Data uploading method and device, equipment, storage medium and program product

Abstract

The embodiment of the invention provides a data uploading method and device, equipment, a storage medium and a program product. The method comprises the following steps: processing current to-be-processed consumption data based on a first message queue cluster to obtain uploading data of the first message queue cluster; meanwhile, processing the consumption data based on the second message queue cluster to obtain uploading data of the second message queue cluster; processing the uploading data of each first message queue cluster to obtain a processing result corresponding to the uploading data of each first message queue cluster; and if it is determined that the processing result represents that the uploading data of each first message queue cluster does not meet the preset condition, uploading the uploading data of the second message queue cluster to a security situation awareness platform. The method provided by the invention is used for achieving the effect of improving the real-time performance, continuity and accuracy of data transmission.

Description

Technical Field

[0001] The present application relates to the field of big data, and in particular to a data uploading method, device, equipment, storage medium and program product. Background Art

[0002] In a modern information environment, it is necessary to collect the operational security situation data of business organizations and send it to the security situation awareness platform to determine the operational security of the business organizations.

[0003] In the prior art, message queues are used as a tool for data flow to process the real-time consumption data collected from various business organizations, obtain operational security situation data, and send it to a security situation awareness platform.

[0004] However, in the above method, a message queue is used. When a message queue fails, data transmission is interrupted or inaccurate, thereby affecting the operational security of the business organization. Summary of the invention

[0005] The embodiments of the present application provide a data uploading method, apparatus, device, storage medium and program product to achieve the effect of improving the real-time, continuity and accuracy of data uploading.

[0006] In a first aspect, an embodiment of the present application provides a data uploading method, the method being applied to a computing device, the computing device deploying a first cluster center and a second cluster center; the first cluster center includes two first message queue clusters; the second cluster center includes a second message queue cluster; the method includes:

[0007] Based on the first message queue cluster, the consumption data to be processed is processed to obtain the uploading data of the first message queue cluster; at the same time, the consumption data is processed based on the second message queue cluster to obtain the uploading data of the second message queue cluster; wherein the uploading data represents the operation security situation of the business organization to which the consumption data belongs;

[0008] Processing the data sent by each of the first message queue clusters to obtain corresponding processing results of the data sent by each of the first message queue clusters;

[0009] If it is determined that the processing result indicates that the data sent by each of the first message queue clusters does not meet the preset conditions, the data sent by the second message queue cluster is sent to the security situation awareness platform.

[0010] In a possible implementation, the first cluster center includes a running node and a cold standby node; processing the sent data of each of the first message queue clusters to obtain corresponding processing results of the sent data of each of the first message queue clusters, including:

[0011] If it is determined that the running node is abnormal, the sent data of each first message queue cluster is processed based on the cold standby node to obtain corresponding processing results of the sent data of each first message queue cluster.

[0012] In a possible implementation, based on the cold standby node, the sent data of each of the first message queue clusters is processed to obtain corresponding processing results of the sent data of each of the first message queue clusters, including:

[0013] According to the processing time slice of the uploaded data of each of the first message queue clusters, the historical uploaded data is obtained; wherein the processing time slice of the uploaded data of each of the first message queue clusters is the same; the historical uploaded data is the uploaded data that has been uploaded to the security situation awareness platform within the historical time period;

[0014] The processing result is determined according to the uploaded data of each of the first message queue clusters and the historical uploaded data.

[0015] In a possible implementation, determining the processing result according to the uploaded data of each of the first message queue clusters and the historical uploaded data includes:

[0016] Compare and process the uploaded data of the first message queue cluster with the first data and the second data in the historical uploaded data to determine a first comparison result of the first message queue cluster; wherein the first data is the uploaded data corresponding to the processing time slice within the previous day; the second data is the uploaded data corresponding to the processing time slice one week ago; the first comparison result represents the sudden change of the uploaded data of the first message queue cluster;

[0017] Compare and process the uploaded data of the first message queue cluster and at least one third data in the historical uploaded data to determine a second comparison result of the first message queue cluster; wherein the third data is the uploaded data corresponding to each processing time slice in the previous time period within the time period where the processing time slice is located within the day; the second comparison result represents the continuity of the uploaded data of the first message queue cluster;

[0018] The processing result is determined according to each of the first comparison results and each of the second comparison results.

[0019] In a possible implementation, comparing the uploaded data of the first message queue cluster with the first data and the second data in the historical uploaded data to determine a first comparison result of the first message queue cluster includes:

[0020] Determine an absolute value of a difference between the data sent by the first message queue cluster and the first data as a first increase difference;

[0021] Determine an absolute value of a difference between the data sent by the first message queue cluster and the second data as a second increase difference;

[0022] The first comparison result is determined according to the first amplification difference and the second amplification difference.

[0023] In a possible implementation, comparing the uploaded data of the first message queue cluster with at least one third data in the historical uploaded data to determine a second comparison result of the first message queue cluster includes:

[0024] Determine the difference between the uploaded data of the first message queue cluster and the adjacent uploaded data as the third increase difference; and determine the absolute value of the difference between the third data with the latest processing time slice in each of the third data and the adjacent uploaded data as the fourth increase difference; wherein the processing time slice of the adjacent uploaded data is the previous processing time slice of the processing time slice of the uploaded data of the first message queue cluster;

[0025] Determine the absolute value of the difference between every two adjacent third data in each processing time slice in each of the third data as at least one fifth increase difference; and determine the average value between each of the fifth increase difference and the fourth increase difference as the increase average value;

[0026] The second comparison result is determined according to the third increase difference and the increase average value.

[0027] In a possible implementation, determining the processing result according to each of the first comparison results and each of the second comparison results includes:

[0028] If it is determined that each of the first comparison results indicates that there is a sudden change in the data sent by the first message queue cluster, and it is determined that each of the second comparison results indicates that the continuity of the data sent by the first message queue cluster is abnormal, then it is determined that the processing result indicates that the data sent by each of the first message queue clusters does not meet the preset conditions.

[0029] In a possible implementation, the method further includes:

[0030] If it is determined that an abnormality exists in the running node and an abnormality exists in the cold standby node, the uploaded data of the second message queue cluster is uploaded to the security situation awareness platform.

[0031] In a possible implementation, the method further includes:

[0032] If it is determined that the processing result indicates that the data sent by each of the first message queue clusters meets a preset condition, determining target sent data from the data sent by each of the first message queue clusters;

[0033] Based on the first cluster center, the target uploaded data is uploaded to the security situation awareness platform.

[0034] In a possible implementation manner, determining target uploaded data from uploaded data of each of the first message queue clusters includes:

[0035] If it is determined that the running node of the first cluster center is normal, then based on the running node, a comparison process is performed on the sent data of each of the first message queue clusters;

[0036] If it is determined that the corresponding values ​​of the uploaded data of each of the first message queue clusters are consistent, determining the uploaded data of any of the first message queue clusters as the target uploaded data;

[0037] If it is determined that the corresponding values ​​of the sent data of each of the first message queue clusters are inconsistent, the sent data with a larger corresponding value among the sent data of each of the first message queue clusters is determined as the target sent data.

[0038] In a possible implementation manner, the uploaded data includes but is not limited to: transaction volume, transaction success rate, and latency.

[0039] In a second aspect, an embodiment of the present application provides a data uploading device, the device is applied to a computing device, the computing device deploys a first cluster center and a second cluster center; the first cluster center includes two first message queue clusters; the second cluster center includes a second message queue cluster; the device includes:

[0040] A first processing module is used to process the current consumption data to be processed based on the first message queue cluster to obtain the uploading data of the first message queue cluster; at the same time, the consumption data is processed based on the second message queue cluster to obtain the uploading data of the second message queue cluster; wherein the uploading data represents the operation security situation of the business organization to which the consumption data belongs;

[0041] A second processing module is used to process the sent data of each of the first message queue clusters to obtain corresponding processing results of the sent data of each of the first message queue clusters;

[0042] The sending module is used to send the sent data of the second message queue cluster to the security situation awareness platform if it is determined that the processing result indicates that the sent data of each first message queue cluster does not meet the preset conditions.

[0043] In a possible implementation, the first cluster center includes a running node and a cold standby node; the second processing module is specifically used to: if it is determined that the running node is abnormal, based on the cold standby node, process the uploaded data of each of the first message queue clusters to obtain corresponding processing results of the uploaded data of each of the first message queue clusters.

[0044] In a possible implementation, the second processing module is specifically used to: obtain historical uploaded data according to the processing time slice of the uploaded data of each of the first message queue clusters; wherein the processing time slice of the uploaded data of each of the first message queue clusters is the same; the historical uploaded data is the uploaded data that has been uploaded to the security situation awareness platform within a historical time period; determine the processing result according to the uploaded data of each of the first message queue clusters and the historical uploaded data.

[0045] In a possible implementation, the second processing module is specifically used to: compare the uploaded data of the first message queue cluster with the first data and the second data in the historical uploaded data to determine the first comparison result of the first message queue cluster; wherein the first data is the uploaded data corresponding to the processing time slice within the previous day; the second data is the uploaded data corresponding to the processing time slice one week ago; the first comparison result represents the sudden change of the uploaded data of the first message queue cluster; compare the uploaded data of the first message queue cluster with at least one third data in the historical uploaded data to determine the second comparison result of the first message queue cluster; wherein the third data is the uploaded data corresponding to each processing time slice in the previous time period within the time period of the processing time slice on the day; the second comparison result represents the continuity of the uploaded data of the first message queue cluster; and determine the processing result according to each of the first comparison results and each of the second comparison results.

[0046] In one possible implementation, the second processing module is specifically used to: determine the absolute value of the difference between the uploaded data of the first message queue cluster and the first data, which is the first amplification difference; determine the absolute value of the difference between the uploaded data of the first message queue cluster and the second data, which is the second amplification difference; determine the first comparison result based on the first amplification difference and the second amplification difference.

[0047] In a possible implementation, the second processing module is further specifically used to: determine the difference between the uploaded data of the first message queue cluster and the adjacent uploaded data, which is a third increase difference; and determine the absolute value of the difference between the third data with the latest processing time slice in each of the third data and the adjacent uploaded data, which is a fourth increase difference; wherein the processing time slice of the adjacent uploaded data is the previous processing time slice of the processing time slice of the uploaded data of the first message queue cluster; determine the absolute value of the difference between every two adjacent third data in each of the third data in the processing time slices, which is at least one fifth increase difference; and determine the average value between each of the fifth increase difference and the fourth increase difference, which is the increase average value; determine the second comparison result based on the third increase difference and the increase average value.

[0048] In a possible implementation, the second processing module is further specifically used for: if it is determined that each of the first comparison results indicates that there is a sudden change in the uploaded data of the first message queue cluster, and it is determined that each of the second comparison results indicates that the continuity of the uploaded data of the first message queue cluster is abnormal, then it is determined that the processing result indicates that the uploaded data of each of the first message queue clusters does not meet the preset conditions.

[0049] In a possible implementation, the second processing module is further specifically used to: if it is determined that an abnormality exists in the running node and an abnormality exists in the cold standby node, send the uploaded data of the second message queue cluster to the security situation awareness platform.

[0050] In a possible implementation, the device is also used to: if it is determined that the processing result indicates that the uploaded data of each first message queue cluster meets a preset condition, determine the target uploaded data from the uploaded data of each first message queue cluster; and upload the target uploaded data to the security situation awareness platform based on the first cluster center.

[0051] In a possible implementation, the device is also specifically used for: if it is determined that the running node of the first cluster center is normal, then based on the running node, comparing and processing the uploaded data of each first message queue cluster; if it is determined that the corresponding values ​​of the uploaded data of each first message queue cluster are consistent, then determining that the uploaded data of any first message queue cluster is the target uploaded data; if it is determined that the corresponding values ​​of the uploaded data of each first message queue cluster are inconsistent, then determining that the uploaded data with a larger corresponding value among the uploaded data of each first message queue cluster is the target uploaded data.

[0052] In a possible implementation manner, the uploaded data includes but is not limited to: transaction volume, transaction success rate, and latency.

[0053] In a third aspect, an embodiment of the present application provides a computing device, including: a memory, a processor;

[0054] The memory stores computer-executable instructions;

[0055] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0056] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0057] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0058] The data uploading method, apparatus, equipment, storage medium and program product provided in the embodiments of the present application are implemented by deploying two cluster centers, wherein two message queue clusters are set in one cluster center and one message queue cluster is set in the other cluster center. After obtaining the consumption data to be processed, each message queue cluster needs to process the consumption data to obtain the corresponding uploaded data. According to the uploaded data obtained by the two message queue clusters in the same cluster center, it can be determined whether both message queue clusters in the cluster center have failed. If so, the uploaded data obtained by the message queue cluster in the other cluster center can be uploaded to the security situation awareness platform, thereby ensuring that when a message queue cluster fails, data upload processing can be performed through the message queue cluster in the other cluster center to improve the real-time, continuity and accuracy of data upload. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0060] Figure 1 A schematic diagram of an application scenario provided for this application;

[0061] Figure 2 A flowchart of a method for uploading data provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of a dual-active dual-center security deployment structure provided in an embodiment of the present application;

[0063] Figure 4 A schematic diagram of a stream processing flow based on a message queue cluster provided in an embodiment of the present application;

[0064] Figure 5 A schematic diagram of the structure of a dual-active high-availability mechanism provided in an embodiment of the present application;

[0065] Figure 6 A schematic diagram of a cluster-based data time window provided in an embodiment of the present application;

[0066] Figure 7 A flowchart of another method for uploading data provided in an embodiment of the present application;

[0067] Figure 8 A schematic diagram of the structure of another active-active high-availability mechanism provided in an embodiment of the present application;

[0068] Fig. 9 A structural diagram of another active-active high availability mechanism provided in an embodiment of the present application;

[0069] Fig.10 A schematic diagram of a deployment structure of a data uploading module provided in an embodiment of the present application;

[0070] Fig.11 A schematic diagram of an example of historical data comparison provided in an embodiment of the present application;

[0071] Fig.12 A schematic diagram of a dual-active dual-center data uploading deployment structure provided in an embodiment of the present application;

[0072] Fig.13 A schematic diagram of the structure of a data uploading device provided in an embodiment of the present application;

[0073] Fig.14 A schematic diagram of the structure of a computing device provided in an embodiment of the present application.

[0074] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0075] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0076] First, the terms involved in this application are explained:

[0077] Message queue: a mechanism for implementing asynchronous communication in distributed systems, often used for data flow, decoupling system components, and improving system scalability and reliability.

[0078] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0079] Figure 1 A schematic diagram of an application scenario provided by this application. Figure 1 As shown, the specific application scenario of the present application includes a computing device 101, a security situation awareness platform 102, and a business organization 103. The computing device 101 collects the operation security situation data of the business organization 103 and sends it to the security situation awareness platform 102 to determine the operation security of the business organization 103.

[0080] In one example, by collecting monitoring data of a distributed cluster, the system can detect a faulty cluster and determine the corresponding fault type, thereby formulating an effective repair strategy; or, based on the message offset of a distributed cluster partition and its monitoring strategy information and the corresponding preset threshold, an anomaly judgment is made. Once an anomaly in data reception or transmission is detected, the system can obtain the partition corresponding to the corresponding topic name information and the message offset of the partition; based on the offset and the preset threshold, the fault anomaly can be automatically and timely discovered, and the anomaly information can be proactively conveyed to the business organization through a variety of notification methods.

[0081] However, the above methods may be insufficient in application scenarios with high real-time requirements. Even if the data can be recovered eventually, it may have exceeded the business time limit. Therefore, this type of technology has limitations in supporting scenarios with strict real-time requirements.

[0082] In another example, by using the ant colony algorithm, an effective partition rebalancing plan can be quickly developed using its efficient heuristic strategy.

[0083] However, the above method may increase the message batch size, which means that more data is written to the distributed cluster waiting for consumers to read, which may slow down the overall processing speed and affect real-time performance.

[0084] In response to the above technical problems, the present application proposes the following technical concept: by deploying two cluster centers, one of which is equipped with two message queue clusters, and the other is equipped with one message queue cluster, each message queue cluster needs to process the consumption data to be processed and obtain the corresponding uploaded data. When it is determined that both message queue clusters in the same cluster center have failed, the uploaded data obtained by the message queue cluster in the other cluster center can be used for data upload processing. In this way, the problem of interruption or inaccuracy of data upload caused by failure of the message queue in the prior art can be solved.

[0085] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0086] Figure 2 A flow chart of a data uploading method provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method includes:

[0087] 201. Based on the first message queue cluster, the consumption data to be processed is processed to obtain the uploaded data of the first message queue cluster; at the same time, the consumption data is processed based on the second message queue cluster to obtain the uploaded data of the second message queue cluster; wherein the uploaded data represents the operational security status of the business organization to which the consumption data belongs.

[0088] The execution subject of this embodiment may be a computing device, which deploys a first cluster center and a second cluster center; the first cluster center includes two first message queue clusters; and the second cluster center includes a second message queue cluster.

[0089] Exemplarily, the execution subject of this embodiment may be a computing device, which is referred to as a device for the sake of description. Figure 3 A schematic diagram of a dual-active dual-center security deployment structure provided in an embodiment of the present application is shown in FIG. Figure 3As shown, a dual-active dual-center mechanism can be deployed in the device, that is, two cluster centers are deployed, including a first cluster center and a second cluster center, wherein two message queue clusters are deployed in the first cluster center, namely, two first message queue clusters, cluster A and cluster B, and one message queue cluster is deployed in the second cluster center, namely, the second message queue cluster. For ease of description, the message queue cluster may be referred to as a cluster below. The device obtains the current consumption data to be processed, including detailed data such as transaction business and financial consumption data of some business institutions. Each message queue cluster deployed in the device processes the current consumption data to be processed at the same time, that is, based on each first message queue cluster, the current consumption data to be processed is processed to obtain the uploaded data of each first message queue cluster, such as the operation security situation data, to characterize the operation security situation of the business institution to which the consumption data belongs; at the same time, the obtained consumption data is processed based on the second message queue cluster to obtain the uploaded data of the second message queue cluster.

[0090] For example, Figure 4 A schematic diagram of a stream processing flow based on a message queue cluster provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the device sends the detailed consumption data of the specified business platform to each message queue cluster. For each message queue cluster, it includes a conversion module, a pre-statistics module and a summary module. Among them, the conversion module is responsible for processing these consumption data and identifying the objects that need to be monitored. Then, the pre-statistics module processes the results provided by the transfer module and counts the indicators of each monitored object. Next, the summary module receives the calculation output results of the pre-statistics module, and based on distributed data processing, summarizes the statistical results to generate the final summary statistical results, that is, the uploaded data.

[0091] For example, Figure 5 A schematic diagram of a dual-active high availability mechanism provided in an embodiment of the present application is shown in FIG. Figure 5 As shown in the figure, in this mechanism, cluster B exists as a cold standby for cluster A. When the data upload module A of cluster A fails, cluster A will be stopped and cluster B will be started. Cluster B and cluster A deploy the same conversion module, pre-statistics module, and summary module to perform the same streaming processing on the same detailed data, and finally send the summary results to the data upload module B of cluster B. That is, the General Data Distribution System (GDDS) sends the same detailed data to clusters A and B at the same time, and the data is processed in the same way in the two clusters. Figure 6 A schematic diagram of a cluster-based data time window provided in an embodiment of the present application, such as Figure 6As shown in the figure, the stream processing process based on the message queue cluster can use 10 seconds as a processing time slice, which can be referred to as a time slice below. Six consecutive 10-second data will be merged into 1-minute granularity data. Taking a 2-minute time window as an example, it includes time slices 1 to 12 respectively. The uploaded data includes key indicators such as transaction volume, success rate and latency. Among them, the transaction volume is calculated as follows: transaction volume = sum (time slice 1 to 6); the success rate is calculated as follows: success rate = sum (time slice 1 to 6) successful transaction volume / transaction volume; the latency is calculated as follows: latency = sum (time slice 1 to 6) processing time / transaction volume. In this way, the high availability of data upload is improved. Even if one of the clusters fails, the other can continue to transmit data in real time.

[0092] 202. Process the data sent by each first message queue cluster to obtain corresponding processing results of the data sent by each first message queue cluster.

[0093] For example, in combination Figure 3 Based on any data uploading module in the first cluster center, data uploading module A or data uploading module B, the uploaded data of the two first message queue clusters are first processed through a configurable intelligent integration calculation method, and the processing result can be obtained based on a preset threshold judgment rule or a preset condition, such as whether the numerical value corresponding to the data is within a threshold range, etc., to determine whether the uploaded data of the two first message queue clusters meet the preset conditions.

[0094] 203. If it is determined that the processing result indicates that the data sent by each first message queue cluster does not meet the preset condition, the data sent by the second message queue cluster is sent to the security situation awareness platform.

[0095] For example, based on any data uploading module in the first cluster center, after obtaining the processing result, if it is determined that the uploading data of the two first message queue clusters do not meet the preset conditions, it may indicate that the two first message queue clusters have failed, that is, the first cluster center has a complete failure. In order to ensure the accuracy of the uploading data, Figure 3 Based on the data uploading module of the second cluster center, the uploaded data obtained by the second message queue cluster can be uploaded to the security situation awareness platform to complete the complete process of uploading the security situation data of multiple institutions to the security situation awareness platform.

[0096] In this embodiment, a data uploading method is provided, by deploying two cluster centers, one of which is equipped with two message queue clusters, and the other is equipped with one message queue cluster, and adopting a multiple backup strategy. When an abnormality occurs in a message queue cluster, the backup plan can still ensure that data is uploaded in real time and accurately. At the same time, multiple message queue clusters simultaneously send data to a data uploading module, which integrates, processes and analyzes the data from these sources. In this way, the operational flexibility and processing efficiency of the data uploading module are improved.

[0097] Figure 7 A flow chart of another method for sending data provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, the method includes:

[0098] 301. Based on the first message queue cluster, the consumption data to be processed is processed to obtain the uploaded data of the first message queue cluster; at the same time, the consumption data is processed based on the second message queue cluster to obtain the uploaded data of the second message queue cluster; wherein the uploaded data represents the operational security status of the business organization to which the consumption data belongs.

[0099] Exemplarily, this step may refer to step 201 and will not be described in detail here.

[0100] After step 301 , step 302 or step 306 may be executed.

[0101] 302. If it is determined that an abnormality exists in the running node, the sent data of each first message queue cluster is processed based on the cold standby node to obtain corresponding processing results of the sent data of each first message queue cluster.

[0102] The first cluster center includes running nodes and cold standby nodes.

[0103] The uploaded data includes but is not limited to: transaction volume, transaction success rate and latency.

[0104] Exemplarily, after step 301, Figure 8 A schematic diagram of another active-active high availability mechanism provided in an embodiment of the present application. Fig. 9 A structural diagram of another active-active high availability mechanism provided in an embodiment of the present application is shown in FIG. Figure 8 , 9As shown, the first cluster center includes cluster A and cluster B, and also includes running nodes and cold standby nodes, which correspond to data upload module A and data upload module B respectively, and the data upload module B is used as the cold standby node of the data upload module A. The device performs self-check monitoring on the two data upload modules. If it is determined that the running node is abnormal, the two data upload modules are switched in time, and then based on the cold standby node, the uploaded data of the two first message queue clusters are obtained, wherein each uploaded data includes but is not limited to: transaction volume, transaction success rate and delay, and the uploaded data of the two first message queue clusters are processed to determine whether the uploaded data of the two first message queue clusters meet the preset conditions. It is worth adding that, based on the self-check monitoring, if it is determined that the current running node is normal, based on the current running node, the uploaded data of the two first message queue clusters are judged to meet the preset conditions in the same processing method as the cold standby node, so as to upload the appropriate uploaded data to the security situation awareness platform. In this way, the switching between the two sets AB does not need to rely on the comprehensive judgment of all module and cluster anomalies of the two sets, which simplifies the anomaly judgment logic and improves the stability and efficiency of the upload process.

[0105] In one example, step 302 includes the following steps:

[0106] The first step of step 302 is to obtain historical uploaded data according to the processing time slice of uploaded data of each first message queue cluster; wherein, the processing time slice of uploaded data of each first message queue cluster is the same; the historical uploaded data is the uploaded data that has been uploaded to the security situation awareness platform within the historical time period.

[0107] The second step of step 302 is to determine the processing result according to the uploaded data and the historical uploaded data of each first message queue cluster.

[0108] Exemplarily, when processing uploaded data, when the message queue cluster processes data with a preset processing time slice, each uploaded data has a corresponding processing time slice, and the corresponding processing time slice can be determined based on the timestamp field carried in each uploaded data, for example 11:05:10-11:05:20. Fig.10 A schematic diagram of a deployment structure of a data uploading module provided in an embodiment of the present application is shown in FIG. Fig.10As shown, based on the data collection layer of the data uploading module, the uploading data obtained from the two message queue cluster data of cluster A and cluster B is collected, that is, the uploading data of the two clusters with the same processing time slice is obtained. According to the processing time slice of the uploading data, the preset historical time period before the processing time slice is determined, and the uploading data that has been uploaded to the security situation awareness platform is obtained, that is, the historical uploading data, such as the historical uploading data from 11:05:10 to 11:05:20 every day in the previous week. Based on the historical data comparison layer of the data uploading module, the threshold and judgment rules are set based on the preset trend, and the uploading data of the two first message queue clusters and the historical uploading data are compared and processed, so as to obtain the processing result of whether the uploading data of the two first message queue clusters meet the preset conditions.

[0109] In one example, the second step of step 302 includes:

[0110] Step 1: Compare the uploaded data of the first message queue cluster with the first data and the second data in the historical uploaded data to determine the first comparison result of the first message queue cluster; wherein the first data is the uploaded data corresponding to the processing time slice within the previous day; the second data is the uploaded data corresponding to the processing time slice one week ago; the first comparison result represents the sudden change of the uploaded data of the first message queue cluster.

[0111] Step 2: Compare the uploaded data of the first message queue cluster with at least one third data in the historical uploaded data to determine the second comparison result of the first message queue cluster; wherein the third data is the uploaded data corresponding to each processing time slice in the previous time period within the time period of the processing time slice within the day; the second comparison result represents the continuity of the uploaded data of the first message queue cluster.

[0112] Step three: determine a processing result according to each first comparison result and each second comparison result.

[0113] Specifically, combined Fig.10, based on the historical data comparison layer of the data uploading module, according to the processing time slice of the uploaded data, the historical uploaded data obtained includes the uploaded data corresponding to the processing time slice in the previous day, that is, the first data; it also includes the uploaded data corresponding to the processing time slice a week ago, that is, the second data; it also includes the uploaded data corresponding to each processing time slice in the previous time period within the time period where the processing time slice is located on the same day, that is, multiple third data. The historical data comparison layer compares the uploaded data of each first message queue cluster with the first data and the second data in the historical uploaded data based on the preset comparison rules, and determines the comparison result of each first message queue cluster, that is, the first comparison result, so as to identify whether there is a sudden change in the summary result of the current time slice. At the same time, the historical data comparison layer compares the uploaded data of each first message queue cluster with all the third data based on the preset comparison rules, and obtains the comparison result of each first message queue cluster, that is, the second comparison result, so as to judge whether there is an abnormality in the continuity of the data in the current time slice. The historical data comparison layer analyzes the first comparison results and the second comparison results of the two first message queue clusters based on the preset comparison rules and in combination with the preset rules, and then determines whether the uploaded data of the two first message queue clusters meet the preset conditions, such as whether they are abnormal, based on whether there are sudden changes in the uploaded data of the two first message queue clusters and whether there are abnormalities in the continuity.

[0114] In one example, step one includes:

[0115] The first step is to determine the absolute value of the difference between the uploaded data of the first message queue cluster and the first data, which is the first increase difference.

[0116] The second step is to determine the absolute value of the difference between the uploaded data of the first message queue cluster and the second data, which is the second increase difference.

[0117] The third step is to determine a first comparison result according to the first amplification difference and the second amplification difference.

[0118] For example, in combination Fig.10For each first message queue cluster, the historical data comparison layer calculates the absolute value of the difference between the uploaded data of the first message queue cluster and the first data to obtain the first increase difference, and calculates the absolute value of the difference between the uploaded data of the first message queue cluster and the second data to obtain the second increase difference. According to the preset threshold rule, the first increase difference and the second increase difference are judged to determine whether there is a sudden change in the uploaded data of the first message queue cluster; for example, if the first increase difference and the second increase difference are both within or any one of the values ​​is within the corresponding preset threshold range, then there is no sudden change in the uploaded data of the first message queue cluster; otherwise, there is a sudden change in the uploaded data of the first message queue cluster.

[0119] In one example, step 2 includes the following steps:

[0120] The first step is to determine the difference between the uploaded data of the first message queue cluster and the adjacent uploaded data, which is the third increase difference; and determine the absolute value of the difference between the third data with the latest processing time slice in each third data and the adjacent uploaded data, which is the fourth increase difference; wherein the processing time slice of the adjacent uploaded data is the previous processing time slice of the processing time slice of the uploaded data of the first message queue cluster.

[0121] The second step is to determine the absolute value of the difference between every two adjacent third data in each processing time slice in each third data, which is at least one fifth amplification difference; and determine the average value between each fifth amplification difference and the fourth amplification difference, which is the amplification average value.

[0122] The third step is to determine the second comparison result according to the third increase difference and the increase average value.

[0123] For example, in combination Fig.10For each first message queue cluster, the historical data comparison layer first determines the adjacent uploaded data of the uploaded data of the first message queue cluster, that is, the processing time slice of the adjacent uploaded data is the previous processing time slice of the processing time slice of the uploaded data of the first message queue cluster. Then, the historical data comparison layer calculates the absolute value of the difference between the uploaded data of the first message queue cluster and the adjacent uploaded data to obtain the third increase difference; at the same time, the third data with the latest processing time slice is determined from all the third data, and the absolute value of the difference between the third data and the adjacent uploaded data is calculated to obtain the fourth increase difference. For all the third data, the historical data comparison layer calculates the absolute value of the difference between every two adjacent third data in the processing time slices to obtain at least one corresponding fifth increase difference. Based on the historical data comparison layer, all the fifth increase differences and the fourth increase differences calculated are averaged to obtain a corresponding average value, that is, the increase average value. The historical data comparison layer judges the average increase value and the third increase difference value according to the preset threshold rule, and then obtains whether there is any abnormality in the continuity of the data sent by the first message queue cluster; for example, if the third increase difference value and the average increase value are both within or any one of them is within the corresponding preset threshold range, then there is no abnormality in the continuity of the data sent by the first message queue cluster, otherwise, there is an abnormality in the continuity of the data sent by the first message queue cluster; or, if the absolute value of the difference between the third increase difference value and the average increase value is within the corresponding preset threshold range, then there is no abnormality in the continuity of the data sent by the first message queue cluster, otherwise, there is an abnormality in the continuity of the data sent by the first message queue cluster.

[0124] For example, Fig.11 A schematic diagram of an example of historical data comparison provided in an embodiment of the present application, such as Fig.11 As shown, the processing time slice is referred to as the time slice for short. The absolute values ​​of the differences in the amplitudes between time slice 2 and time slice 1, time slice 3 and time slice 2, time slice 4 and time slice 3, time slice 5 and time slice 4, time slice 6 and time slice 5, and time slice 7 and time slice 6 are calculated, and the average value of the absolute values ​​of these differences is taken. The average value is combined with the absolute value of the difference in the amplitudes between time slice 7 and time slice 8 to determine whether time slice 8 is abnormal.

[0125] In one example, step three includes the following steps: if it is determined that each first comparison result indicates that there is a sudden change in the uploaded data of the first message queue cluster, and it is determined that each second comparison result indicates that the continuity of the uploaded data of the first message queue cluster is abnormal, then it is determined that the processing result indicates that the uploaded data of each first message queue cluster does not meet the preset conditions.

[0126] For example, in combination Fig.10After the historical data comparison layer obtains whether there is a sudden change in the data uploaded by the two first message queue clusters and whether the continuity is abnormal, it analyzes based on the preset rules. If it is determined that there is a sudden change in the data uploaded by the two first message queue clusters and the continuity of the data uploaded by the two first message queue clusters is abnormal, it means that the data uploaded by the two first message queue clusters is abnormal. At this time, based on the abnormal center switching execution layer, according to the judgment result of the historical data comparison layer, if the data summary of the two clusters does not meet the preset rules, the system will execute the center switching operation, combined with Figure 3 , switch to the data upload module of the second cluster center, and upload the uploaded data of the second message queue cluster to ensure the normal upload of data. Furthermore, the data upload module obtains data from two independent active-active message queue clusters at the same time. For multi-source data, the module will record and track the historical upload status. When the data upload module collects current data, it compares and analyzes the newly collected data with the historical records. Through comparison and judgment, the system can identify abnormal upload in advance and take corresponding preventive measures, such as switching centers, to ensure the continuity and accuracy of the uploaded data.

[0127] After step 302, step 303 or step 304 may be executed.

[0128] 303. If it is determined that the processing result indicates that the data sent by each first message queue cluster does not meet the preset condition, the data sent by the second message queue cluster is sent to the security situation awareness platform.

[0129] Illustratively, after step 302, this step may refer to step 203 and will not be described in detail here.

[0130] 304. If it is determined that the processing result indicates that the data sent by each first message queue cluster meets a preset condition, determine target data sent from the data sent by each first message queue cluster.

[0131] Exemplarily, after step 302, in combination with Fig.10 , based on the historical data comparison and analysis layer of the data uploading module, if it is determined that the uploading data of the two first message queue clusters meet the preset conditions, such as the data are normal, the uploading data of the two first message queue clusters are forwarded to the cluster comparison and analysis layer. Based on the cluster comparison and analysis layer, the uploading data of the two first message queue clusters are judged and analyzed to determine the target uploading data to be finally sent for processing.

[0132] In one example, determining the target uploaded data from the uploaded data of each first message queue cluster in step 304 includes the following steps:

[0133] The first step is to compare and process the uploaded data of each first message queue cluster based on the running node if it is determined that the running node of the first cluster center is normal.

[0134] In the second step, if it is determined that the corresponding values ​​of the sent data of each first message queue cluster are consistent, the sent data of any first message queue cluster is determined as the target sent data.

[0135] In the third step, if it is determined that the corresponding values ​​of the sent data of each first message queue cluster are inconsistent, determine the sent data with a larger corresponding value among the sent data of each first message queue cluster as the target sent data.

[0136] Exemplarily, based on the first cluster center, it is necessary to determine whether the running node currently responsible for data upload is normal. If the running node is normal, then based on the running node, the uploaded data of the two first message queue clusters are compared, that is, the values ​​corresponding to the uploaded data of the two first message queue clusters are compared to see if they are consistent. If it is determined that the values ​​corresponding to the uploaded data of the two first message queue clusters are consistent, then the uploaded data of any one of the first message queue clusters is determined to be the target uploaded data. If it is determined that the values ​​corresponding to the uploaded data of the two first message queue clusters are inconsistent, then the uploaded data with the larger corresponding value in the uploaded data of the two first message queue clusters is determined to be the target uploaded data. Based on the data upload module, the data from these sources will be integrated, processed and analyzed. By accepting multiple data sources, it not only helps to improve the accuracy and quality of the uploaded data, but also improves the operational flexibility and processing efficiency of the data upload module.

[0137] 305. Based on the first cluster center, the target upload data is uploaded to the security situation awareness platform.

[0138] For example, in combination Fig.10 , based on the data uploading module of the first cluster center, the uploading layer obtains the target uploading data, and according to the data uploading frequency specified by the security situation awareness platform, the data uploading module continuously pushes data to the platform. At the same time, the uploading layer will save the uploaded data into the database for the historical data comparison layer to use in subsequent comparison and judgment.

[0139] For example, Fig.12 A schematic diagram of a dual-active dual-center data uploading deployment structure provided in an embodiment of the present application, combined with Fig.10 , 12As shown, the dual-active dual-center data uploading deployment structure includes cluster A, cluster B, and cluster C. The same consumption data is processed by the conversion module, the pre-statistical module, and the summary module. The values ​​of transaction volume, success rate, and latency in the 1-minute granularity corresponding to the uploading data are consistent, which means that the two first message queue clusters of cluster A and cluster B have not failed. Then, only one copy of the indicator data of the same time granularity needs to be uploaded to the security situation awareness platform. In order to ensure the real-time and accurate data uploading, these modules decide the selection of data according to the timestamp corresponding to the consumption data during the processing process: if the current time slice and the timestamp of the data exceed 10 seconds, data loss will occur. When the message queue cluster fails, it affects the production and consumption of the message queue cluster by each module. The data is lost due to the abnormality of the message queue cluster, which leads to the calculation of each indicator of the summary result being low or zero. When the values ​​of transaction volume, success rate, and latency in the uploaded data corresponding to the calculated 1-minute granularity are inconsistent, in this case, the data upload module can compare the output results of various indicators of the two message queue clusters in the same time slice. The data results of the faulty cluster are usually smaller. At this time, the larger result value is taken to send data to the upload layer. When both clusters A and cluster B in the first cluster center fail, in order to ensure the normal data upload, a multi-center deployment method is adopted, and the data upload module C uploads the uploaded data of cluster C. In this way, when any center completely fails and causes abnormal data upload, the other center can still continue to upload data, thereby ensuring real-time and continuous data upload.

[0140] 306. If it is determined that an abnormality exists in the running node and an abnormality exists in the cold standby node, the uploaded data of the second message queue cluster is uploaded to the security situation awareness platform.

[0141] Exemplarily, after step 301, Fig.12 Based on the self-check mechanism, if there are abnormalities in the running nodes and corresponding cold standby nodes in the first cluster center that are sending data, a center switching operation is performed. Based on the data sending module of the second cluster center, the sent data of the second message queue cluster is sent to the security situation awareness platform to ensure the normal sending of data.

[0142] In this embodiment, based on the above embodiment, on the one hand, multiple message queue clusters simultaneously send data to the data upload module, which will integrate, process and analyze the data from these sources. In this way, the operational flexibility and processing efficiency of the data upload module are improved; on the other hand, through intelligent analysis of multi-source data, combined with customizable threshold rules, the accuracy of cluster data can be identified in a timely manner, and the backup plan can be switched to as soon as possible, thereby ensuring the continuity, accuracy and real-time nature of data upload.

[0143] Fig.13A schematic diagram of the structure of a data uploading device provided in an embodiment of the present application is shown in FIG. Fig.13 As shown, the apparatus is applied to a computing device, the computing device deploys a first cluster center and a second cluster center; the first cluster center includes two first message queue clusters; the second cluster center includes a second message queue cluster; the apparatus includes:

[0144] The first processing module 401 is used to process the current consumption data to be processed based on the first message queue cluster to obtain the uploading data of the first message queue cluster; at the same time, the consumption data is processed based on the second message queue cluster to obtain the uploading data of the second message queue cluster; wherein the uploading data represents the operation security situation of the business organization to which the consumption data belongs;

[0145] The second processing module 402 is used to process the sent data of each first message queue cluster to obtain the corresponding processing results of the sent data of each first message queue cluster;

[0146] The sending module 403 is used to send the sent data of the second message queue cluster to the security situation awareness platform if it is determined that the processing result indicates that the sent data of each first message queue cluster does not meet the preset conditions.

[0147] In one possible implementation, the first cluster center includes a running node and a cold standby node; the second processing module 402 is specifically used to: if it is determined that there is an abnormality in the running node, then based on the cold standby node, process the uploaded data of each first message queue cluster to obtain the corresponding processing results of the uploaded data of each first message queue cluster.

[0148] In one possible implementation, the second processing module 402 is specifically used to: obtain historical uploaded data based on the processing time slice of the uploaded data of each first message queue cluster; wherein the processing time slice of the uploaded data of each first message queue cluster is the same; the historical uploaded data is the uploaded data that has been uploaded to the security situation awareness platform within a historical time period; determine the processing result based on the uploaded data of each first message queue cluster and the historical uploaded data.

[0149] In one possible implementation, the second processing module 402 is specifically used to: compare the uploaded data of the first message queue cluster with the first data and the second data in the historical uploaded data to determine the first comparison result of the first message queue cluster; wherein the first data is the uploaded data corresponding to the processing time slice within the previous day; the second data is the uploaded data corresponding to the processing time slice one week ago; the first comparison result represents the sudden change of the uploaded data of the first message queue cluster; compare the uploaded data of the first message queue cluster with at least one third data in the historical uploaded data to determine the second comparison result of the first message queue cluster; wherein the third data is the uploaded data corresponding to each processing time slice in the previous time period within the time period where the processing time slice is located within the day; the second comparison result represents the continuity of the uploaded data of the first message queue cluster; and determine the processing result according to each first comparison result and each second comparison result.

[0150] In one possible implementation, the second processing module 402 is specifically used to: determine the absolute value of the difference between the uploaded data of the first message queue cluster and the first data, which is the first amplification difference; determine the absolute value of the difference between the uploaded data of the first message queue cluster and the second data, which is the second amplification difference; determine the first comparison result based on the first amplification difference and the second amplification difference.

[0151] In a possible implementation, the second processing module 402 is further specifically used to: determine the difference between the uploaded data of the first message queue cluster and the adjacent uploaded data, which is the third increase difference; and determine the absolute value of the difference between the third data with the latest processing time slice in each third data and the adjacent uploaded data, which is the fourth increase difference; wherein the processing time slice of the adjacent uploaded data is the previous processing time slice of the processing time slice of the uploaded data of the first message queue cluster; determine the absolute value of the difference between every two adjacent third data in each third data processing time slice, which is at least one fifth increase difference; and determine the average value between each fifth increase difference and the fourth increase difference, which is the increase average value; determine the second comparison result based on the third increase difference and the increase average value.

[0152] In a possible implementation, the second processing module 402 is further specifically used for: if it is determined that each first comparison result indicates that there is a sudden change in the uploaded data of the first message queue cluster, and it is determined that each second comparison result indicates that the continuity of the uploaded data of the first message queue cluster is abnormal, then it is determined that the processing result indicates that the uploaded data of each first message queue cluster does not meet the preset conditions.

[0153] In a possible implementation, the second processing module 402 is further specifically used to: if it is determined that an abnormality exists in the running node and an abnormality exists in the cold standby node, send the uploaded data of the second message queue cluster to the security situation awareness platform.

[0154] In a possible implementation, the device is also used to: if it is determined that the processing result indicates that the uploaded data of each first message queue cluster meets a preset condition, then determine the target uploaded data from the uploaded data of each first message queue cluster; based on the first cluster center, upload the target uploaded data to the security situation awareness platform.

[0155] In a possible implementation, the device is also specifically used for: if it is determined that the running node of the first cluster center is normal, then based on the running node, comparing and processing the uploaded data of each first message queue cluster; if it is determined that the corresponding values ​​of the uploaded data of each first message queue cluster are consistent, then determining that the uploaded data of any first message queue cluster is the target uploaded data; if it is determined that the corresponding values ​​of the uploaded data of each first message queue cluster are inconsistent, then determining that the uploaded data with a larger corresponding value among the uploaded data of each first message queue cluster is the target uploaded data.

[0156] In a possible implementation manner, the uploaded data includes, but is not limited to: transaction volume, transaction success rate, and latency.

[0157] The device of this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same and will not be repeated here.

[0158] Fig.14 A schematic diagram of a computing device provided in an embodiment of the present application is shown in FIG. Fig.14 As shown, the computing device includes: a memory 501 and a processor 502; the memory 501 is a memory for storing instructions executable by the processor 502.

[0159] The processor 502 is configured to execute the method provided in the above embodiment.

[0160] The computing device 500 further includes a receiver 503 and a transmitter 504. The receiver 503 is used to receive instructions and data sent by other devices, and the transmitter 504 is used to send instructions and data to external devices.

[0161] The specific implementation process of the processor can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0162] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0163] The memory may include a high-speed memory (Random Access Memory, referred to as RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0164] An embodiment of the present application also provides a chip for executing instructions, which is used to execute the technical solution of the processing method in the above embodiment.

[0165] The embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed on a computer, the computer executes the technical solution of the processing method of the above embodiment.

[0166] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0167] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in a dedicated integrated circuit. Of course, the processor and the readable storage medium can also exist in a device as discrete components.

[0168] An embodiment of the present application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When at least one processor executes the computer program, the technical solution of the processing method in the above embodiment can be implemented.

[0169] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0170] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0171] Those skilled in the art will appreciate that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed.

[0172] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A data uploading method, characterized in that: The method is applied to a computing device, wherein the computing device deploys a first cluster center and a second cluster center; the first cluster center includes two first message queue clusters; The second cluster center includes a second message queue cluster; the method includes: Based on the first message queue cluster, the consumption data to be processed is processed to obtain the uploading data of the first message queue cluster; at the same time, the consumption data is processed based on the second message queue cluster to obtain the uploading data of the second message queue cluster; wherein the uploading data represents the operation security situation of the business organization to which the consumption data belongs; Processing the data sent by each of the first message queue clusters to obtain corresponding processing results of the data sent by each of the first message queue clusters; If it is determined that the processing result indicates that the data sent by each of the first message queue clusters does not meet the preset conditions, the data sent by the second message queue cluster is sent to the security situation awareness platform.

2. The method according to claim 1, characterized in that The first cluster center includes a running node and a cold standby node; processing the sent data of each of the first message queue clusters to obtain corresponding processing results of the sent data of each of the first message queue clusters, including: If it is determined that the running node is abnormal, the sent data of each first message queue cluster is processed based on the cold standby node to obtain corresponding processing results of the sent data of each first message queue cluster.

3. The method according to claim 2, characterized in that Based on the cold standby node, the sent data of each of the first message queue clusters is processed to obtain corresponding processing results of the sent data of each of the first message queue clusters, including: According to the processing time slice of the uploaded data of each of the first message queue clusters, the historical uploaded data is obtained; wherein the processing time slice of the uploaded data of each of the first message queue clusters is the same; the historical uploaded data is the uploaded data that has been uploaded to the security situation awareness platform within the historical time period; The processing result is determined according to the uploaded data of each of the first message queue clusters and the historical uploaded data.

4. The method according to claim 3, characterized in that Determining the processing result according to the uploaded data of each of the first message queue clusters and the historical uploaded data includes: Compare and process the uploaded data of the first message queue cluster with the first data and the second data in the historical uploaded data to determine a first comparison result of the first message queue cluster; wherein the first data is the uploaded data corresponding to the processing time slice within the previous day; the second data is the uploaded data corresponding to the processing time slice one week ago; the first comparison result represents the sudden change of the uploaded data of the first message queue cluster; Compare and process the uploaded data of the first message queue cluster and at least one third data in the historical uploaded data to determine a second comparison result of the first message queue cluster; wherein the third data is the uploaded data corresponding to each processing time slice in the previous time period within the time period where the processing time slice is located within the day; the second comparison result represents the continuity of the uploaded data of the first message queue cluster; The processing result is determined according to each of the first comparison results and each of the second comparison results.

5. The method according to claim 4, characterized in that Comparing the uploaded data of the first message queue cluster with the first data and the second data in the historical uploaded data to determine a first comparison result of the first message queue cluster, including: Determine an absolute value of a difference between the data sent by the first message queue cluster and the first data as a first increase difference; Determine an absolute value of a difference between the data sent by the first message queue cluster and the second data as a second increase difference; The first comparison result is determined according to the first amplification difference and the second amplification difference.

6. The method according to claim 4, characterized in that Comparing the uploaded data of the first message queue cluster with at least one third data in the historical uploaded data to determine a second comparison result of the first message queue cluster, including: Determine the difference between the uploaded data of the first message queue cluster and the adjacent uploaded data as the third increase difference; and determine the absolute value of the difference between the third data with the latest processing time slice in each of the third data and the adjacent uploaded data as the fourth increase difference; wherein the processing time slice of the adjacent uploaded data is the previous processing time slice of the processing time slice of the uploaded data of the first message queue cluster; Determine the absolute value of the difference between every two adjacent third data in each processing time slice in each of the third data as at least one fifth increase difference; and determine the average value between each of the fifth increase difference and the fourth increase difference as the increase average value; The second comparison result is determined according to the third increase difference and the increase average value.

7. The method according to claim 4, characterized in that Determining the processing result according to each of the first comparison results and each of the second comparison results includes: If it is determined that each of the first comparison results indicates that there is a sudden change in the data sent by the first message queue cluster, and it is determined that each of the second comparison results indicates that the continuity of the data sent by the first message queue cluster is abnormal, then it is determined that the processing result indicates that the data sent by each of the first message queue clusters does not meet the preset conditions.

8. The method according to claim 2, characterized in that: The method further comprises: If it is determined that an abnormality exists in the running node and an abnormality exists in the cold standby node, the uploaded data of the second message queue cluster is uploaded to the security situation awareness platform.

9. The method according to claim 1, characterized in that: The method further comprises: If it is determined that the processing result indicates that the data sent by each of the first message queue clusters meets a preset condition, determining target sent data from the data sent by each of the first message queue clusters; Based on the first cluster center, the target uploaded data is uploaded to the security situation awareness platform.

10. The method according to claim 9, characterized in that Determining target uploaded data from uploaded data of each of the first message queue clusters includes: If it is determined that the running node of the first cluster center is normal, then based on the running node, a comparison process is performed on the sent data of each of the first message queue clusters; If it is determined that the corresponding values ​​of the uploaded data of each of the first message queue clusters are consistent, determining the uploaded data of any of the first message queue clusters as the target uploaded data; If it is determined that the corresponding values ​​of the sent data of each of the first message queue clusters are inconsistent, the sent data with a larger corresponding value among the sent data of each of the first message queue clusters is determined as the target sent data.

11. The method according to any one of claims 1 to 10, characterized in that The uploaded data includes but is not limited to: transaction volume, transaction success rate and latency.

12. A data uploading device, characterized in that: The apparatus is applied to a computing device, and the computing device deploys a first cluster center and a second cluster center; the first cluster center includes two first message queue clusters; The second cluster center includes a second message queue cluster; the device includes: A first processing module is used to process the current consumption data to be processed based on the first message queue cluster to obtain the uploading data of the first message queue cluster; at the same time, the consumption data is processed based on the second message queue cluster to obtain the uploading data of the second message queue cluster; wherein the uploading data represents the operation security situation of the business organization to which the consumption data belongs; A second processing module is used to process the sent data of each of the first message queue clusters to obtain corresponding processing results of the sent data of each of the first message queue clusters; The sending module is used to send the sent data of the second message queue cluster to the security situation awareness platform if it is determined that the processing result indicates that the sent data of each first message queue cluster does not meet the preset conditions.

13. A computing device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 11 when executed by a processor.

15. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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