Data pushing method, computer equipment, readable storage medium and program product

Abstract

The invention discloses a data pushing method, computer equipment, a readable storage medium and a program product, and relates to the technical field of data processing, and the data pushing method comprises the following steps: obtaining to-be-pushed data; predicting a predicted load of the target monitoring system; and in response to the fact that the predicted load reaches a load threshold value, carrying out dimension reduction processing on the to-be-pushed data and then pushing the to-be-pushed data to the target monitoring system. The technical problem that global congestion is easily caused by instability of the target monitoring system is solved, and the technical effect of reducing the burden of the target monitoring system to facilitate improvement of data pushing reliability is achieved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data push method, computer device, computer-readable storage medium, and computer program product. Background Technology

[0002] In application scenarios involving real-time data push, such as ride-hailing, logistics monitoring, and e-commerce monitoring, the upstream data volume of the target monitoring system is voluminous. During peak data push periods, the heavy monitoring burden can easily lead to a decline in the performance of the target monitoring system. There may be situations where the pushed data cannot be processed in a timely manner, or even a global delay. That is, if the data volume in area A is too large, it will cause the target monitoring system to degrade in performance and even affect the data push in area B. Summary of the Invention

[0003] This application provides a data push method, a computer device, a computer-readable storage medium, and a computer program product to at least solve the problem in the related art that the instability of the target monitoring system can easily lead to global blocking.

[0004] This application provides a data push method, which includes: acquiring data to be pushed; predicting the predicted load of the target monitoring system; and in response to the predicted load reaching a load threshold, performing dimensionality reduction processing on the data to be pushed and then pushing it to the target monitoring system.

[0005] In one embodiment of this application, predicting the load of a target monitoring system includes: acquiring operational data of the target monitoring system; using the operational data to predict the system quality of the target monitoring system; and using the predicted system quality as the predicted load.

[0006] In one embodiment of this application, obtaining the operational data of the target monitoring system includes: obtaining monitoring data of the target monitoring system within an adjacent forward first period as first data; wherein the monitoring data includes at least one of data push success rate and data reception delay; obtaining monitoring data of the target monitoring system within a historical second period as second data; wherein the time length of the second period is greater than the time length of the first period; and using the first data and the second data as operational data.

[0007] In one embodiment of this application, predicting the system quality of a target monitoring system using operational data includes: inputting operational data and a matching process with system quality levels into a prediction model; wherein the number of system quality levels is at least two; the prediction model is constructed based on a model context protocol; and obtaining the system quality level predicted by the prediction model as the system quality.

[0008] In one embodiment of this application, pushing data to the target monitoring system after dimensionality reduction processing includes: performing dimensionality reduction processing on the data tags that match the data to be pushed with the target monitoring system; and / or reducing the number of servers that generate the data to be pushed; and / or adjusting the routing path for pushing the data to be pushed to the target monitoring system.

[0009] In one embodiment of this application, the dimensionality reduction processing of the data tag to be pushed and the data tag to be matched with the target monitoring system includes: obtaining the current data tag of the data to be pushed; querying the parent tag to which the current data tag belongs as the target data tag; and modifying the current data tag of the data to be pushed to the target data tag.

[0010] In one embodiment of this application, after predicting the predicted load of the target monitoring system, the method further includes: in response to the predicted load not reaching the load threshold, pushing the data to be pushed to the target monitoring system.

[0011] This application also provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program to implement the steps of any of the above-described data push methods.

[0012] This application also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of any of the above-described data push methods.

[0013] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above-described data push methods.

[0014] This application enables the prediction of the target monitoring system's load and real-time processing performance. In response to the predicted load reaching a load threshold, the data to be pushed is dimensionality-reduced to decrease its volume. Pushing this dimensionality-reduced data to the target monitoring system alleviates overload and reduces the risk of the data exceeding its processing capacity, thus mitigating instability caused by data accumulation. Therefore, it solves the problem of global blocking due to target monitoring system instability, effectively reducing the system's burden and improving data push reliability. Attached Figure Description

[0015] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram illustrating an application scenario of one embodiment of the data push method of this application; Figure 2 This is a flowchart illustrating an embodiment of the data push method of this application; Figure 3 This is a schematic diagram of the structure of an embodiment of the data push system of this application; Figure 4 This is a flowchart illustrating another embodiment of the data push method of this application; Figure 5 This is a flowchart illustrating one embodiment of the load prediction method in this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0018] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0019] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] The specific application environment architecture or specific hardware architecture on which the data push method depends is described here.

[0021] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario of one embodiment of the data push method of this application.

[0022] In one embodiment, the application scenario of the data push method may typically include a data push system 10, a target monitoring system 20, and other systems 30.

[0023] The data push system 10 and other systems 30 are all connected to the target monitoring system 20. The other systems 30 are other systems that push data to the target monitoring system 20.

[0024] The data push system 10 connects to the server and pushes the data to be pushed sent by the server to the target monitoring system 20 using a data push method. The data push method may include at least: acquiring the data to be pushed; predicting the predicted load of the target monitoring system 20; and, in response to the predicted load reaching a load threshold, performing dimensionality reduction processing on the data to be pushed before pushing it to the target monitoring system 20.

[0025] As its name suggests, the target monitoring system 20 is used to monitor and analyze the data pushed by the data push system 10 and other systems 30. The specific structure and detailed working principle of the data push system 10 will be described in detail later.

[0026] Taking ride-hailing monitoring as an example, the following will illustrate the detailed principles of how ride-hailing operators process business data and push it out.

[0027] Ride-hailing platforms generate single data entries in formats such as JSON based on order and / or location information. This data is then pushed to message queues such as Apache RocketMQ (an open-source message queue). The data is matched against target region servers using topic, groupid, and tag. Subsequently, Nginx (a proxy server) acts as a forwarding agent, pushing the matched data to servers of relevant regulatory departments across the country. RocketMQ supports high-throughput asynchronous message processing, while Nginx provides load balancing and routing capabilities, supporting static rule configuration. Typically, data push systems require a large number of servers, such as 100, to subscribe to RocketMQ to handle the consumer load.

[0028] The data push process includes: generating data "{"order_id":123, "region":"Beijing","time":"2025-09-01 09:00:00"}". The data is then pushed to RocketMQ using a push matching mechanism, with the specific format "topic="regulatory_data", groupid="enforce_group", tag="beijing"". Nginx forwards the data to the corresponding regulatory department's server based on the matching results.

[0029] Currently, there are problems such as long processing time for a single piece of data, global delays caused by business backlog, high server resource requirements, and insufficient scalability.

[0030] In detail, during peak ride-hailing hours such as 9 AM, there is a risk of unstable server performance for regulatory authorities, which can easily lead to long processing times after matching. For example, forwarding a single data item might take more than 5 seconds or even time out, resulting in low Nginx forwarding efficiency. Delays in processing individual data items cause RocketMQ queue backlogs, preventing consumers from consuming subsequent data in a timely manner and potentially causing global delays. For instance, a performance drop in server A during the 9 AM peak could block data pushes from region B. To cope with peak loads, a large number of servers need to subscribe to RocketMQ, increasing hardware and maintenance costs and making it difficult to optimize server resources or integrate AI (Artificial Intelligence) optimization.

[0031] To address the aforementioned technical problems, this application provides a data push method that can reduce the burden on the target monitoring system, alleviate the performance deficiencies of the target monitoring system, thereby improving the reliability of data push and the timeliness of data response, and ultimately enhancing data push performance.

[0032] The embodiments of this application provide a data push method, and the data push method is described in detail in conjunction with the execution flow of the data push method.

[0033] Please see Figure 2 , Figure 2 This is a flowchart illustrating an embodiment of the data push method of this application.

[0034] S101: Get the data to be pushed.

[0035] In this embodiment, data to be pushed is obtained, which refers to data to be pushed to the target monitoring system.

[0036] S102: Predictive load of the target monitoring system.

[0037] In this embodiment, the predicted load of the target monitoring system can be predicted to roughly understand its current operating status and processing capacity. Considering that the target monitoring system's processing capacity has a fixed upper limit, this embodiment can predict its predicted load to anticipate potential overload risks, providing a crucial basis for determining whether subsequent data dimensionality reduction processing is necessary. This reduces the likelihood of blindly pushing data to the target monitoring system, which could lead to system malfunctions, and facilitates adjustments to the data push strategy to improve data push reliability.

[0038] S103: In response to the predicted load reaching the load threshold, the data to be pushed is processed by dimensionality reduction and then pushed to the target monitoring system.

[0039] In this embodiment, when the predicted load reaches the load threshold, the current processing capacity of the target monitoring system is about to be unable to handle the transmission and processing pressure of the original data to be pushed. Therefore, the data to be pushed can be dimensionality reduced before being pushed to the target monitoring system. Dimensionality reduction can reduce the data volume of the data to be pushed, thereby reducing the monitoring burden on the target monitoring system. This can help match the transmission pressure and processing difficulty of the processed data to be pushed with the current processing capacity of the target monitoring system, alleviate the situation where the amount of data to be pushed exceeds the processing limit of the target monitoring system, and reduce the occurrence of global blocking phenomena. This can help reduce the operating burden of the target monitoring system, ensure the smoothness of the data transmission link, and thus improve the reliability and stability of data push.

[0040] In other words, this embodiment can predict the predicted load of the target monitoring system and predict the real-time processing performance of the target monitoring system. In response to the predicted load reaching the load threshold, the data to be pushed is dimensionality reduced to reduce the data volume. If the dimensionality-reduced data to be pushed is pushed to the target monitoring system, the overload of the target monitoring system can be alleviated, thereby reducing the risk that the amount of data to be pushed exceeds the processing capacity limit of the target monitoring system. This reduces the instability of the target monitoring system due to data accumulation, thereby reducing the burden on the target monitoring system and improving the reliability of data push.

[0041] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0042] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an embodiment of the data push system of this application.

[0043] In one embodiment, the data push system may include a data push module 11, a model interface module 12, a prediction model 13, a feedback module 14, a proxy server 15, and a forwarding execution module 16.

[0044] The data push module 11 can generate data to be pushed and use keywords such as topic, groupid, and tag to match and adapt to the target monitoring system, and push the data to be pushed to the target monitoring system.

[0045] The model interface module 12 can use model context protocols such as MCP (Message Context Protocol) as a standardized bridge between the prediction model 13 and external custom applications such as the matching process, to inject context information of the target monitoring system server quality prediction in real time, such as the success rate / latency and historical data in the first half hour.

[0046] Predictive Model 13: It can use machine learning algorithms to integrate the context provided by MCP to predict the server quality (e.g., high / medium / low) of the target monitoring system, detect peak-period mutations and generate dynamic matching rules to optimize consumer load distribution.

[0047] The forwarding execution module 16 can perform pre-optimized forwarding based on the proxy server 15, and supports changing the path, adjusting the priority, or buffering.

[0048] Feedback module 14 can record the push and forwarding results of the data to be pushed, update the historical and near real-time database of the custom application, and form a closed-loop learning.

[0049] For a description of the features in the embodiments corresponding to the data push system, please refer to the relevant descriptions of the embodiments corresponding to the data push method in the preceding and following texts, which will not be repeated here.

[0050] As can be seen, MCP is used for integrating AI models with general tools. In this embodiment, it is applied to the real-time prediction scenario of data to be pushed by the target monitoring system, calling a custom application (such as a self-developed server quality prediction tool, i.e., a prediction model). This application can combine near-real-time information such as the first half hour (e.g., a sudden drop in success rate between 9:00 and 9:30) and historical data (success rate in the previous few days / weeks) to predict the quality of the target detection system and inject a matching process into the prediction model. Thus, push resources can be optimized through real-time prediction. For example, if server quality changes suddenly during peak detection periods, tag matching or reroute (path change) data can be adjusted to reduce the risk of global backlog caused by a single delay. Furthermore, by optimizing consumer load distribution, the number of servers subscribed to the target monitoring system can be reduced, for example, from 100 to 80, saving 20% ​​of resources. Moreover, MCP's encryption and authentication mechanisms ensure reliable transmission of predictive data and business data. As a result, the processing time for a single piece of data to be pushed can be shortened by approximately 25-45%, the backlog rate can be reduced by 55%, the global latency rate during peak periods can be less than 4%, and the number of servers subscribed to the target monitoring system can be reduced by approximately 20%. The following provides detailed examples illustrating the principles behind the relevant data push methods.

[0051] Please see Figure 4 , Figure 4 This is a flowchart illustrating another embodiment of the data push method of this application.

[0052] S201: Get the data to be pushed.

[0053] In this embodiment, the data to be pushed can be considered as business data that is planned to be transmitted to the target monitoring system.

[0054] S202: Obtain operational data from the target monitoring system.

[0055] In this embodiment, the operational data of the target monitoring system can be obtained. It can be assumed that the operational data can reflect the historical and recent operational status of the target monitoring system in a relatively comprehensive and objective manner, providing a reliable data basis for subsequent prediction of the target monitoring system's load and avoiding distortion of the predicted load results due to insufficient data support.

[0056] The monitoring data of the target monitoring system within the adjacent forward first period is acquired as the first data. This monitoring data includes at least one of the following: data push success rate and data reception delay. The first period is a short time interval immediately adjacent to the current moment. The first data can accurately capture the recent operating status of the target monitoring system. For example, the data push success rate directly reflects the smoothness of data reception by the system, and the data reception delay reflects the real-time efficiency of data processing. The introduction of short-term data helps improve the timeliness of predictive load.

[0057] The monitoring data from the second historical period of the target monitoring system is acquired as the second data. The second period is longer than the first period. The second period, covering multiple historical phases, is a long-term time interval. This second data reflects the long-term operational trend and stable state of the target monitoring system, avoiding biased judgments about the system's state due to reliance on short-term data, and thus improving the comprehensiveness of the operational data.

[0058] As such, the first and second data, as operational data, can take into account both the real-time status and long-term trends of the target monitoring system by combining short-term and long-term data. This makes the operational data more complete in dimensions and richer in information, which is conducive to providing more comprehensive and accurate data support for subsequent load prediction.

[0059] S203: Utilize operational data to predict the predicted load of the target monitoring system.

[0060] In this embodiment, the system quality of the target monitoring system is predicted using operational data. The predicted system quality is then used as the predicted load. It can be assumed that the system quality is relatively directly related to the core indicators of the target monitoring system, such as processing capacity and operational stability. Using system quality to characterize the predicted load can relatively objectively reflect the upper limit of the target monitoring system's ability to carry data push, reducing the risk of ambiguity in the judgment of load status.

[0061] For example, operational data and the matching process with system quality levels can be input into the prediction model. There should be at least two system quality levels to allow for different processing capacity levels of the target monitoring system. This level division clearly defines the high and low ranges of the predicted load, making the prediction results more predictable. The prediction model is built based on a model context protocol. Obtaining the system quality level predicted by the model as the system quality transforms complex operational data into a clear quality level result, facilitating subsequent judgment of whether the predicted load has reached the load threshold, and improving the practicality and operability of the prediction results.

[0062] S204: Determine whether the predicted load has reached the load threshold.

[0063] In this embodiment, when the predicted load reaches the load threshold, step S205 is executed. When the predicted load does not reach the load threshold, step S206 is executed.

[0064] S205: After performing dimensionality reduction processing on the data to be pushed, it is pushed to the target monitoring system.

[0065] In this embodiment, in response to the predicted load reaching the load threshold, it can be considered that the current processing capacity of the target monitoring system is close to or has reached its limit. If the original data to be pushed is directly pushed, it may lead to system instability. Therefore, in this embodiment, the data to be pushed is dimensionality reduced before being pushed to the target monitoring system. This can effectively reduce the transmission volume of the data to be pushed, simplify the data structure, or optimize the transmission path, so that the processed data to be pushed matches the current processing capacity of the target monitoring system. This reduces problems such as data accumulation and transmission interruption caused by system overload, which helps to reduce the operating burden of the target monitoring system and ensures smooth data transmission.

[0066] For example, the data tags used to match the data to be pushed with the target monitoring system can be dimensionality-reduced. This reduces redundancy in the labels of the data to be pushed, simplifies the data parsing process in the target monitoring system, and reduces the complexity of data processing. And / or, reducing the number of servers generating the data to be pushed directly reduces the overall volume of data to be pushed, reducing the load on the target monitoring system from the source. And / or, adjusting the routing path for pushing the data to be pushed to the target monitoring system optimizes the data transmission link, reduces latency and resource consumption during transmission, and indirectly reduces the load on the target monitoring system receiving data.

[0067] One method for dimensionality reduction of data tags is to obtain the current data tag of the data to be pushed. Then, the parent tag to which the current data tag belongs is queried and used as the target data tag. Finally, the current data tag of the data to be pushed is modified to the target data tag. This simplification of the tag hierarchy allows the target monitoring system to monitor the data to be pushed over a larger area, further reducing the processing load on the target monitoring system.

[0068] S206: Push the data to be pushed to the target monitoring system.

[0069] In this embodiment, in response to the predicted load not reaching the load threshold, it can be assumed that the current processing capacity of the target monitoring system is sufficient to handle the original data to be pushed. Therefore, in this embodiment, the data to be pushed can be pushed to the target monitoring system in a conventional way, that is, the data to be pushed can be pushed to the target monitoring system without additional dimensionality reduction processing. This can preserve the integrity and original information of the data to be pushed to the maximum extent, avoid data value loss or reduced push efficiency due to unnecessary processing, and ensure that the target monitoring system receives complete data under the premise of stable operation.

[0070] The following provides a detailed explanation of how load forecasting works. Please refer to [link / reference]. Figure 5 , Figure 5 This is a flowchart illustrating one embodiment of the load prediction method in this application.

[0071] In this embodiment, monitoring data from the target monitoring system can be acquired, and the monitoring data can be used as operational data. The prediction load can be predicted using the prediction model and the operational data. The prediction model can be injected with context using the model context protocol to generate dynamic rules and / or optimize the push path.

[0072] The following will explain the above content in simple terms, using specific application scenarios as examples.

[0073] When generating a single piece of data to be pushed, the data can be like {"order_id":123, "region":"Beijing", "time":"2025-09-01 09:00:00"}, pushed to RocketMQ and matched with topic / groupid / tag. Context injection is achieved using MCP; the prediction model can call a self-written application via the MCP protocol to obtain real-time first data (first half hour, 9:00-9:30) and historical second data (first few days / weeks) as context information. For example, the server success rate of the target monitoring system in region A suddenly drops from 90% to 70%. AI is integrated for prediction optimization; machine learning models such as random forests can be used to detect sudden changes during peak periods and predict whether the target monitoring system's system quality level is high, medium, or low. If the target monitoring system's system quality level is low, tags or reroute can be adjusted, and consumer load distribution can be optimized to reduce the number of servers. Updating Nginx Lua script execution rules can reduce buffer data backlog if prediction is slow. Lua is a lightweight scripting language designed for embedding in applications, providing flexible extension and customization capabilities. It records prediction results and real-time feedback data from the target monitoring system to facilitate updating the real-time and historical databases of the self-written application.

[0074] For example, during the morning peak at 9 AM, a data entry for "Beijing" is generated. MCP calls its self-written application, based on a 70% success rate in the previous half hour and a historical success rate of 90%. When the prediction model detects a sudden drop in quality, it adjusts the tag reroute to the North China node to optimize load distribution. This can achieve a single data entry processing time of less than 1.5 seconds, avoiding data backlog and reducing global latency to less than 1 minute, while reducing the number of consumer servers from 100 to 80.

[0075] The following are examples of relevant pseudocode: "import com.alibaba.rocketmq.client.producer.DefaultMQProducer; import com.alibaba.rocketmq.common.message.Message; import com.example.nginx.NginxController; import com.example.mcp.McpSdk; import com.example.ai.AIModel; import com.example.feedback.FeedbackService; public class IntelligentForwarder { private DefaultMQProducer producer; private NginxController nginxController; private McpSdk mcpSdk; private AIModel aiModel; private FeedbackService feedbackService; public IntelligentForwarder() { this.producer = new DefaultMQProducer("enforce_group"); this.nginxController = new NginxController(); this.mcpSdk = new McpSdk(); this.aiModel = new AIModel(); this.feedbackService = new FeedbackService(); } public void intelligentForward(String singleData) throws Exception { / / Push data to RocketMQ Message msg = new Message("regulatory", "beijing", singleData.getBytes()); producer.send(msg); / / MCP calls a self-written application to obtain context String context = mcpSdk.callTool("server_quality_predictor", "{\"period\": [\"last_30min\", \"last_week\"], \"region\": \"beijing\"}"); AI predicts server quality and optimizes load. Prediction prediction = aiModel.predictQualityAndOptimizeLoad(context); if ("low".equals(prediction.getQuality()) ||prediction.isPeakDropDetected()) { String rules = generateRerouteRules(prediction); / / Generate dynamic rules nginxController.update(rules); bufferIfNeeded(singleData); / / Buffer to avoid data accumulation optimizeConsumerLoad(prediction); / / Optimize consumer load and reduce server load } else { nginxController.forward(singleData); } / / Update real-time and historical databases feedbackService.updateHistoryAndRealtime(prediction.getResult()); } private String generateRerouteRules(Prediction prediction) { / / Simulate the generation of rule logic return "reroute:alternate_region"; } private void bufferIfNeeded(String data) { / / Simulated buffer logic producer.send(new Message("buffer_topic", data.getBytes())); } private void optimizeConsumerLoad(Prediction prediction) { / / Simulate load optimization to reduce consumer servers (e.g., from 100 to 80) aiModel.adjustConsumerAllocation(prediction.getLoadMetrics()); } } class Prediction { private String quality; private boolean peakDropDetected; private String result; private String loadMetrics; / / Getters and setters public String getQuality() { return quality;} public void setQuality(String quality) { this.quality = quality;} public boolean isPeakDropDetected() { return peakDropDetected;} public void setPeakDropDetected(boolean peakDropDetected) {this.peakDropDetected = peakDropDetected;} public String getResult() { return result;} public void setResult(String result) { this.result = result;} public String getLoadMetrics() { return loadMetrics;} public void setLoadMetrics(String loadMetrics) { this.loadMetrics =loadMetrics;} }".

[0076] Embodiments of this application also provide a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above-described data push method embodiments.

[0077] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above-described data push method embodiments when it is run.

[0078] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0079] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above-described data push method embodiments.

[0080] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above-described data push method embodiments.

[0081] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0082] The foregoing has provided a detailed description of a data push method, computer device, computer-readable storage medium, and computer program product provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only intended to help understand the method and core ideas of this application. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A data push method, characterized in that, The data push method includes: Get the data to be pushed; Predictive load of the target monitoring system; In response to the predicted load reaching the load threshold, the data to be pushed is processed by dimensionality reduction and then pushed to the target monitoring system.

2. The data push method according to claim 1, characterized in that, The prediction load of the prediction target monitoring system includes: Obtain the operational data of the target monitoring system; The system quality of the target monitoring system is predicted using the operational data, and the predicted system quality is used as the predicted load.

3. The data push method according to claim 2, characterized in that, The acquisition of the operational data of the target monitoring system includes: The monitoring data of the target monitoring system within the adjacent forward first cycle is obtained as the first data; wherein, the monitoring data includes at least one of data push success rate and data reception delay; The monitoring data of the target monitoring system during the second historical period is obtained as the second data; wherein the time length of the second period is greater than the time length of the first period; The first data and the second data are used as the running data.

4. The data push method according to claim 2, characterized in that, The method of using the operational data to predict the system quality of the target monitoring system includes: The operational data and the matching process with the system quality level are input into the prediction model; wherein, the number of system quality levels is at least two; the prediction model is constructed based on the model context protocol. The system quality level predicted by the prediction model is taken as the system quality.

5. The data push method according to claim 1, characterized in that, The step of performing dimensionality reduction processing on the data to be pushed and then pushing it to the target monitoring system includes: Dimensionality reduction processing is performed on the data tags of the data to be pushed and the data tags that match the target monitoring system; and / or, Reduce the number of servers that generate the data to be pushed; and / or, Adjust the routing path for pushing the data to be pushed to the target monitoring system.

6. The data push method according to claim 5, characterized in that, The dimensionality reduction processing of the data tags matching the data to be pushed with the target monitoring system includes: Obtain the current data tag of the data to be pushed; Query the parent tag to which the current data tag belongs as the target data tag; Modify the current data tag of the data to be pushed to the target data tag.

7. The data push method according to claim 1, characterized in that, Following the prediction load of the prediction target monitoring system, the following also applies: In response to the predicted load not reaching the load threshold, the data to be pushed is pushed to the target monitoring system.

8. A computer device, characterized in that, The computer device includes: Memory, used to store computer programs; A processor, configured to implement the steps of the data push method as described in any one of claims 1 to 7 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the data push method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the data push method as described in any one of claims 1 to 7.

Images