Crowd monitoring method, device, equipment and storage medium based on DMP

Abstract

The present invention discloses a crowd monitoring method, device, equipment and computer-readable storage medium based on DMP, which belongs to the field of data management technology. The method is: using a burying point interface service to carry out burying points in a DMP program and a big data program respectively; when the DMP program or the big data program runs to the corresponding burying point interface, the status data of the burying point interface is sent and stored in a data monitoring library; during the operation of the DMP program or the big data program, if the status data read from the data monitoring library is abnormal, a data warning is issued for the burying point interface corresponding to the status data. The present invention monitors whether the sending and receiving of crowd instructions are correct, judges the calculation and results of crowd data, and issues a data warning for abnormal burying points during the operation of the DMP or big data program. It is realized that the problem node can be quickly located when the crowd is automatically delineated.

Description

Technical Field

[0001] The present invention relates to the field of data management, and in particular to a crowd monitoring method, apparatus, device and computer-readable storage medium based on DMP. Background Art

[0002] With the rise of ad recommendations, many companies have developed their own DMPs (Data Management Platforms). Audience segmentation is an essential component of this process. However, if a malfunction occurs during the segmentation process, it may not be discovered and corrected in a timely manner. This failure can further impact company operations. Summary of the Invention

[0003] The main purpose of the present invention is to provide a crowd monitoring method based on DMP, aiming to solve the technical problem in the prior art of being unable to quickly locate problem nodes when automatically delineating a crowd.

[0004] To achieve the above objectives, the present invention provides a crowd monitoring method based on DMP, the crowd monitoring method based on DMP comprising:

[0005] Use the tracking interface service to track data in the DMP program and big data program respectively;

[0006] When the DMP program or the big data program runs to the corresponding tracking interface, the status data of the tracking interface is sent and stored in the data monitoring library;

[0007] During the operation of the DMP program or the big data program, if there is an abnormality in the status data read from the data monitoring library, a data warning is issued to the tracking interface corresponding to the status data.

[0008] Optionally, the step of using the tracking interface service to track data in the DMP program and the big data program respectively includes:

[0009] Use the tracking interface service to refer to the pre-stored tracking status dictionary table, and perform tracking on the running nodes in the DMP program and the big data program respectively to obtain the service parameters of each of the running nodes.

[0010] Optionally, the step of sending and storing the status data of the tracking interface to a data monitoring library includes:

[0011] The data sending mechanism is triggered according to the service parameters, and the status data of the embedding interface is sent and stored in the data monitoring library.

[0012] Optionally, the DMP-based crowd monitoring method further includes:

[0013] When the DMP program runs to the embedding interface, the status data sent by the crowd instruction of the embedding interface is sent and stored in the data monitoring library.

[0014] Optionally, the DMP-based crowd monitoring method further includes:

[0015] When the big data program runs to the embedding interface, the status data of the embedding interface, including crowd instruction reception, crowd calculation start, crowd calculation completion, crowd calculation results, normal crowd data, abnormal crowd data or crowd calculation failure, is sent and stored to the data monitoring library.

[0016] Optionally, the step of detecting that the status data read from the data monitoring library is abnormal includes:

[0017] If there is an abnormality in one or more of the status data of the crowd instruction sent by running the DMP program, or the status data of the crowd instruction received, the crowd calculation started, the crowd calculation completed, and the crowd calculation result obtained by running the big data program, then it is determined that there is an abnormality in the status data read from the data monitoring library.

[0018] Optionally, the step of detecting that the status data read from the data monitoring library is abnormal further includes:

[0019] After the execution of the DMP program or the big data program is completed, if the total growth rate of the same population is not within the preset increase rate range, it is determined that the status data read from the data monitoring library is abnormal.

[0020] In addition, to achieve the above-mentioned object, the present invention further provides a crowd monitoring device based on DMP, the crowd monitoring device based on DMP comprising:

[0021] The tracking module is used to use the tracking interface service to track data in DMP programs and big data programs respectively;

[0022] A sending module, configured to send and store status data of a corresponding tracking interface to a data monitoring library when the DMP program or the big data program runs to the corresponding tracking interface;

[0023] The early warning module is used to issue a data early warning to the tracking interface corresponding to the status data if there is an abnormality in the status data read from the data monitoring library during the operation of the DMP program or the big data program.

[0024] In addition, to achieve the above-mentioned purpose, the present invention also provides a DMP-based crowd monitoring device, which includes: a memory, a processor, and a DMP-based crowd monitoring program stored in the memory and runnable on the processor. When the DMP-based crowd monitoring program is executed by the processor, the steps of the DMP-based crowd monitoring method as described above are implemented.

[0025] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a DMP-based crowd monitoring program is stored. When the DMP-based crowd monitoring program is executed by a processor, the steps of the DMP-based crowd monitoring method described above are implemented.

[0026] The embodiments of the present invention propose a crowd monitoring method, apparatus, device and computer-readable storage medium based on DMP, which use a burying point interface service to perform burying points in a DMP program and a big data program respectively; when the DMP program or the big data program runs to the corresponding burying point interface, the status data of the burying point interface is sent and stored in a data monitoring library; during the operation of the DMP program or the big data program, if the status data read from the data monitoring library is abnormal, a data warning is issued for the burying point interface corresponding to the status data.

[0027] Therefore, the tracking interface service is used to track data in the DMP program that sends crowd instructions and the big data program that calculates the crowd. When the DMP program or big data program runs to the corresponding tracking interface, various status data of the tracking interface will be sent and stored in the data monitoring library. At the same time, by monitoring whether the sending and receiving of crowd instructions are correct, the calculation and results of the crowd data are judged. During the operation of the DMP program or big data program, abnormal status data is read from the data monitoring library, and data warnings are issued in time for the tracking interface corresponding to the abnormal status data. In this way, when the crowd is automatically delineated, the problem node can be quickly located. If there is an abnormality, the problem node can be quickly located, the problem can be repaired, and the problem can be solved, ultimately avoiding the failure of crowd execution that causes the operation to be unable to send marketing strategies to customers. Similarly, R&D and operation-related personnel can be quickly notified to carry out timely repairs to avoid task failures affecting operational work. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Schematic diagram of the structure of a crowd monitoring device based on DMP in the hardware operating environment involved in an embodiment of the present invention;

[0029] Figure 2 1 is a flow chart of an embodiment of a crowd monitoring method based on DMP according to the present invention;

[0030] Figure 3 Schematic diagram of data monitoring and early warning of an embodiment of a crowd monitoring method based on DMP of the present invention.

[0031] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0032] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0033] Reference Figure 1 , Figure 1 This is a structural diagram of a DMP-based crowd monitoring device in the hardware operating environment involved in an embodiment of the present invention.

[0034] like Figure 1 As shown, the DMP-based crowd monitoring device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to implement communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) memory or a stable non-volatile memory (NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0035] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the crowd monitoring device based on DMP, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0036] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and a crowd monitoring program based on DMP.

[0037] exist Figure 1In the DMP-based crowd monitoring device shown, the network interface 1004 is primarily used for data communication with other devices; the user interface 1003 is primarily used for data interaction with the user; the processor 1001 and the memory 1005 in the DMP-based crowd monitoring device of the present invention can be provided in the DMP-based crowd monitoring device. The DMP-based crowd monitoring device calls the DMP-based crowd monitoring program stored in the memory 1005 via the processor 1001 and performs the following operations:

[0038] Use the tracking interface service to track data in the DMP program and big data program respectively;

[0039] When the DMP program or the big data program runs to the corresponding tracking interface, the status data of the tracking interface is sent and stored in the data monitoring library;

[0040] During the operation of the DMP program or the big data program, if there is an abnormality in the status data read from the data monitoring library, a data warning is issued to the tracking interface corresponding to the status data.

[0041] Furthermore, the processor 1001 may call a crowd monitoring program based on the DMP stored in the memory 1005 and perform the following operations:

[0042] The steps of using the tracking interface service to track data in the DMP program and the big data program respectively include:

[0043] Use the tracking interface service to refer to the pre-stored tracking status dictionary table, and perform tracking on the running nodes in the DMP program and the big data program respectively to obtain the service parameters of each of the running nodes.

[0044] Furthermore, the processor 1001 may call a crowd monitoring program based on the DMP stored in the memory 1005 and perform the following operations:

[0045] The step of sending and storing the status data of the tracking interface to the data monitoring library includes:

[0046] The data sending mechanism is triggered according to the service parameters, and the status data of the embedding interface is sent and stored in the data monitoring library.

[0047] Furthermore, the processor 1001 may call a crowd monitoring program based on the DMP stored in the memory 1005 and perform the following operations:

[0048] The DMP-based crowd monitoring method further includes:

[0049] When the DMP program runs to the embedding interface, the status data sent by the crowd instruction of the embedding interface is sent and stored in the data monitoring library.

[0050] Furthermore, the processor 1001 may call a crowd monitoring program based on the DMP stored in the memory 1005 and perform the following operations:

[0051] The DMP-based crowd monitoring method further includes:

[0052] When the big data program runs to the embedding interface, the status data of the embedding interface, including crowd instruction reception, crowd calculation start, crowd calculation completion, crowd calculation results, normal crowd data, abnormal crowd data or crowd calculation failure, is sent and stored to the data monitoring library.

[0053] Furthermore, the processor 1001 may call a crowd monitoring program based on the DMP stored in the memory 1005 and perform the following operations:

[0054] The step of detecting that the status data read from the data monitoring library is abnormal comprises:

[0055] If there is an abnormality in one or more of the status data of the crowd instruction sent by running the DMP program, or the status data of the crowd instruction received, the crowd calculation started, the crowd calculation completed, and the crowd calculation result obtained by running the big data program, then it is determined that there is an abnormality in the status data read from the data monitoring library.

[0056] Furthermore, the processor 1001 may call a crowd monitoring program based on the DMP stored in the memory 1005 and perform the following operations:

[0057] The step of detecting that the status data read from the data monitoring library is abnormal further includes:

[0058] After the execution of the DMP program or the big data program is completed, if the total growth rate of the same population is not within the preset increase rate range, it is determined that the status data read from the data monitoring library is abnormal.

[0059] The embodiment of the present invention provides a crowd monitoring method based on DMP, referring to Figure 2 , Figure 2 This is a flow chart of a first embodiment of a crowd monitoring method based on DMP according to the present invention.

[0060] In this embodiment, the crowd monitoring method based on DMP includes:

[0061] Step S10: Use the tracking interface service to track data in the DMP program and the big data program respectively.

[0062] In this embodiment, the tracking interface service performs unified tracking in both the DMP program and the big data program. The value of big data lies in mining and analyzing user behavior habits and preferences from the vast amount of data, identifying products and services that best suit their "tastes," and then tailoring and optimizing them based on user needs. The collection and analysis of this information requires tracking. Tracking involves collecting relevant information at the required locations. For example, road cameras can capture vehicle attributes such as color, license plate number, and vehicle model. They can also capture vehicle behavior, such as whether the vehicle ran a red light, crossed the road line, its speed, and whether the driver answered a call while driving. Each tracking point acts like a camera, collecting user behavior data and conducting multi-dimensional cross-analysis to truly restore user usage scenarios, uncover user needs, and ultimately maximize the value of the user's entire lifecycle.

[0063] Step S20: When the DMP program or the big data program runs to the corresponding tracking interface, the status data of the tracking interface is sent and stored in the data monitoring library.

[0064] In this embodiment, when the DMP (Data Management Platform) program or the big data program runs to the tracking interface preset in step S10, the data sending mechanism will be automatically triggered to send the status data of the tracking interface and store it in the data monitoring library.

[0065] Step S30: During the operation of the DMP program or the big data program, if the status data read from the data monitoring library is abnormal, a data warning is issued to the tracking interface corresponding to the status data.

[0066] In this embodiment, during the execution of a DMP program or big data program, if calculations fail or data anomalies occur during the sending and receiving of crowd commands, the start and completion of calculations, and other events, the status data of the corresponding tracking interface in the data monitoring library will be abnormal, and a data warning will be issued for the abnormal status data. In fact, if anomalies occur at any stage of the execution of a crowd command task, a data warning will be issued via SMS or WeChat for Business, quickly notifying R&D and operations personnel and enabling timely repairs to prevent the failure of the crowd command task and the impact on operations.

[0067] In this embodiment, a tracking interface service is used to track data in the DMP program and the big data program respectively. When the DMP program or the big data program runs to the corresponding tracking interface, the status data of the tracking interface is sent and stored in the data monitoring library. During the operation of the DMP program or the big data program, if the status data read from the data monitoring library is abnormal, a data warning is issued for the tracking interface corresponding to the status data. The tracking interface service is used to track data in the DMP program that sends the crowd instruction and the big data program that calculates the crowd. When the DMP program or the big data program runs to the corresponding tracking interface, various status data of the tracking interface are sent and stored in the data monitoring library. At the same time, by monitoring whether the sending and receiving of the crowd instruction is correct, the calculation and results of the crowd data are judged. During the operation of the DMP program or the big data program, abnormal status data is read from the data monitoring library, and a data warning is issued in time for the tracking interface corresponding to the abnormal status data. This allows the problem node to be quickly located during the automated crowd delineation. If there is an abnormality, the problem node can be quickly located, the problem can be repaired, and the problem can be solved, ultimately avoiding the failure of crowd execution that causes the operation to be unable to deliver marketing strategies to customers. Similarly, R&D and operations personnel can be quickly notified to carry out timely repairs to avoid mission failures affecting operations.

[0068] Optionally, the step of using the tracking interface service to track data in the DMP program and the big data program respectively includes:

[0069] Use the tracking interface service to refer to the pre-stored tracking status dictionary table, and perform tracking on the running nodes in the DMP program and the big data program respectively to obtain the service parameters of each of the running nodes.

[0070] Table 1:

[0071] Serial number Crowd status Status data description 1 101 Crowd command sending 2 102 Crowd command reception 3 103 The crowd begins to count 4 104 Crowd calculation completed 5 105 Crowd calculation results 6 106 Population data is normal 7 107 Crowd calculation failure 8 108 Abnormal population data

[0072] In this embodiment, as shown in the tracking state dictionary table in Table 1 above, when tracking is performed in the DMP program and the big data program respectively using the tracking interface service, the tracking state dictionary table in Table 1 above is referred to, and according to the corresponding relationship between crowd state 101 corresponding to crowd instruction sending, crowd state 102 corresponding to crowd instruction receiving, crowd state 103 corresponding to crowd calculation starting, crowd state 104 corresponding to crowd calculation completion, crowd state 105 corresponding to crowd calculation result, crowd state 106 corresponding to crowd data normal, crowd state 107 corresponding to crowd calculation failure, and crowd state 108 corresponding to crowd data abnormality, tracking is performed at specific locations in the DMP program and the big data program in a quantity and size that meets user needs. In this embodiment, the definition of the crowd state in the tracking state dictionary table is not limited, and the description of the state data should conform to the actual operating results of the operating nodes in the DMP program and the big data program. The corresponding relationship between the crowd state and the state data description is not limited, and the corresponding relationship between the crowd state and the state data description should also conform to the actual operating results of the operating nodes in the DMP program and the big data program.

[0073] Table 2:

[0074]

[0075]

[0076] Thus, the tracking interface service can obtain the service parameters of each running node by referring to the pre-stored tracking status dictionary table shown in Table 1 above and tracking the running nodes in the DMP program and the big data program respectively, that is, the service parameters of each running node as shown in Table 2 above: crowd ID, crowd version, crowd status and status data. Among them, Table 2 is only an example of tracking status data, such as the crowd ID is 111000, the crowd version is 2021111001, the crowd status is 101, and the status data is sent by the crowd instruction, or the crowd ID is 111000, the crowd version is 2021111001, the crowd status is 105, and the status data is 10000, that is, the crowd calculation result is 10000.

[0077] Optionally, the step of sending and storing the status data of the tracking interface to a data monitoring library includes:

[0078] The data sending mechanism is triggered according to the service parameters, and the status data of the embedding interface is sent and stored in the data monitoring library.

[0079] In this embodiment, when the DMP program or big data program runs to the corresponding tracking interface, it will trigger the data sending mechanism according to the service parameters of each running node as shown in Table 2 above: crowd ID, crowd version, crowd status and status data, and send the status data in the corresponding service parameters to the data monitoring library, and store the status data in the data monitoring library.

[0080] Optionally, the DMP-based crowd monitoring method further includes:

[0081] When the DMP program runs to the embedding interface, the status data sent by the crowd instruction of the embedding interface is sent and stored in the data monitoring library.

[0082] Table 3:

[0083] Serial number Crowd status Status data description 1 101 Crowd command sending

[0084] In this embodiment, when the DMP program runs to the corresponding tracking interface preset by the DMP program, the status data sent by the crowd instruction of the tracking interface is sent and stored in the data monitoring library. For example, according to the description of the crowd status and 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, indicating that the crowd instruction has been sent by the DMP program to the big data program, that is, the DMP program has completed sending the crowd instruction.

[0085] Optionally, the DMP-based crowd monitoring method further includes:

[0086] When the big data program runs to the embedding interface, the status data of the embedding interface, including crowd instruction reception, crowd calculation start, crowd calculation completion, crowd calculation results, normal crowd data, abnormal crowd data or crowd calculation failure, is sent and stored to the data monitoring library.

[0087] Table 4:

[0088] Serial number Crowd status Status data description 2 102 Crowd command reception 3 103 The crowd begins to count 4 104 Crowd calculation completed 5 105 Crowd calculation results 6 106 Population data is normal 7 107 Crowd calculation failure 8 108 Abnormal population data

[0089] In this embodiment, when the big data program runs to the corresponding embedding interface preset by the big data program, the status data of the crowd status of the embedding interface is sent and stored in the data monitoring library. For example, according to the description of the crowd status and data status shown in Table 4 above, when the crowd status is 102, the status data of the crowd instruction received is sent to the data monitoring library, indicating that the crowd instruction has been sent from the DMP program to the big data program, that is, the big data program has received the crowd instruction. Or for example, according to the description of the crowd status and data status shown in Table 4 above, when the crowd status is 107, the status data of the crowd calculation failure is sent to the data monitoring library, indicating that the crowd calculation process has failed, that is, indicating that there is a problem with the crowd calculation of the big data program.

[0090] Optionally, the step of detecting that the status data read from the data monitoring library is abnormal includes:

[0091] If there is an abnormality in one or more of the status data of the crowd instruction sent by running the DMP program, or the status data of the crowd instruction received, the crowd calculation started, the crowd calculation completed, and the crowd calculation result obtained by running the big data program, then it is determined that there is an abnormality in the status data read from the data monitoring library.

[0092] In this embodiment, the status data read from the data monitoring library is judged to be abnormal, and the status data is monitored. Figure 3 As shown in the data monitoring and early warning diagram, if during the operation 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 start of crowd calculation, the crowd calculation results or the completion of crowd calculation, one or more data are abnormal, then it is judged that the status data of the corresponding preset embedding point is abnormal, and then a data early warning is issued after the data abnormality, and the data early warning is issued through SMS and / or enterprise WeChat.

[0093] Optionally, the step of detecting that the status data read from the data monitoring library is abnormal further includes:

[0094] After the execution of the DMP program or the big data program is completed, if the total growth rate of the same population is not within the preset increase rate range, it is determined that the status data read from the data monitoring library is abnormal.

[0095] Table 5:

[0096] Crowd ID Crowd version Execution Date Total number of people 111000 2021111001 20211110 10000 111000 2021111101 20211111 11000

[0097] In this embodiment, the total number of people after the crowd instruction is executed is judged as abnormal, and the growth rate of the total number of people is monitored. Figure 3 As shown in the data monitoring and early warning diagram, if after the DMP program or big data program is executed, if the total growth rate of the same group of people is not within the preset increase rate range, the status data read from the data monitoring library is judged to be abnormal, and a data early warning is issued after the data abnormality, and the data early warning is issued via SMS and / or WeChat for Business. In this embodiment, the preset increase rate range is set to [0.9, 1.1]. That is, if the total growth rate is between [0.9, 1.1], it indicates that the status data read from the data monitoring library is not abnormal. As shown in Table 5 above, the total number of people calculated for two consecutive days has a growth rate of 11000 / 10000 = 1.1, which does not exceed the preset increase rate range. The operation process of the DMP program and big data program is not abnormal, and no data early warning is required.

[0098] In addition, an embodiment of the present invention further provides a crowd monitoring device based on a DMP, the crowd monitoring device based on a DMP comprising:

[0099] The tracking module is used to use the tracking interface service to track data in DMP programs and big data programs respectively;

[0100] A sending module, configured to send and store status data of a corresponding tracking interface to a data monitoring library when the DMP program or the big data program runs to the corresponding tracking interface;

[0101] The early warning module is used to issue a data early warning to the tracking interface corresponding to the status data if there is an abnormality in the status data read from the data monitoring library during the operation of the DMP program or the big data program.

[0102] In addition, an embodiment of the present invention also provides a DMP-based crowd monitoring device, which includes: a memory, a processor, and a DMP-based crowd monitoring program stored in the memory and executable on the processor. When the DMP-based crowd monitoring program is executed by the processor, the steps of the DMP-based crowd monitoring method described above are implemented.

[0103] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a DMP-based crowd monitoring program. When the DMP-based crowd monitoring program is executed by a processor, the steps of the DMP-based crowd monitoring method described above are implemented.

[0104] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0105] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0106] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0107] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A crowd monitoring method based on DMP, characterized in that: The crowd monitoring method based on DMP includes the following steps: Use the tracking interface service to refer to the pre-stored tracking status dictionary table, and perform tracking on the running nodes in the DMP program and the big data program respectively to obtain the service parameters of each running node; When the DMP program or the big data program runs to the corresponding burying point interface, the data sending mechanism is triggered according to the service parameters, and the status data of the burying point interface is sent and stored in the data monitoring library; wherein, when the DMP program runs to the burying point interface, the status data of the crowd instruction sent by the burying point interface is sent and stored in the data monitoring library; when the big data program runs to the burying point interface, the status data of the crowd instruction received, the crowd calculation started, the crowd calculation completed, the crowd calculation result, the crowd data normal, the crowd data abnormal or the crowd calculation failed of the burying point interface is sent and stored in the data monitoring library; During the operation of the DMP program or the big data program, if there is an abnormality in one or more of the status data of the crowd instruction sent by running the DMP program, or the status data of the crowd instruction received, the crowd calculation started, the crowd calculation completed, and the crowd calculation result obtained by running the big data program, then it is determined that there is an abnormality in the status data read from the data monitoring library, and a data warning is issued for the burying point interface corresponding to the status data.

2. The crowd monitoring method based on DMP according to claim 1, characterized in that: The step of detecting that the status data read from the data monitoring library is abnormal further includes: After the execution of the DMP program or the big data program is completed, if the total growth rate of the same population is not within the preset increase rate range, it is determined that the status data read from the data monitoring library is abnormal.

3. A crowd monitoring device based on DMP, characterized in that: The DMP-based crowd monitoring device includes: The tracking module is used to use the tracking interface service to refer to the pre-stored tracking status dictionary table to track the running nodes in the DMP program and the big data program respectively, and obtain the service parameters of each of the running nodes; A sending module is used to trigger a data sending mechanism according to the service parameters when the DMP program or the big data program runs to the corresponding burying point interface, and send and store the status data of the burying point interface to the data monitoring library; wherein, when the DMP program runs to the burying point interface, the status data of the crowd instruction sent by the burying point interface is sent and stored to the data monitoring library; when the big data program runs to the burying point interface, the status data of the crowd instruction received, the crowd calculation started, the crowd calculation completed, the crowd calculation result, the crowd data normal, the crowd data abnormal or the crowd calculation failed of the burying point interface is sent and stored to the data monitoring library; An early warning module is used to determine that there is an abnormality in the status data read from the data monitoring library during the operation of the DMP program or the big data program, if there is an abnormality in one or more of the status data of the crowd instruction sent by the DMP program or the status data of the crowd instruction received, the crowd calculation started, the crowd calculation completed, and the crowd calculation result obtained by running the big data program, and then issue a data early warning to the burying point interface corresponding to the status data.

4. A crowd monitoring device based on DMP, characterized in that: The DMP-based crowd monitoring device includes: a memory, a processor, and a DMP-based crowd monitoring program stored in the memory and executable on the processor, wherein the DMP-based crowd monitoring program is configured to implement the steps of the DMP-based crowd monitoring method according to any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a crowd monitoring program based on DMP. When the crowd monitoring program based on DMP is executed by a processor, the steps of the crowd monitoring method based on DMP as claimed in any one of claims 1 to 2 are implemented.

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