CN116149887A - Crowd monitoring method, device, equipment and storage medium based on DMP - Google Patents
Crowd monitoring method, device, equipment and storage medium based on DMP Download PDFInfo
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- CN116149887A CN116149887A CN202111402384.6A CN202111402384A CN116149887A CN 116149887 A CN116149887 A CN 116149887A CN 202111402384 A CN202111402384 A CN 202111402384A CN 116149887 A CN116149887 A CN 116149887A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/079—Root cause analysis, i.e. error or fault diagnosis
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- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
Abstract
The invention discloses a crowd monitoring method, a device, equipment and a computer readable storage medium based on a DMP, belonging to the technical field of data management, wherein the method comprises the following steps: embedding points in the DMP program and the big data program respectively by using the embedded point interface service; when the DMP program or the big data program runs to a corresponding embedded point interface, sending and storing state data of the embedded point interface to a data monitoring library; and in the running process of the DMP program or the big data program, if the state data read from the data monitoring library is abnormal, carrying out data early warning on the buried point interface corresponding to the state data. According to the invention, whether the sending and receiving of the crowd instructions are correct or not is monitored, the calculation and the result of crowd data are judged, and data early warning is carried out on an abnormal buried point interface in the running process of a DMP or a big data program. The problem node is quickly positioned when the automatic crowd circles are timed.
Description
Technical Field
The present invention relates to the field of data management, and in particular, to a crowd monitoring method, device, equipment and computer readable storage medium based on DMP.
Background
Currently, with the continuous rise of advertisement recommendation, many companies have own DMP (Data Management Platform ) platforms. However, if a fault occurs in the crowd-setting process, it is possible that the fault cannot be found and detected in time, and the failure of the crowd-setting task may further affect the operation of the company.
Disclosure of Invention
The invention mainly aims to provide a crowd monitoring method based on DMP, which aims to solve the technical problem that in the prior art, nodes cannot be positioned quickly when automatic crowd circles are timed.
In order to achieve the above object, the present invention provides a crowd monitoring method based on DMP, including:
embedding points in the DMP program and the big data program respectively by using the embedded point interface service;
when the DMP program or the big data program runs to a corresponding embedded point interface, sending and storing state data of the embedded point interface to a data monitoring library;
and in the running process of the DMP program or the big data program, if the state data read from the data monitoring library is abnormal, carrying out data early warning on the buried point interface corresponding to the state data.
Optionally, the step of using the embedded point interface service to perform embedded point in the DMP program and the big data program respectively includes:
and using the embedded point interface service to refer to a pre-stored embedded point state dictionary table, and respectively embedding points in the operation nodes in the DMP program and the big data program to obtain service parameters of each operation node.
Optionally, the step of sending and storing the status data of the buried point interface to a data monitoring library includes:
and triggering a data sending mechanism according to the service parameters, and sending and storing the state data of the buried point interface into a data monitoring library.
Optionally, the crowd monitoring method based on the DMP further includes:
and when the DMP program runs to the buried point interface, sending and storing state data sent by the crowd instruction of the buried point interface into a data monitoring library.
Optionally, the crowd monitoring method based on the DMP further includes:
when the big data program is operated to the buried point interface, sending and storing the crowd instruction receiving, crowd starting calculation, crowd calculation completion, crowd calculation results, crowd data normal, crowd data abnormal or crowd calculation failure state data of the buried point interface to a data monitoring library.
Optionally, the step of reading the status data from the data monitoring library has an abnormality includes:
if one or more of the crowd instruction sent state data obtained by running the DMP program or the crowd instruction received, crowd starting calculation, crowd calculation completion and crowd calculation result state data obtained by running the big data program are abnormal, judging that the state data read from the data monitoring library are abnormal.
Optionally, the step of reading the status data from the data monitoring library has an abnormality, further includes:
after the execution of the running process of the DMP program or the big data program is completed, if the total increase proportion of the same crowd is not in the preset increase proportion interval, judging that the state data read from the data monitoring library is abnormal.
In addition, in order to achieve the above object, the present invention also provides a crowd monitoring device based on a DMP, including:
the embedded point module is used for embedding points in the DMP program and the big data program respectively by using embedded point interface service;
the sending module is used for sending and storing the state data of the embedded point interface into a data monitoring library when the DMP program or the big data program runs to the corresponding embedded point interface;
and the early warning module is used for carrying out data early warning on the buried point interface corresponding to the state data if the state data read from the data monitoring library is abnormal in the running process of the DMP program or the big data program.
In addition, in order to achieve the above object, the present invention also provides a crowd monitoring device based on a DMP, including: the system comprises a memory, a processor and a DMP-based crowd monitoring program stored in the memory and capable of running on the processor, wherein the DMP-based crowd monitoring program realizes the steps of the DMP-based crowd monitoring method when being executed by the processor.
In addition, in order to achieve the above object, the present invention also provides a computer readable storage medium having stored thereon a DMP-based crowd monitoring program, which when executed by a processor, implements the steps of the DMP-based crowd monitoring method as described above.
The embodiment of the invention provides a crowd monitoring method, a device, equipment and a computer readable storage medium based on DMP, which are used for burying points in a DMP program and a big data program respectively by using a burying point interface service; when the DMP program or the big data program runs to a corresponding embedded point interface, sending and storing state data of the embedded point interface to a data monitoring library; and in the running process of the DMP program or the big data program, if the state data read from the data monitoring library is abnormal, carrying out data early warning on the buried point interface corresponding to the state data.
Therefore, the embedded point interface service is used for embedding points in the DMP program for sending the crowd instruction and the big data program for calculating the crowd respectively, and when the DMP program or the big data program runs to the corresponding embedded point interface, various state data of the embedded point interface are sent and stored to the data monitoring library. Meanwhile, whether the sending and receiving of the crowd instructions are correct or not is judged, calculation and results of crowd data are judged, abnormal state data are read from a data monitoring library in the running process of a DMP program or a big data program, and data early warning is conducted on buried point interfaces corresponding to the abnormal state data in time. Therefore, when the automated crowd circles, the problem node can be quickly positioned, if the problem node is abnormal, the problem can be quickly positioned, the problem can be repaired, the problem can be solved, and finally the problem that the marketing strategy cannot be sent to the client due to the fact that the crowd fails to execute is avoided. Similarly, the system can also quickly inform relevant personnel for research and development and operation, and timely repair is performed to avoid task failure from affecting operation work.
Drawings
FIG. 1 is a schematic diagram of a DMP-based crowd monitoring device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for monitoring a crowd based on a DMP according to an embodiment of the invention;
fig. 3 is a schematic diagram of data monitoring and early warning according to an embodiment of the crowd monitoring method based on DMP of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a crowd monitoring device based on a DMP in a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the DMP-based crowd monitoring apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the configuration shown in fig. 1 is not limiting of a DMP-based crowd monitoring device and may include more or fewer components than shown, or certain components in combination, or a different arrangement of components.
As shown in fig. 1, an operating system, a data storage module, a network communication module, a user interface module, and a DMP-based crowd monitor may be included in the memory 1005 as one storage medium.
In the DMP-based crowd monitoring device shown in fig. 1, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the DMP-based crowd monitoring apparatus of the present invention may be provided in the DMP-based crowd monitoring apparatus, and the DMP-based crowd monitoring apparatus calls the DMP-based crowd monitoring program stored in the memory 1005 through the processor 1001 and performs the following operations:
embedding points in the DMP program and the big data program respectively by using the embedded point interface service;
when the DMP program or the big data program runs to a corresponding embedded point interface, sending and storing state data of the embedded point interface to a data monitoring library;
and in the running process of the DMP program or the big data program, if the state data read from the data monitoring library is abnormal, carrying out data early warning on the buried point interface corresponding to the state data.
Further, the processor 1001 may call the DMP-based crowd monitor stored in the memory 1005, and further perform the following operations:
the step of embedding the point in the DMP program and the big data program by using the embedded point interface service comprises the following steps:
and using the embedded point interface service to refer to a pre-stored embedded point state dictionary table, and respectively embedding points in the operation nodes in the DMP program and the big data program to obtain service parameters of each operation node.
Further, the processor 1001 may call the DMP-based crowd monitor stored in the memory 1005, and further perform the following operations:
the step of sending and storing the state data of the embedded point interface to a data monitoring library comprises the following steps:
and triggering a data sending mechanism according to the service parameters, and sending and storing the state data of the buried point interface into a data monitoring library.
Further, the processor 1001 may call the DMP-based crowd monitor stored in the memory 1005, and further perform the following operations:
the crowd monitoring method based on the DMP further comprises the following steps:
and when the DMP program runs to the buried point interface, sending and storing state data sent by the crowd instruction of the buried point interface into a data monitoring library.
Further, the processor 1001 may call the DMP-based crowd monitor stored in the memory 1005, and further perform the following operations:
the crowd monitoring method based on the DMP further comprises the following steps:
when the big data program is operated to the buried point interface, sending and storing the crowd instruction receiving, crowd starting calculation, crowd calculation completion, crowd calculation results, crowd data normal, crowd data abnormal or crowd calculation failure state data of the buried point interface to a data monitoring library.
Further, the processor 1001 may call the DMP-based crowd monitor stored in the memory 1005, and further perform the following operations:
the step of reading the state data from the data monitoring library to have abnormality comprises the following steps:
if one or more of the crowd instruction sent state data obtained by running the DMP program or the crowd instruction received, crowd starting calculation, crowd calculation completion and crowd calculation result state data obtained by running the big data program are abnormal, judging that the state data read from the data monitoring library are abnormal.
Further, the processor 1001 may call the DMP-based crowd monitor stored in the memory 1005, and further perform the following operations:
the step of reading the state data from the data monitoring library to have abnormality further comprises the following steps:
after the execution of the running process of the DMP program or the big data program is completed, if the total increase proportion of the same crowd is not in the preset increase proportion interval, judging that the state data read from the data monitoring library is abnormal.
The embodiment of the invention provides a crowd monitoring method based on a DMP, and referring to FIG. 2, FIG. 2 is a flow chart of a first embodiment of the crowd monitoring method based on the DMP.
In this embodiment, the crowd monitoring method based on DMP includes:
step S10: and embedding points in the DMP program and the big data program respectively by using the embedded point interface service.
In this embodiment, the embedded point interface service performs unified embedded point in the DMP program and the big data program, respectively. In the analysis of big data, the behavior habit and preference of the user are mined and analyzed from the vast data back, products and services which are more in line with the taste of the user are found, and the products and services are purposefully adjusted and optimized according to the requirements of the user, so that the big data is worth. The collection and analysis of the information do not bypass the "buried point". The buried point is to collect corresponding information at a required position, for example, a camera on a road can collect attributes of a vehicle, such as: color, license plate number, vehicle type and other information, and can also collect the behavior of the vehicle, such as: the method has the advantages that the situation that a red light is broken, the situation that a line is pressed, the speed is high, a driver can answer a call in driving or not, and the like is achieved, each buried point is similar to a camera, user behavior data are collected, multi-dimensional cross analysis is conducted on the data, accordingly, a user use scene is truly restored, user requirements are mined, and the maximum value of the full life cycle of the user is further improved.
Step S20: and when the DMP program or the big data program runs to the corresponding embedded point interface, sending and storing the state data of the embedded point interface to a data monitoring library.
In this embodiment, when the DMP (Data Management Platform ) program or the big data program runs to the buried point interface preset in step S10, the data transmission mechanism is automatically triggered, and the status data of the buried point interface is transmitted and stored in the data monitoring library.
Step S30: and in the running process of the DMP program or the big data program, if the state data read from the data monitoring library is abnormal, carrying out data early warning on the buried point interface corresponding to the state data.
In this embodiment, during the running process of the DMP program or the big data program, if the state at the time of starting calculation and completion of calculation is sent and received by the crowd instruction, the state data corresponding to the buried point interface in the data monitoring library is abnormal due to failure in calculation or abnormal data, and the abnormal state data is subjected to data early warning. In practice, if the task of the crowd instruction is abnormal in each stage of execution, data early warning can be performed through short messages or enterprise WeChat and the like, research, development and operation related personnel are rapidly notified, timely rush repair is performed, and the problem that the task of the crowd instruction fails to influence operation work is avoided.
In this embodiment, the buried point interface service is used to perform buried points in the DMP program and the big data program, respectively; when the DMP program or the big data program runs to a corresponding embedded point interface, sending and storing state data of the embedded point interface to a data monitoring library; and in the running process of the DMP program or the big data program, if the state data read from the data monitoring library is abnormal, carrying out data early warning on the buried point interface corresponding to the state data. And (3) burying points in the DMP program for sending the crowd instruction and the big data program for calculating the crowd respectively by using a burying point interface service, and sending and storing various state data of the burying point interface into a data monitoring library when the DMP program or the big data program runs to the corresponding burying point interface. Meanwhile, whether the sending and receiving of the crowd instructions are correct or not is judged, calculation and results of crowd data are judged, abnormal state data are read from a data monitoring library in the running process of a DMP program or a big data program, and data early warning is conducted on buried point interfaces corresponding to the abnormal state data in time. Therefore, when the automated crowd circles, the problem node can be quickly positioned, if the problem node is abnormal, the problem can be quickly positioned, the problem can be repaired, the problem can be solved, and finally the problem that the marketing strategy cannot be sent to the client due to the fact that the crowd fails to execute is avoided. Similarly, the system can also quickly inform relevant personnel for research and development and operation, and timely repair is performed to avoid task failure from affecting operation work.
Optionally, the step of using the embedded point interface service to perform embedded point in the DMP program and the big data program respectively includes:
and using the embedded point interface service to refer to a pre-stored embedded point state dictionary table, and respectively embedding points in the operation nodes in the DMP program and the big data program to obtain service parameters of each operation node.
Table 1:
| sequence number | Crowd status | State data description |
| 1 | 101 | Crowd instruction transmission |
| 2 | 102 | Crowd instruction reception |
| 3 | 103 | The crowd begins to calculate |
| 4 | 104 | Crowd calculation completion |
| 5 | 105 | Crowd calculation results |
| 6 | 106 | Normal crowd data |
| 7 | 107 | Failure of crowd calculation |
| 8 | 108 | Abnormal crowd data |
In this embodiment, as shown in the above table 1 embedded point state dictionary table, when the embedded point interface service is used to perform embedded point in the DMP program and the big data program, the embedded point corresponding to the number of the embedded points meeting the user requirement is performed at the specific position in the DMP program and the big data program according to the corresponding relation of the crowd instruction sending corresponding to the crowd state 101, the crowd instruction receiving corresponding to the crowd state 102, the crowd starting calculation corresponding to the crowd state 103, the crowd calculation completion corresponding to the crowd state 104, the crowd calculation result corresponding to the crowd state 105, the crowd data corresponding to the crowd state 106 being normal, the crowd calculation failure corresponding to the crowd state 107, and the crowd data abnormality corresponding to the crowd state 108, referring to the embedded point state dictionary table of the table 1. In this embodiment, the definition of the crowd status in the embedded status dictionary table is not limited, the description of the status data should conform to the actual operation results of the operation nodes in the DMP program and the big data program, the correspondence between the crowd status and the description of the status data is not limited, and the correspondence between the crowd status and the description of the status data should also conform to the actual operation results of the operation nodes in the DMP program and the big data program.
Table 2:
thus, the embedded point interface service performs embedded points on the operation nodes in the DMP program and the big data program respectively by referring to the pre-stored embedded point state dictionary table shown in the above table 1, so as to obtain service parameters of each operation node, namely, service parameters of each operation node shown in the above table 2: crowd ID, crowd version, crowd status and status data. Table 2 is only an example of the embedded state data, such as crowd ID 111000, crowd version 2021111001, crowd state 101, and state data sent as crowd instructions, or crowd ID 111000, crowd version 2021111001, crowd state 105, and state data 10000, that is, crowd calculation result 10000.
Optionally, the step of sending and storing the status data of the buried point interface to a data monitoring library includes:
and triggering a data sending mechanism according to the service parameters, and sending and storing the state data of the buried point interface into a data monitoring library.
In this embodiment, when the DMP program or the big data program runs to the corresponding embedded point interface, the service parameters of each running node are according to the above table 2: the crowd ID, the crowd version, the crowd state and the state data trigger a data sending mechanism, send the state data in the corresponding service parameters to a data monitoring library, and store the state data in the data monitoring library.
Optionally, the crowd monitoring method based on the DMP further includes:
and when the DMP program runs to the buried point interface, sending and storing state data sent by the crowd instruction of the buried point interface into a data monitoring library.
Table 3:
| sequence number | Crowd status | State data description |
| 1 | 101 | Crowd instruction transmission |
In this embodiment, when the DMP program runs to a corresponding embedded point interface preset by the DMP program, state data sent by a crowd instruction of the embedded point interface is sent and stored to the data monitoring library. For example, according to the description of the crowd status and the data status shown in table 3 above, when the crowd status is 101, the status data sent by the crowd instruction is sent to the data monitoring library, which indicates that the crowd instruction has been sent to the big data program by the DMP program, that is, indicates that the DMP program has sent the crowd instruction.
Optionally, the crowd monitoring method based on the DMP further includes:
when the big data program is operated to the buried point interface, sending and storing the crowd instruction receiving, crowd starting calculation, crowd calculation completion, crowd calculation results, crowd data normal, crowd data abnormal or crowd calculation failure state data of the buried point interface to a data monitoring library.
Table 4:
| sequence number | Crowd status | State data description |
| 2 | 102 | Crowd instruction reception |
| 3 | 103 | The crowd begins to calculate |
| 4 | 104 | Crowd calculation completion |
| 5 | 105 | Crowd calculation results |
| 6 | 106 | Normal crowd data |
| 7 | 107 | Failure of crowd calculation |
| 8 | 108 | Abnormal crowd data |
In this embodiment, when the big data program runs to the corresponding embedded point interface preset by the big data program, state data of the crowd state of the embedded point interface is sent and stored to the data monitoring library. For example, according to the description of the crowd status and the data status shown in table 4 above, when the crowd status is 102, the status data received by the crowd instruction is sent to the data monitoring library, which indicates that the crowd instruction has been sent from the DMP program to the big data program, i.e. indicates that the big data program has received the crowd instruction. Or for example, according to the description of the crowd status and the data status shown in the above table 4, when the crowd status is 107, the status data of the crowd calculation failure is sent to the data monitoring library, which indicates that the crowd calculation process fails, that is, the crowd calculation of the big data program is problematic.
Optionally, the step of reading the status data from the data monitoring library has an abnormality includes:
if one or more of the crowd instruction sent state data obtained by running the DMP program or the crowd instruction received, crowd starting calculation, crowd calculation completion and crowd calculation result state data obtained by running the big data program are abnormal, judging that the state data read from the data monitoring library are abnormal.
In this embodiment, abnormality determination is performed on the status data read in the data monitoring library, and data monitoring is performed on the status data. As shown in the data monitoring and early warning schematic diagram in fig. 3, if one or more data are abnormal during the running process of the DMP program or the big data program, that is, the sending of crowd instructions in the DMP program, or the receiving of crowd instructions in the big data program, the crowd start calculation, the crowd calculation result or the crowd calculation completion, etc., the state data of the corresponding preset buried point is judged to be abnormal, and then the data early warning after the data abnormality is performed, and the data early warning is performed through short messages and/or enterprise WeChat, etc.
Optionally, the step of reading the status data from the data monitoring library has an abnormality, further includes:
after the execution of the running process of the DMP program or the big data program is completed, if the total increase proportion of the same crowd is not in the preset increase proportion interval, judging that the state data read from the data monitoring library is abnormal.
Table 5:
| crowd ID | Crowd version | Execution date | Total number of people |
| 111000 | 2021111001 | 20211110 | 10000 |
| 111000 | 2021111101 | 20211111 | 11000 |
In this embodiment, anomaly judgment is performed on the total number of people after the completion of the crowd instruction operation, and data monitoring is performed on the increase proportion of the total number of people. As shown in the data monitoring and early warning schematic diagram in fig. 3, if the total number increase proportion of the same crowd is not within the preset increase proportion interval after the execution of the operation process of the DMP program or the big data program is completed, it is determined that the state data read from the data monitoring library is abnormal, and then data early warning after the data abnormality is performed, and data early warning is performed through short messages and/or enterprise WeChat and the like. In this embodiment, the preset increment ratio interval is set to [0.9,1.1], i.e., if the total increment ratio is between [0.9,1.1], it is indicated that there is no abnormality in the status data read from the data monitoring library. The total number of people on two adjacent days calculated as shown in the above table 5 has an increase ratio of 11000/10000=1.1, and does not exceed the preset increase ratio interval, and no abnormality exists in the running process of the DMP program and the big data program, and no data early warning is needed.
In addition, the embodiment of the invention also provides a crowd monitoring device based on the DMP, which comprises:
the embedded point module is used for embedding points in the DMP program and the big data program respectively by using embedded point interface service;
the sending module is used for sending and storing the state data of the embedded point interface into a data monitoring library when the DMP program or the big data program runs to the corresponding embedded point interface;
and the early warning module is used for carrying out data early warning on the buried point interface corresponding to the state data if the state data read from the data monitoring library is abnormal in the running process of the DMP program or the big data program.
In addition, the embodiment of the invention also provides a crowd monitoring device based on the DMP, which comprises: the system comprises a memory, a processor and a DMP-based crowd monitoring program stored in the memory and capable of running on the processor, wherein the DMP-based crowd monitoring program realizes the steps of the DMP-based crowd monitoring method when being executed by the processor.
In addition, the embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a crowd monitoring program based on the DMP, and the crowd monitoring program based on the DMP realizes the steps of the crowd monitoring method based on the DMP when being executed by a processor.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The crowd monitoring method based on the DMP is characterized by comprising the following steps of:
embedding points in the DMP program and the big data program respectively by using the embedded point interface service;
when the DMP program or the big data program runs to a corresponding embedded point interface, sending and storing state data of the embedded point interface to a data monitoring library;
and in the running process of the DMP program or the big data program, if the state data read from the data monitoring library is abnormal, carrying out data early warning on the buried point interface corresponding to the state data.
2. The DMP-based crowd monitoring method of claim 1, wherein the step of using the buried point interface service to perform buried points in the DMP program and the big data program, respectively, includes:
and using the embedded point interface service to refer to a pre-stored embedded point state dictionary table, and respectively embedding points in the operation nodes in the DMP program and the big data program to obtain service parameters of each operation node.
3. The DMP-based crowd monitoring method of claim 2, wherein the step of sending and storing status data of the buried point interface to a data monitoring library includes:
and triggering a data sending mechanism according to the service parameters, and sending and storing the state data of the buried point interface into a data monitoring library.
4. The DMP-based crowd monitoring method of claim 3, wherein the DMP-based crowd monitoring method further includes:
and when the DMP program runs to the buried point interface, sending and storing state data sent by the crowd instruction of the buried point interface into a data monitoring library.
5. The DMP-based crowd monitoring method of claim 4, wherein the DMP-based crowd monitoring method further includes:
when the big data program is operated to the buried point interface, sending and storing the crowd instruction receiving, crowd starting calculation, crowd calculation completion, crowd calculation results, crowd data normal, crowd data abnormal or crowd calculation failure state data of the buried point interface to a data monitoring library.
6. The DMP-based crowd monitoring method of claim 5, wherein the step of reading the status data from the data monitoring library for anomalies includes:
if one or more of the crowd instruction sent state data obtained by running the DMP program or the crowd instruction received, crowd starting calculation, crowd calculation completion and crowd calculation result state data obtained by running the big data program are abnormal, judging that the state data read from the data monitoring library are abnormal.
7. The DMP-based crowd monitoring method of claim 6, wherein the step of reading the status data from the data monitoring library for anomalies further comprises:
after the execution of the running process of the DMP program or the big data program is completed, if the total increase proportion of the same crowd is not in the preset increase proportion interval, judging that the state data read from the data monitoring library is abnormal.
8. A DMP-based crowd monitoring apparatus, the DMP-based crowd monitoring apparatus comprising:
the embedded point module is used for embedding points in the DMP program and the big data program respectively by using embedded point interface service;
the sending module is used for sending and storing the state data of the embedded point interface into a data monitoring library when the DMP program or the big data program runs to the corresponding embedded point interface;
and the early warning module is used for carrying out data early warning on the buried point interface corresponding to the state data if the state data read from the data monitoring library is abnormal in the running process of the DMP program or the big data program.
9. A DMP-based crowd monitoring device, the DMP-based crowd monitoring device comprising: a memory, a processor, and a DMP-based crowd monitor stored on the memory and executable on the processor, the DMP-based crowd monitor configured to implement the steps of the DMP-based crowd monitoring method of any one of claims 1 to 7.
10. A computer readable storage medium, wherein a DMP-based crowd monitoring program is stored on the computer readable storage medium, which when executed by a processor, implements the steps of the DMP-based crowd monitoring method of any one of claims 1 to 7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111402384.6A CN116149887A (en) | 2021-11-23 | 2021-11-23 | Crowd monitoring method, device, equipment and storage medium based on DMP |
| PCT/CN2022/131788 WO2023093561A1 (en) | 2021-11-23 | 2022-11-14 | Dmp-based audience segment monitoring method and apparatus, device, and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111402384.6A CN116149887A (en) | 2021-11-23 | 2021-11-23 | Crowd monitoring method, device, equipment and storage medium based on DMP |
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| Publication Number | Publication Date |
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| CN116149887A true CN116149887A (en) | 2023-05-23 |
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| CN202111402384.6A Pending CN116149887A (en) | 2021-11-23 | 2021-11-23 | Crowd monitoring method, device, equipment and storage medium based on DMP |
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| CN (1) | CN116149887A (en) |
| WO (1) | WO2023093561A1 (en) |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190102794A1 (en) * | 2017-10-02 | 2019-04-04 | Jillian Lee Shapiro | Systems and methods for monitoring and evaluating consumer data |
| CN110458485A (en) * | 2019-06-21 | 2019-11-15 | 上海数据交易中心有限公司 | Monitoring method and device, storage medium, terminal, the monitoring system of data distribution operating status |
| CN112312207B (en) * | 2020-11-20 | 2022-11-25 | 广州欢网科技有限责任公司 | Method, device and equipment for getting through traffic between smart television terminal and mobile terminal |
| CN113536148A (en) * | 2021-07-28 | 2021-10-22 | 深圳市酷开网络科技股份有限公司 | Crowd delineating method, device, equipment and storage medium |
| CN113569158A (en) * | 2021-07-28 | 2021-10-29 | 深圳市酷开网络科技股份有限公司 | Crowd delineation task state display method, device, equipment and storage medium |
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2021
- 2021-11-23 CN CN202111402384.6A patent/CN116149887A/en active Pending
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| WO2023093561A1 (en) | 2023-06-01 |
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