Business data monitoring method and device, equipment and storage medium

Abstract

The invention provides a business data monitoring method and device, equipment and a storage medium. The method comprises the following steps: monitoring business data corresponding to a target service in real time; according to the monitored current data and preset historical peak data, determining whether the current data is peak breaking data; under the condition that the current data is the peak-breaking data, determining an early warning level corresponding to the peak-breaking data according to a business index carried by the peak-breaking data and a business index carried by the historical peak data; and sending early warning information based on the early warning level. According to the embodiment of the invention, the business data corresponding to the target service are monitored in real time, the current data and the historical peak data are compared, the business peak period is predicted in time, and the corresponding early warning information is sent out according to the early warning level in the business peak period, so that early warning of the business peak is realized.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method, apparatus, device, and storage medium for business data monitoring. Background Art

[0002] With the rapid development of the social economy, the business volume is continuously increasing. During the business peak period, problems such as a sharp increase in business volume and a surge in orders are faced. In the case of a sharp increase in business volume, it is necessary to analyze and warn of the business peak state, and then take corresponding countermeasures.

[0003] However, the existing business monitoring systems can usually only simply monitor business data and cannot detect the arrival of the business peak in time, which easily leads to the paralysis of the business system during the business peak period and cannot warn of the business peak in time. Summary of the Invention

[0004] The main purpose of this application is to provide a method, apparatus, device, and storage medium for business data monitoring, aiming to solve the technical problem that the business system is prone to paralysis during the business peak period and cannot warn of the business peak in time.

[0005] To achieve the above object, this application provides a method for business data monitoring, and the method includes: Real-time monitor the business data corresponding to the target service; According to the monitored current data and the preset historical peak data, determine whether the current data is peak-breaking data; In the case where the current data is peak-breaking data, determine the warning level corresponding to the peak-breaking data according to the business indicators carried by the peak-breaking data and the business indicators carried by the historical peak data; Based on the warning level, send a warning message.

[0006] Optionally, after the real-time monitoring of the business data corresponding to the target service, the method further includes: Perform data processing on the monitored business data; Perform slicing processing on the business data after data processing to obtain a plurality of data slices; each data slice includes the business data within a preset time period; Store the plurality of data slices into a preset database.

[0007] Optionally, after obtaining the plurality of data slices, the method further includes: Display the business data included in each data slice.

[0008] Optionally, the determining whether the current data is peak-breaking data according to the monitored current data and the preset historical peak data includes: Obtain N first service metrics carried by the current data and N second service metrics carried by the historical peak data; the first service metrics correspond to the second service metrics, and N is a positive integer greater than 1; If there is at least one first service metric higher than the corresponding second service metric, determine that the current data is peak-breaking data; If there is no at least one first service metric higher than the corresponding second service metric, determine that the current data is not peak-breaking data.

[0009] Optionally, after determining whether the current data is peak-breaking data according to the monitored current data and the preset historical peak data, the method further includes: Display the peak-breaking data.

[0010] Optionally, determining the warning level corresponding to the peak-breaking data according to the service metrics carried by the peak-breaking data and the service metrics carried by the historical peak data includes: Determine the third service metric carried by the peak-breaking data and the fourth service metric carried by the historical peak data; the third service metric corresponds to the fourth service metric, and the third service metric is higher than the fourth service metric; Calculate the ratio between the third service metric and the fourth service metric; Determine the warning level corresponding to the peak-breaking data according to the ratio.

[0011] Optionally, sending a warning message based on the warning level includes: Query the warning level and the target service in a preset mapping table to obtain a warning strategy and a warning terminal; the mapping table stores the mapping relationship between the warning level, the target service, the warning strategy, and the warning terminal; Send a warning message to the warning terminal, where the warning message includes the warning strategy.

[0012] In addition, to achieve the above object, the present application further provides a service data monitoring device, including: A monitoring module for real-time monitoring of service data corresponding to a target service; A first determination module for determining whether the current data is peak-breaking data according to the monitored current data and the preset historical peak data; A second determination module for determining the warning level corresponding to the peak-breaking data according to the service metrics carried by the peak-breaking data and the service metrics carried by the historical peak data when the current data is peak-breaking data; A warning module for sending a warning message based on the warning level.

[0013] To solve the above technical problems, an embodiment of the present application further provides a computer device, which adopts the following technical solutions: The computer device includes a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the steps of any one of the service data monitoring methods proposed in the embodiments of the present application are implemented.

[0014] To solve the above technical problems, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions: A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps of any one of the service data monitoring methods proposed in the embodiments of the present application are implemented.

[0015] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects: The present application provides a service data monitoring method, device, equipment and storage medium. The above method includes: real-time monitoring of service data corresponding to a target service; determining whether the current data is peak-breaking data according to the monitored current data and preset historical peak data; in the case where the current data is peak-breaking data, determining the warning level corresponding to the peak-breaking data according to the service indicators carried by the peak-breaking data and the service indicators carried by the historical peak data; and sending a warning message based on the warning level. In the embodiments of the present application, the service data corresponding to the target service is monitored in real time, and by comparing the current data with the historical peak data, the business peak period is predicted in a timely manner. During the business peak period, corresponding warning messages are sent according to the warning level to realize the warning of the business peak. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 is an exemplary system architecture diagram to which the present application can be applied; Figure 2 is a flowchart of the service data monitoring method provided by the embodiment of the present application; Figure 3 is a schematic structural diagram of an embodiment of the service data monitoring device provided by the embodiment of the present application; Figure 4 is a basic structural block diagram of the computer device provided by the embodiment of the present application. Detailed implementation manners

[0018] The business data monitoring provided by the embodiments of the present application is applied to a business data monitoring device. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used in the description of the present application in the specification are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of the present application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0019] Referring to "embodiments" herein means that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase does not necessarily refer to the same embodiment each time it appears in the specification, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0020] In order to enable those skilled in the art of this technology to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings.

[0021] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0022] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the terminal devices 101, 102, 103, such as a web browser application, a shopping application, a search application, an instant messaging tool, an email client, a social online platform software, etc.

[0023] The terminal devices 101, 102, and 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, and so on.

[0024] The server 105 can be a server that provides various services, such as a background server that supports the pages displayed on the terminal devices 101, 102, and 103.

[0025] It should be noted that the service data monitoring method provided in the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the service data monitoring device is generally set in the server / terminal device.

[0026] It should be understood that Figure 1 the numbers of the terminal devices, networks, and servers in

[0027] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. Figure 2 Please refer to

[0028] which shows a flowchart of an embodiment of the service data monitoring method proposed in the present application. The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology.

[0029] Optionally, the service data monitoring method provided in the present application can be applied to the freight scenario to monitor freight data and achieve early warning of freight peaks.

[0030] Optionally, the service data monitoring method provided in the present application can be applied to the e-commerce retail scenario to monitor transaction data, such as real-time monitoring of key data such as product view volume, purchase volume, and payment amount, and early warning of business peaks.

[0031] Optionally, the service data monitoring method provided in the present application can be applied to the transportation scenario to detect traffic data, such as real-time monitoring of vehicle scheduling, passenger flow, road congestion, etc., provide real-time scheduling suggestions, optimize transport capacity allocation, and reduce passenger waiting time.

[0032] Optionally, the business data monitoring method provided by this application can be applied to the food delivery scenario to monitor order data during peak dining hours in real time, so as to reasonably arrange food preparation and allocate delivery personnel, improve delivery efficiency, and reduce user waiting time.

[0033] The business data monitoring method provided by the embodiments of this application includes the following steps: S210, monitor the business data corresponding to the target service in real time.

[0034] An optional implementation is to monitor the business data corresponding to the target service through a monitoring tool, such as Flink. Among them, the above-mentioned business data includes but is not limited to order data, transaction amount data, etc.

[0035] Optionally, the monitored business data can be collected once every 1 minute.

[0036] It should be noted that in the freight scenario, the above-mentioned target service is the freight service; in the e-commerce retail scenario, the above-mentioned target service is the e-commerce service.

[0037] S220, determine whether the current data is peak-breaking data according to the monitored current data and the preset historical peak data.

[0038] It should be understood that the business data includes multiple business indicators.

[0039] In this step, historical peak data is pre-stored, and each business indicator carried by the above historical peak data is the highest in the historical data.

[0040] During the process of monitoring business data, determine whether the current data is peak-breaking data according to the monitored current data and the preset historical peak data. Among them, if a certain business indicator carried by the current data is higher than the corresponding business indicator in the historical peak data, it can be determined that the current data is peak-breaking data.

[0041] S230, in the case that the current data is peak-breaking data, determine the warning level corresponding to the peak-breaking data according to the business indicators carried by the peak-breaking data and the business indicators carried by the historical peak data.

[0042] In the case that the current data is peak-breaking data, parse the peak-breaking data to obtain the business indicators carried by the peak-breaking data, parse the historical peak data to obtain the business indicators carried by the historical peak data, and then compare the business indicators carried by the peak-breaking data with the business indicators carried by the historical peak data to determine the warning level corresponding to the peak-breaking data.

[0043] S240, send a warning message based on the warning level.

[0044] In this step, after determining the warning level, different warning messages are sent for different warning levels. For specific implementation manners, please refer to the subsequent embodiments.

[0045] In the embodiments of the present application, the service data corresponding to the target service is monitored in real time, and by comparing the current data with the historical peak data, the business peak period is predicted in a timely manner. During the business peak period, corresponding warning messages are sent according to the warning level to realize the warning of the business peak.

[0046] Optionally, after the service data corresponding to the target service is monitored in real time, the method further includes: Performing data processing on the monitored service data; Performing slicing processing on the service data after data processing to obtain a plurality of data slices; each data slice includes service data within a preset time period; Storing the plurality of data slices into a preset database.

[0047] In this embodiment, after the service data is monitored, data processing is performed on the service data. Specifically, data cleaning and normalization processing can be performed on the service data to remove outliers and invalid data.

[0048] Performing slicing processing on the service data according to a preset time period to obtain a plurality of data slices. Optionally, the preset time period is 5 minutes, that is, the service data is sliced every 5 minutes to generate 5-minute-level service data.

[0049] Furthermore, storing the above data slices into a preset database. Optionally, the above database is a doris database.

[0050] In this embodiment, by performing data processing on the service data, abnormal data and invalid data are cleared, thereby improving the accuracy of the warning result.

[0051] Optionally, after the plurality of data slices are obtained, the method further includes: Displaying the service data included in each data slice.

[0052] In this embodiment, after the plurality of data slices are obtained, the service data included in each data slice can be displayed. For example, if the data slice includes service data within 5 minutes, the service data monitored in each 5-minute period can be displayed to achieve minute-level real-time monitoring.

[0053] Optionally, determining whether the current data is peak-breaking data according to the monitored current data and the preset historical peak data includes: Obtain N first service metrics carried by the current data and N second service metrics carried by the historical peak data; the first service metrics correspond to the second service metrics, and N is a positive integer greater than 1; If there is at least one first service metric higher than the corresponding second service metric, determine that the current data is peak-breaking data; If there is no at least one first service metric higher than the corresponding second service metric, determine that the current data is not peak-breaking data.

[0054] It should be understood that service data carries N service metrics, and N is a positive integer greater than 1.

[0055] When the current data is monitored, parse the current data to obtain N first service metrics carried by the current data; pre-parse the stored historical peak data to obtain N second service metrics carried by the historical peak data. Among them, the first service metrics correspond to the second service metrics.

[0056] Furthermore, compare the N first service metrics and the N second service metrics. If there is at least one first service metric higher than the corresponding second service metric, determine that the current data is peak-breaking data; if there is no at least one first service metric higher than the corresponding second service metric, determine that the current data is not peak-breaking data.

[0057] Optionally, after determining whether the current data is peak-breaking data according to the monitored current data and the preset historical peak data, the method further includes: Display the peak-breaking data.

[0058] In this embodiment, after determining the peak-breaking data, the peak-breaking data can be displayed. Optionally, each service metric in the peak-breaking data can be displayed, or the service metrics in the peak-breaking data that exceed the historical peak can be displayed.

[0059] Optionally, determining the warning level corresponding to the peak-breaking data according to the service metrics carried by the peak-breaking data and the service metrics carried by the historical peak data includes: Determine the third service metric carried by the peak-breaking data and the fourth service metric carried by the historical peak data; the third service metric corresponds to the fourth service metric, and the third service metric is higher than the fourth service metric; Calculate the ratio between the third service metric and the fourth service metric; According to the ratio, determine the warning level corresponding to the peak-breaking data.

[0060] As described above, service data carries N service metrics.

[0061] In this embodiment, the third service indicator carried by the peak-breaking data and the fourth service indicator carried by the historical peak data are determined, where the third service indicator corresponds to the fourth service indicator, and the third service indicator is higher than the fourth service indicator.

[0062] For example, both the peak-breaking data and the historical peak data carry three service indicators: the order volume, the total order amount, and the order unit price. If the order volume in the peak-breaking data is higher than that in the historical peak data, then the order volume indicator carried by the peak-breaking data is determined as the third service indicator, and the order volume indicator carried by the historical peak data is determined as the fourth service indicator.

[0063] Furthermore, calculate the ratio between the third service indicator and the fourth service indicator, and based on the ratio, determine the warning level corresponding to the peak-breaking data. It is easy to understand that the higher the ratio, the greater the gap between the third service indicator and the fourth service indicator, and the higher the corresponding warning level.

[0064] In an optional implementation manner, if the ratio between the third service indicator and the fourth service indicator is between 0 - 5%, the warning level is determined as the first level; if the ratio between the third service indicator and the fourth service indicator is greater than 5% and less than 10%, the warning level is determined as the second level; if the ratio between the third service indicator and the fourth service indicator is greater than or equal to 10%, the warning level is determined as the third level.

[0065] In this embodiment, during the business peak period, based on the gap between the service indicators of the peak-breaking data and the historical peak data, determine the warning level corresponding to the peak-breaking data, so as to perform accurate warning based on the peak-breaking data.

[0066] Optionally, sending a warning message based on the warning level includes: Query the warning level and the target service in a preset mapping table to obtain a warning strategy and a warning terminal; the mapping table stores the mapping relationship between the warning level, the target service, the warning strategy, and the warning terminal; Send a warning message to the warning terminal, where the warning message includes the warning strategy.

[0067] It should be noted that a mapping table is preset, and the mapping table stores the mapping relationship between the warning level, the target service, the warning strategy, and the warning terminal.

[0068] In this embodiment, input the warning level and the target service into the mapping table for query to obtain a warning strategy and a warning terminal. Specifically, the warning terminal can be obtained by querying the target service in the mapping table, and the warning strategy can be obtained by querying the warning level.

[0069] Exemplarily, in the freight scenario, the above warning strategy includes, but is not limited to, increasing inspections, adopting operating strategies such as raising order prices, and adopting technical means such as expanding memory and increasing bandwidth.

[0070] The target service is a freight service and the warning level is the second level. In this case, query the target service in the preset mapping table, determine the driver terminal and the principal terminal as warning terminals, query the warning level in the preset mapping table, and determine the warning strategy as raising the order price.

[0071] In this embodiment, by adopting different warning strategies for different warning levels, and then sending different warning messages, it is possible to prevent the target service from crashing during the business peak period.

[0072] Please refer to Figure 3 , a service data monitoring device 300 provided by an embodiment of the present application, the service data monitoring device 300 includes: A monitoring module 310, configured to monitor the service data corresponding to the target service in real time; A first determination module 320, configured to determine whether the current data is peak-breaking data according to the monitored current data and the preset historical peak data; A second determination module 330, configured to determine the warning level corresponding to the peak-breaking data according to the service metrics carried by the peak-breaking data and the service metrics carried by the historical peak data when the current data is peak-breaking data; A warning module 340, configured to send a warning message based on the warning level.

[0073] Optionally, the service data monitoring device 300 further includes: A first processing module, configured to process the monitored service data; A second processing module, configured to slice the service data after data processing to obtain a plurality of data slices; each data slice includes service data within a preset time period; A storage module, configured to store the plurality of data slices in a preset database.

[0074] Optionally, the service data monitoring device 300 further includes: A display module, configured to display the service data included in each data slice.

[0075] Optionally, the first determination module 320 is specifically configured to: Obtain N first service metrics carried by the current data and N second service metrics carried by the historical peak data; the first service metrics correspond to the second service metrics, and N is a positive integer greater than 1; If there is at least one first service indicator higher than the corresponding second service indicator, determine that the current data is peak-breaking data; If there is no at least one first service indicator higher than the corresponding second service indicator, determine that the current data is not peak-breaking data.

[0076] Optionally, the display module is further configured to: Display the peak-breaking data.

[0077] Optionally, the second determination module 330 is specifically configured to: Determine the third service indicator carried by the peak-breaking data and the fourth service indicator carried by the historical peak data; the third service indicator corresponds to the fourth service indicator, and the third service indicator is higher than the fourth service indicator; Calculate the ratio between the third service indicator and the fourth service indicator; Determine the warning level corresponding to the peak-breaking data according to the ratio.

[0078] Optionally, the warning module 340 is specifically configured to: Query the warning level and the target service in a preset mapping table to obtain a warning strategy and a warning terminal; the mapping table stores the mapping relationship between the warning level, the target service, the warning strategy, and the warning terminal; Send a warning message to the warning terminal, where the warning message includes the warning strategy.

[0079] To solve the above technical problems, an embodiment of the present application further provides a computer device. For details, please refer to Figure 4 , Figure 4 which is the basic structural block diagram of the computer device in this embodiment. The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are communicatively connected to each other through a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art of the present technology can understand that a computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0080] The computer device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can interact with the user through a keyboard, a mouse, a remote control, a touchpad, a voice control device, or the like.

[0081] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or the memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a FlashCard, etc. equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and the external storage device of the computer device 4. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed in the computer device 4, such as the program code of the business data monitoring method. In addition, the memory 41 may also be used to temporarily store various types of data that have been output or will be output.

[0082] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the program code stored in the memory 41 or process data, such as running the program code of the business data monitoring method.

[0083] The network interface 43 may include a wireless network interface or a wired network interface, and the network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0084] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium storing the business data monitoring program, and the business data monitoring program can be executed by at least one processor to enable the at least one processor to execute the steps of the business data monitoring method as described above.

[0085] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware online platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0086] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0087] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structures directly or indirectly using the content of the specification and drawings of the present application in other related technical fields are equally within the scope of the patent protection of the present application.

Claims

1. A business data monitoring method, characterized in that, The method includes: Real-time monitoring of service data corresponding to the target service; Determining whether the current data is peak-breaking data according to the monitored current data and preset historical peak data; In the case where the current data is peak-breaking data, determining the warning level corresponding to the peak-breaking data according to the service metrics carried by the peak-breaking data and the service metrics carried by the historical peak data; Sending a warning message based on the warning level.

2. The method according to claim 1, characterized in that After the real-time monitoring of service data corresponding to the target service, the method further includes: Performing data processing on the monitored service data; Performing slicing processing on the service data after data processing to obtain a plurality of data slices; each data slice includes service data within a preset time period; Storing the plurality of data slices in a preset database.

3. The method according to claim 2, characterized in that, After obtaining the plurality of data slices, the method further includes: Displaying the service data included in each data slice.

4. The method according to claim 1, characterized in that, The determining whether the current data is peak-breaking data according to the monitored current data and preset historical peak data includes: Obtaining N first service metrics carried by the current data and N second service metrics carried by the historical peak data; the first service metrics correspond to the second service metrics, and N is a positive integer greater than 1; If there is at least one first service metric higher than the corresponding second service metric, determining that the current data is peak-breaking data; If there is no at least one first service metric higher than the corresponding second service metric, determining that the current data is not peak-breaking data.

5. The method according to claim 1, wherein After the determining whether the current data is peak-breaking data according to the monitored current data and preset historical peak data, the method further includes: Displaying the peak-breaking data.

6. The method according to claim 1, wherein The determining the warning level corresponding to the peak-breaking data according to the service metrics carried by the peak-breaking data and the service metrics carried by the historical peak data includes: Determining a third service metric carried by the peak-breaking data and a fourth service metric carried by the historical peak data; the third service metric corresponds to the fourth service metric, and the third service metric is higher than the fourth service metric; Calculating the ratio between the third service metric and the fourth service metric; Determining the warning level corresponding to the peak-breaking data according to the ratio.

7. The method according to claim 1, characterized in that The sending a warning message based on the warning level includes: Querying the warning level and the target service in a preset mapping table to obtain a warning strategy and a warning terminal; the mapping table stores the mapping relationship between the warning level, the target service, the warning strategy, and the warning terminal; Sending a warning message to the warning terminal, where the warning message includes the warning strategy.

8. A business data monitoring device, characterized in that, Includes: A monitoring module for real-time monitoring of service data corresponding to the target service; A first determination module for determining whether the current data is peak-breaking data according to the monitored current data and preset historical peak data; A second determination module for, in the case where the current data is peak-breaking data, determining the warning level corresponding to the peak-breaking data according to the service metrics carried by the peak-breaking data and the service metrics carried by the historical peak data; An early warning module, configured to send an early warning message based on the early warning level.

9. A computer device, characterized in that, It includes a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the steps of the service data monitoring method described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps of the service data monitoring method described in any one of claims 1-7 are implemented.

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