A data processing method, device, apparatus, and storage medium

Abstract

The application discloses a data processing method and device, equipment and a storage medium. The method comprises the following steps: acquiring multi-source monitoring data; establishing an association relationship between the multi-source monitoring data; determining a correlation coefficient between the multi-source monitoring data according to the association relationship; and determining to-be-processed data in the multi-source monitoring data according to the correlation coefficient. In the scheme, the association relationship between all the monitoring data is established, so that the comprehensiveness of the association relationship is ensured. When the corresponding correlation coefficient is calculated based on the association relationship between the data and the to-be-processed data is determined, the artificial empiricism error can be avoided, any details in the whole system operation process of data monitoring and analysis are not missed, the problems or abnormal data in the system are positioned comprehensively, accurately and timely, and the processing efficiency is improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of railway mobile communication technology, and in particular to a data processing method and device, equipment and storage medium. BACKGROUND

[0002] In the field of railway mobile communication, from the early dispatch information with low real-time performance to the current control instruction with both precision and real-time performance, in order to ensure the comprehensiveness and accuracy of communication network monitoring, diversified monitoring means are usually used for monitoring. However, in the face of different types of monitoring data in each link, how to accurately and efficiently analyze and explore valuable problems from massive data is crucial for front-line personnel to quickly solve problems. There are many factors that usually cause communication quality fluctuations, but the existing monitoring means are only effective for a part of these factors, and these factors do not only affect the communication quality or only affect the communication quality, that is, the results and causes are in a many-to-many relationship, and the contribution of each cause to the formation of the result is also different. Therefore, the existing method cannot adapt to highly complex and cross-system scenarios. SUMMARY

[0003] Embodiments of the present application provide a data processing method, device, equipment and storage medium, by establishing the relationship between all monitoring data, the comprehensiveness of the association relationship is ensured, so that when calculating the corresponding correlation coefficient based on the association relationship between the data and determining the to-be-processed data, human empiricism errors can be avoided, realizing that data monitoring and analysis accompany the whole process of system operation without missing any details, ensuring comprehensive, accurate and timely positioning of problems or abnormal data in the system, and improving processing efficiency.

[0004] In a first aspect, the embodiments of the present application also provide a data processing method, which comprises:

[0005] obtaining multi-source monitoring data;

[0006] establishing an association relationship between the multi-source monitoring data;

[0007] determining a correlation coefficient between the multi-source monitoring data according to the association relationship;

[0008] determining to-be-processed data in the multi-source monitoring data according to the correlation coefficient.

[0009] Optionally, the above-mentioned establishing an association relationship between the multi-source monitoring data comprises: establishing an association between the multi-source monitoring data based on at least one of the three dimensions of time, space and event.

[0010] Optionally, the above-mentioned determining a correlation coefficient between the multi-source monitoring data according to the association relationship comprises:

[0011] determine a processing rule of the multi-source monitoring data according to parent-child tags in the association relationship;

[0012] determine a correlation coefficient between any two of the multi-source monitoring data according to the processing rule;

[0013] The processing rule includes a first processing rule or a second processing rule.

[0014] Optionally, the processing rule of the multi-source monitoring data according to the parent-child tags in the association relationship includes:

[0015] In a case where the parent-child tags have a dependency relationship, the processing rule of the two data corresponding to the parent-child tags is determined as the first processing rule;

[0016] In a case where the parent-child tags do not have the dependency relationship, the processing rule of the two data corresponding to the parent-child tags is determined as the second processing rule;

[0017] The two data corresponding to the parent-child tags are included in the multi-source data.

[0018] Optionally, the dependency relationship includes that two functional modules corresponding to the parent-child tags cooperatively form a device group, and the two data corresponding to the parent-child tags are derived from the two functional modules.

[0019] Optionally, the first processing rule includes calculating a correlation coefficient of any two of the multi-source data based on a calculation rule.

[0020] The calculation rule includes a pre-designed calculation formula corresponding to any two of the data.

[0021] The second processing rule includes calculating a correlation coefficient of any two of the multi-source data based on a Pearson correlation coefficient formula.

[0022] Optionally, the determining the to-be-processed data in the multi-source monitoring data according to the correlation coefficient includes:

[0023] The data corresponding to the maximum correlation coefficient is determined as the to-be-processed data.

[0024] In a second aspect, an embodiment of the present application further provides a data processing apparatus, which includes:

[0025] An acquisition module is configured to acquire multi-source monitoring data.

[0026] An establishment module is configured to establish an association relationship between any two of the multi-source monitoring data.

[0027] A determination module is configured to determine a correlation coefficient between any two of the multi-source monitoring data according to the association relationship.

[0028] The determining module is further configured to determine the to-be-processed data in the multi-source monitoring data according to the correlation coefficients.

[0029] In a third aspect, an embodiment of the present application further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the data processing method provided by any of the embodiments of the present application is implemented.

[0030] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the data processing method provided by any of the embodiments of the present application is implemented.

[0031] The embodiments of the present application provide a data processing method, device, equipment and storage medium, the method comprising: acquiring multi-source monitoring data; establishing an association relationship between the multi-source monitoring data; determining a correlation coefficient between the multi-source monitoring data according to the association relationship; and determining to-be-processed data in the multi-source monitoring data according to the correlation coefficient. In the present scheme, since the relationship between all the monitoring data is established, the comprehensiveness of the association relationship is ensured, so that when the corresponding correlation coefficient is calculated based on the association relationship between the data and the to-be-processed data is determined, the artificial empiricism error can be avoided, the system running process is not missed in data monitoring and analysis, the problems or abnormal data in the system are positioned comprehensively, accurately and timely, and the processing efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is a flowchart of a data processing method provided by an embodiment of the present application;

[0033] Figure 2 is a schematic diagram of data tagging provided by an embodiment of the present application;

[0034] Figure 3 is a data graph schematic diagram provided by an embodiment of the present application;

[0035] Figure 4 is a structural schematic diagram of a data processing device provided by an embodiment of the present application;

[0036] Figure 5 is a structural schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0037] The present disclosure will be further described below in conjunction with the embodiments shown in the drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings, but not all the structures.

[0038] In addition, in the embodiments of the present application, the words "optionally" or "exemplarily" are used to mean by way of example, illustration or description. Any embodiment or design scheme described as "optionally" or "exemplarily" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design schemes. Rather, the words "optionally" or "exemplarily" are used to present the relevant concept in a specific manner.

[0039] In a railway mobile communication system, there are many factors that can cause fluctuations in communication quality, such as the following aspects: 1, weak coverage caused by reduced anti-interference ability, forming poor quality, and the reasons for weak coverage can be lack of base station coverage, base station equipment failure, etc.; 2, internal system interference; 3, external system interference; 4, unstable performance of transceiver equipment; 5, waveguide effect caused by meteorological factors; 6, coverage failure or out-of-area caused by external force on the antenna; 7, core network equipment problem; 8, transmission network equipment problem; 9, dragging in the handover process caused by insufficient resources of the target cell.

[0040] However, any one of the above detection methods can only be effective for part of the factors, and the above factors do not only affect the communication quality or only affect the communication quality, that is, the result and the cause are in a many-to-many relationship, and the contribution of each cause to the formation of the result is also different. Therefore, how to associate the result and the cause without missing and how to reasonably divide the proportion of the result and the cause become the core of the positioning problem.

[0041] Based on this, the embodiments of the present application provide a data processing method, which can be applied to the field of railway mobile communication system, by analyzing the correlation between numerous isolated multi-source monitoring data in the communication network operation monitoring, to realize comprehensive, accurate and timely positioning of problems or abnormal data in the system, and meanwhile, the method takes into account the convenience of computer program implementation, and can be applied to large-scale platform deployment. In addition, the method can be executed by a data processing apparatus provided by the embodiments of the present application, and the apparatus can be realized by software and / or hardware. In a specific embodiment, the apparatus can be integrated in a computer device, such as a server. The following embodiments will be described by taking the apparatus integrated in the computer device as an example, as shown in the flowchart of the data processing method provided by the embodiments of the present application, the method can include but not limited to the following steps: Figure 1

[0042] S101, acquiring multi-source monitoring data.

[0043] ​The multi-source monitoring data in the embodiments of the present application can include four categories of subject indicators, environmental factors, solution measures, and effect evaluation. Exemplarily, the subject indicators mainly refer to direct detection and measurement indicators formed in the communication service running process in the communication network system, and are usually used to describe indicators such as communication accessibility, communication maintainability, performance deviation degree, network coverage category, mobile performance, system noise, and communication quality. The environmental factors mainly refer to factor descriptive indicators that cause the performance of the communication system to decrease or be unstable due to communication and associated equipment factors, external interference of the system, room environment, weather, and external environmental factors such as topography. The solution measures mainly refer to the sorting and induction of solutions corresponding to abnormal data or related problems, such as communication system internal and external interference troubleshooting, wireless network coverage optimization and measurement, channel resource optimization, handover chain optimization, and physical cell identifier (PCI) coding reservation for weather forming waveguide effectiveness. It should be noted that the same problem or abnormal data has different priority rankings, implementation times, resource arrangements, and secondary problem monitoring methods in different application scenarios. The effect evaluation mainly refers to the comparison and evaluation of the effects before and after the implementation of the optimization scheme and measures in the same scene, the confirmation of the effectiveness of the scheme in the same scene, the confirmation of the adjustment direction and scale to form a database, and the formation of a dynamic experience library combined with the measures and steps corresponding to the problems to be solved.

[0044] In S102, an association relationship between the multi-source monitoring data is established.

[0045] The association relationship in the embodiments of the present application can include an association between the multi-source monitoring data based on at least one of time, space, and event dimensions. Due to the diversification of multi-source data collection, there can be different data sampling time intervals, position expression methods, measurement units, measurement accuracies, and reference standards. Therefore, in addition to calibration and fitting, the association between the monitoring data in terms of time, space, and event dimensions also needs to comply with the type principle. Specifically, the relationship in the time dimension can include a complete containing relationship, a partial overlapping relationship, and a sequential adjacent relationship; the relationship in the space dimension can include a complete containing relationship, a partial overlapping relationship, and a sequential adjacent relationship; and the relationship in the event dimension can include a complete containing relationship, a partial overlapping relationship, and a sequential adjacent relationship.

[0046] Through the factorization information model of the combination of the time dimension, the space dimension, and the event dimension, the four categories of multi-source monitoring data, i.e., the subject indicators, the environmental factors, the solution measures, and the effect evaluation, are analyzed, classified, and multi-dimensionally labeled (this series of processes can also be referred to as factorization processing), so that the association relationship between the data can be established. Figure 2As shown, each dimension can be finely divided according to the actual application scenario, and the labels can be further expanded and deepened. The labels can also form new labels for data through business rules, and form a data network that can be associated and coordinated through the labels, and give it new analysis capabilities.

[0047] In the embodiments of the present application, since the correlation between all monitoring data is established by the traversal principle, the comprehensiveness of the correlation can be ensured while avoiding human empiricism errors. In this way, data monitoring and analysis accompany the whole process of system operation, can remove human experience intervention, realize full analysis without missing any details, ensure problem analysis positioning based on relevant priority, and thus improve processing efficiency. In addition, the correlation between data is established, which makes data analysis accurate and process closed loop, and has quantitative prediction ability, so that the communication system maintenance work has preventive nature.

[0048] S103, determining the correlation coefficient between the two of the multi-source monitoring data according to the correlation.

[0049] Exemplarily, the implementation manner of the present step can include: determining the processing rule of the multi-source monitoring data according to the parent-child labels in the correlation; and further, determining the correlation coefficient between the two of the multi-source monitoring data according to the processing rule.

[0050] In the embodiments of the present application, the processing rule includes a first processing rule or a second processing rule.

[0051] Specifically, the first processing rule can include calculating the correlation coefficient of any two data in the multi-source data based on a calculation rule. The calculation rule includes a pre-designed calculation formula corresponding to any two data. For example, the calculation formula for evaluating the switching quality is switching success rate = switching success times / switching trigger times; and call drop rate = communication abnormal release times / communication successful establishment times. In addition, the working state changes of various internal and external devices of the system, such as excessive optical attenuation or blockage of optical cable, and meteorological data released by the meteorological department, etc. For data in these different situations, the corresponding calculation formula is usually defined by the communication equipment manufacturer, the operator or the national institution in the corresponding monitoring field and maintenance manual.

[0052] The second processing rule can include calculating the correlation coefficient of any two data in the multi-source data based on the Pearson correlation coefficient formula.

[0053] S104, determining the to-be-processed data in the multi-source monitoring data according to the correlation coefficient.

[0054] In the embodiments of the present application, the data corresponding to the maximum correlation coefficient is determined as the to-be-processed data. That is, the correlation coefficient can be understood as a priority, the larger the correlation coefficient, the higher the priority, and then the determined correlation coefficients can be sorted in descending order, and the data with the largest correlation coefficient is determined as the to-be-processed data with the highest priority (indicating that the data may be abnormal, and the corresponding event or problem has a higher importance level), and the data can be processed preferentially.

[0055] The embodiments of the present application provide a data processing method, which comprises: acquiring multi-source monitoring data; establishing an association relationship between each two of the multi-source monitoring data; determining a correlation coefficient between each two of the multi-source monitoring data according to the association relationship; and determining to-be-processed data in the multi-source monitoring data according to the correlation coefficient. In the present scheme, the relationship between all monitoring data is established, ensuring the comprehensiveness of the association relationship, so that when the corresponding correlation coefficient is calculated based on the association relationship between the data and the to-be-processed data is determined, the artificial empiricism error can be avoided, the system operation process is not missed in data monitoring and analysis, and the problems or abnormal data in the system are positioned comprehensively, accurately and timely, and the processing efficiency is improved.

[0056] In an example, the implementation manner of determining the processing rule of the multi-source monitoring data according to the parent-child label in the association relationship in the step S103 can include: in the case that the parent-child label has a dependency relationship, determining that the processing rule of the two data corresponding to the parent-child label is a first processing rule; and in the case that the parent-child label does not have a dependency relationship, determining that the processing rule of the two data corresponding to the parent-child label is a second processing rule. The two data corresponding to the parent-child label in the embodiments of the present application are included in the multi-source data.

[0057] It should be noted that the dependency relationship includes that the two functional modules corresponding to the parent-child label cooperatively form a device assembly, and the two data corresponding to the parent-child label are derived from the two functional modules. The lack of dependency relationship can be understood as that the two functional modules corresponding to the parent-child label belong to different device assemblies, but belong to a unified system. In addition, the parent-child label has a dependency relationship in the time, event and space dimensions.

[0058] In an example, the dependency relationship can be understood as a Key Performance Indicator (KPI) calculation result that has been used in the monitoring data analysis in the existing railway mobile communication detection system. The result is usually a parent event record calculated by a basic acquisition data sub-event record in a certain time and location interval through a calculation rule, for example, as shown in the following table.

[0059] Table 1

[0060]

[0061] Wherein, the parent event C=F (sub-event A, sub-event B) means that the parent event C is calculated by the data involved by the sub-event A and the data involved by the sub-event B, indicating that the event C is certainly related to the time A and the time B, but the time A and the time B are not necessarily related, and F represents a calculation rule. According to the calculation rule F, the positive and negative correlation of the time C and the event A and the event B can be determined.

[0062] If the two data do not have a dependency relationship, that is, the processing rule of the two data is the second processing rule, the correlation coefficient between the two data can be calculated according to the Pearson correlation coefficient formula. That is, the following formula is used for calculation:

[0063]

[0064] Wherein, r represents the Pearson correlation coefficient, n represents the total number of data, and respectively represent the average of the continuous data of the event A and the event B, X i and Y i respectively represent the i-th data in the continuous data of the event A and the event B. The value range of r is -1-1, -1 represents that the two data are negatively correlated, 1 represents that the two data are positively correlated, the positive correlation and the negative correlation mean that the two data can be well described by a straight line equation, and 0 represents that the two data are not correlated, that is, there is no linear relationship between the two data.

[0065] Optionally, according to the actual application scene, the correlation represented by the correlation coefficient can also be subdivided, for example, if the absolute value of the correlation coefficient between the two data is between [0.8, 1], it is considered that the two data are strongly correlated, or the correlation can also be sorted according to the size of the correlation coefficient, as shown in Table 2.

[0066] Table 2

[0067] Data Number Time Range Location Event 1 Event 2 Correlation Coefficient Correlation Degree Link 1 T1-TX Link 2 T1-TX Link 3 ±0.8 T1-TX Figure 3 Figure 4 Figure 4 Figures 1-3 Figure 5 ±0.6 Figure 5 Figure 5 Figure 5 Figure 1 Figure 1 ​ 0 ​

[0068] As shown in ​ , a data graph based on the correlation between two data is established, the circle in the figure represents the node data corresponding to the event, the arrow represents the directed relationship, and the value on the arrow represents the correlation coefficient between the two node data.

[0069] ​ A structure diagram of a data processing device provided by the embodiment of the application, as shown in ​ , the device can include: an acquisition module 401, an establishment module 402, and a determination module 403.

[0070] The acquisition module is configured to acquire multi-source monitoring data.

[0071] The establishment module is configured to establish an association relationship between any two of the multi-source monitoring data.

[0072] The determination module is configured to determine a correlation coefficient between any two of the multi-source monitoring data according to the association relationship.

[0073] The determination module is further configured to determine to-be-processed data in the multi-source monitoring data according to the correlation coefficient.

[0074] In an example, the establishment module is configured to establish the association relationship between any two of the multi-source monitoring data based on at least one of time, space, and event.

[0075] In an example, the determination module is configured to determine a processing rule of the multi-source monitoring data according to a parent-child label in the association relationship, and determine the correlation coefficient between any two of the multi-source monitoring data according to the processing rule.

[0076] The processing rule includes a first processing rule or a second processing rule.

[0077] In an example, the determination module is configured to determine, when the parent-child label has a dependency relationship, the processing rule of two data corresponding to the parent-child label to be the first processing rule, and determine, when the parent-child label does not have the dependency relationship, the processing rule of the two data corresponding to the parent-child label to be the second processing rule.

[0078] The two data corresponding to the parent-child label are included in the multi-source data.

[0079] Further, the dependency relationship includes that two functional modules corresponding to the parent-child label cooperatively form a device group, and the two data corresponding to the parent-child label are derived from the two functional modules.

[0080] In an example, the first processing rule includes calculating the correlation coefficient of any two of the multi-source data based on a calculation rule, and the calculation rule includes a preset calculation formula corresponding to the any two of the data.

[0081] The second processing rule includes calculating the correlation coefficient of any two of the multi-source data based on a Pearson correlation coefficient formula.

[0082] In an example, the determination module is further configured to determine data corresponding to a maximum correlation coefficient as the to-be-processed data.

[0083] The data processing apparatus can perform ​ The provided data processing method has the corresponding devices and beneficial effects in the method.

[0084] ​ A structural schematic diagram of a computer device provided in an embodiment of the present application is shown in FIG. 1. As shown in the figure, the computer device includes a controller 501, a memory 502, an input device 503, and an output device 504. The number of controllers 501 in the computer device can be one or more, and one controller 501 is taken as an example in the figure. The controller 501, the memory 502, the input device 503, and the output device 504 in the computer device can be connected through a bus or other means, and the connection through a bus is taken as an example in the figure. ​ ​ The memory 502 is a computer readable storage medium, which can be used to store software programs, computer executable programs, and modules, such as the program instructions / modules (for example, the acquisition module 401, the establishment module 402, and the determination module 403) corresponding to the data processing method in the embodiment. The controller 501 executes the software programs, instructions, and modules stored in the memory 502, thereby performing various functions and data processing of the computer device, that is, implementing the data processing method described above. ​

[0085] The memory 502 can mainly include a program storage area and a data storage area. The program storage area can store an operating system and at least one application program required by a function; the data storage area can store data created according to the use of the computer, and the like. In addition, the memory 502 can include a high-speed random access memory, and can also include a non-volatile memory, for example, at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some examples, the memory 502 can further include a memory remotely arranged with respect to the controller 501, and these remote memories can be connected to the terminal / server through a network. Examples of the network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. ​ The input device 503 can be used to receive input digital or character information, and to generate key signal inputs related to the user settings and function control of the computer device. The output device 504 can include a display device such as a display screen.

[0086] The input device 503 can be used to receive input digital or character information, and to generate key signal inputs related to the user settings and function control of the computer device. The output device 504 can include a display device such as a display screen.

[0087] The computer executable instructions of the storage medium provided in the embodiment of the present application, when executed by the computer controller, are used to perform a data processing method, which includes the steps shown in FIG. 2.

[0088] The computer executable instructions of the storage medium provided in the embodiment of the present application, when executed by the computer controller, are used to perform a data processing method, which includes the steps shown in FIG. 2. ​

[0089] ​​​Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary universal hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product in essence or in the form of a part of the prior art that makes a contribution, and the computer software product can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in various embodiments of the present application.

[0090] It is worth noting that the modules included in the above data processing apparatus are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized, and are not used to limit the protection scope of the present application.

[0091] Note that the above are only preferred embodiments of the present application and the technical principles applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and those skilled in the art can make various obvious changes, readjustments and substitutions without departing from the scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A data processing method, characterized by, The method comprises: obtaining multi-source monitoring data; establishing an association relationship between the multi-source monitoring data; determining a correlation coefficient between the multi-source monitoring data according to the association relationship; determining to-be-processed data in the multi-source monitoring data according to the correlation coefficient; determining a processing rule of the multi-source monitoring data according to a parent-child tag in the association relationship; determining a correlation coefficient between the multi-source monitoring data according to the processing rule; wherein the processing rule comprises a first processing rule or a second processing rule; determining the processing rule of the multi-source monitoring data according to the parent-child tag in the association relationship comprises: in a case where the parent-child tag has a dependency relationship, determining that a processing rule of two data corresponding to the parent-child tag is a first processing rule; in a case where the parent-child tag does not have a dependency relationship, determining that a processing rule of two data corresponding to the parent-child tag is a second processing rule; wherein the two data corresponding to the parent-child tag are contained in the multi-source monitoring data. The association relationship between the multi-source monitoring data is established based on at least one of three dimensions of time, space and event.

2. The method of claim 1, wherein, The dependency relationship comprises that two functional modules corresponding to the parent-child tag cooperatively form a device group, and the two data corresponding to the parent-child tag are derived from the two functional modules.

3. The method of claim 1, wherein, The first processing rule comprises calculating a correlation coefficient of any two data in the multi-source monitoring data based on a calculation rule; 4. The method of claim 1, wherein, wherein the calculation rule comprises a pre-designed calculation formula corresponding to the any two data; The second processing rule comprises calculating a correlation coefficient of any two data in the multi-source monitoring data based on a Pearson correlation coefficient formula. The to-be-processed data in the multi-source monitoring data is determined according to the correlation coefficient, comprising:

5. The method of claim 1, wherein, determining data corresponding to a maximum correlation coefficient as to-be-processed data. The device comprises:

6. A data processing apparatus, characterized by, an obtaining module configured to obtain multi-source monitoring data; an establishing module configured to establish an association relationship between the multi-source monitoring data; a determining module configured to determine a correlation coefficient between the multi-source monitoring data according to the association relationship; the determining module is further configured to determine to-be-processed data in the multi-source monitoring data according to the correlation coefficient; determining a processing rule of the multi-source monitoring data according to a parent-child tag in the association relationship; determining a correlation coefficient between the multi-source monitoring data according to the processing rule; wherein the processing rule comprises a first processing rule or a second processing rule; determining the processing rule of the multi-source monitoring data according to the parent-child tag in the association relationship comprises: in a case where the parent-child tag has a dependency relationship, determining that a processing rule of two data corresponding to the parent-child tag is a first processing rule; in a case where the parent-child tag does not have a dependency relationship, determining that a processing rule of two data corresponding to the parent-child tag is a second processing rule; wherein the two data corresponding to the parent-child tag are contained in the multi-source monitoring data. The association relationship between the multi-source monitoring data is established based on at least one of three dimensions of time, space and event. The dependency relationship comprises that two functional modules corresponding to the parent-child tag cooperatively form a device group, and the two data corresponding to the parent-child tag are derived from the two functional modules. The first processing rule comprises calculating a correlation coefficient of any two data in the multi-source monitoring data based on a calculation rule; wherein the calculation rule comprises a pre-designed calculation formula corresponding to the any two data; The second processing rule comprises calculating a correlation coefficient of any two data in the multi-source monitoring data based on a Pearson correlation coefficient formula. The to-be-processed data in the multi-source monitoring data is determined according to the correlation coefficient, comprising: determining data corresponding to a maximum correlation coefficient as to-be-processed data. In a case that the parent-child tags do not have a dependency relationship, the processing rule of the two data corresponding to the parent-child tags is determined as a second processing rule. The two data corresponding to the parent-child tags are contained in the multi-source monitoring data.

7. A computer device, comprising: The application provides a data processing method and device, a computer device and a computer program. The application provides a data processing method and device, a computer device and a computer program.

8. An apparatus readable storage medium having stored thereon a computer program, characterized in that, The application provides a data processing method and device, a computer device and a computer program.

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