
and adding the opportunity and logic abnormal data file and the parameter abnormal data file to a preset abnormal problem queue, and sending the abnormal problem queue to corresponding research personnel.
2. The method as claimed in claim 1, wherein the detecting the time and logic abnormal condition of the buried point event according to the predetermined rule comprises:
acquiring buried point data and an event type corresponding to the buried point event;
selecting corresponding opportunity and logic rule based on the event type;
judging whether the buried point data conforms to the opportunity and the logic rule;
when the buried point data accords with the opportunity and the logic rule, judging that the buried point event is a normal buried point event;
and when the buried point data does not accord with the opportunity and logic rules, judging that the buried point event is an opportunity and logic abnormal data file.
3. The buried point data detection method of claim 1, wherein the detecting of the abnormal condition of the parameter of the buried point event according to the buried point data table comprises:
acquiring the event type of the buried point event and the parameters of the buried point event;
finding a buried point event type consistent with the event type from the buried point data table;
based on the buried point event type, disassembling the buried point data table to obtain a target buried point parameter corresponding to the buried point event type;
comparing the parameters of the buried point event with the target buried point parameters one by one, and judging whether the parameters of the buried point event are completely consistent with the target buried point parameters;
when the parameters of the embedded point event are completely consistent with the target embedded point parameters, judging that the data file is a normal data file;
and when the parameters of the buried point event are not completely consistent with the target buried point parameters, judging that the data file is an abnormal data file.
4. The buried point data detection method of claim 1, wherein the generating a buried point data table according to the target buried point parameter comprises:
carrying out blood margin tracking on the target buried point parameter to obtain a buried point requirement corresponding to the target buried point parameter;
classifying the target buried point parameters according to the buried point event type corresponding to the buried point demand to obtain classified target buried point parameters;
and storing the classified target buried point parameters into a data table according to the classification to obtain a buried point data table.
5. The method of claim 1, wherein the classifying the buried point requirements to obtain a buried point event type comprises:
extracting a characteristic vector of the buried point requirement;
and classifying the feature vectors by using a pre-constructed naive Bayes classifier to obtain the buried point event type.
6. The method of claim 1, wherein the configuring target burial point parameters according to the burial point requirements comprises:
acquiring a buried point event type and a service requirement corresponding to the buried point requirement,
and screening out the buried points corresponding to the target buried point parameter configuration which accords with the preset definition from the buried point parameters corresponding to the target buried point event type based on the service requirements.
7. The buried point data detection method of claim 1, wherein the sending the abnormal problem queue to a corresponding developer comprises:
identifying the IP address of the research personnel subscribed in the abnormal problem queue;
and sending the opportunity and logic abnormal data files and parameter abnormal data files stored in the abnormal problem queue to the IP address according to the storage time sequence.
8. A buried point data detection apparatus, the apparatus comprising:
the buried point data table issuing module is used for acquiring buried point requirements, classifying the buried point requirements to obtain buried point event types, configuring target buried point parameters according to the buried point requirements, generating a buried point data table according to the target buried point parameters, and issuing the buried point data table to a preset target client;
the embedded point event acquisition module is used for operating a preset embedded point demand automatic operation script to obtain an embedded point event corresponding to the embedded point demand;
and the abnormal data file collection module is used for acquiring a preset rule of the type of the buried point event, detecting the time and logic abnormal conditions of the buried point event according to the preset rule, outputting a time and logic abnormal data file, acquiring the buried point data table from the target client, detecting the abnormal conditions of the parameters of the buried point event according to the buried point data table, outputting a parameter abnormal data file, adding the time and logic abnormal data file and the parameter abnormal data file to a preset abnormal problem queue, and sending the abnormal problem queue to a corresponding research and development personnel.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the buried point data detection method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the buried point data detection method according to any one of claims 1 to 7.
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