A real-time review monitoring and data acquisition storage system and method

Abstract

The present invention relates to the technical field of data acquisition and storage, and discloses a real-time review monitoring and data acquisition and storage system and method. Monitoring data is acquired through a data acquisition module, and the acquisition frequency of the monitoring data is controlled through a dynamic acquisition strategy. The data fusion module performs fusion processing on the acquired monitoring data through a data fusion strategy, and provides more comprehensive monitoring data for subsequent analysis, which is conducive to revealing the deeper state and trend of the monitored object. The monitoring analysis module performs predictive analysis on the fused monitoring data through a predictive analysis strategy to generate monitoring results. The data storage module stores the fused monitoring data and monitoring results to a cloud platform through a secure storage strategy, so as to ensure the security and privacy of the data, and at the same time ensure the persistence and reliability of the data. The interactive feedback module receives and visually displays the monitoring data and monitoring results through a feedback display strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition and storage, and in particular to a real-time review monitoring and data acquisition and storage system and method. Background Art

[0002] At this stage, data has become the core resource for driving decision-making and business optimization. Data has important use value for public resource transactions. The research on real-time review monitoring and data collection and storage systems aims to improve data processing efficiency and provide support for real-time analysis. Researchers have designed system architectures including high-concurrency data collection, message queue management, and stream processing systems for different scenarios, realizing real-time collection and processing of large amounts of data, providing the possibility for rapid decision-making. However, most of them have not solved how to obtain monitoring data for fusion processing and predictive analysis, store the fused monitoring data and monitoring results on the cloud platform, and visualize the monitoring data and monitoring results.

[0003] For example, a Chinese patent application with publication number CN114911667A discloses a monitoring data acquisition method, system and storage medium, which are applied to a serial acquisition device, and include the following steps: obtaining configuration information, and determining the devices belonging to the first group and the second group respectively according to the configuration information; determining the fixed acquisition frequency of each device in the first group, and performing data acquisition on the devices in the first group according to the corresponding fixed acquisition frequency; determining the initial acquisition frequency of each device in the second group, and after performing at least two rounds of acquisition on each device in the second group, dynamically adjusting the acquisition frequency of the devices in the second group according to the data collected by the devices; wherein the acquisition frequency of the devices in the second group is set with an upper limit and a lower limit. This solution can reduce the power consumption of the acquisition device, while ensuring that the high real-time devices can be acquired in a timely manner.

[0004] For example, a Chinese patent with authorization announcement number CN113641552B discloses a method, system, electronic device and storage medium for horizontal expansion of monitoring data acquisition, which belongs to the field of monitoring data acquisition. The method includes: after the initial data acquisition of a preset monitoring instance is completed, the memory space required by the monitoring system is calculated according to the data amount, collection interval time and data retention time of each collection point; if the memory space available to the monitoring system is less than the required memory space, the horizontal expansion of the monitoring system is triggered; the preset monitoring instance becomes the main monitoring instance, and a number of slave monitoring instances are derived, wherein the number of slave monitoring instances is calculated based on the comparison between the required memory space and the available memory space, and each monitoring instance reserves a buffer space of a preset threshold; each collection point collects data through the main monitoring instance and a number of slave monitoring instances, which can solve the problem of monitoring system crash due to a large number of collection points and data loss of a monitoring point for a certain period of time due to the failure of a single monitoring instance.

[0005] The above patents have the problems raised by the background technology: the above monitoring data collection method determines the devices belonging to the first group and the second group respectively by obtaining configuration information, determines the fixed collection frequency of each device in the first group, and collects data for the devices in the first group according to the corresponding fixed collection frequency, determines the initial collection frequency of each device in the second group, and after at least two rounds of collection for each device in the second group, dynamically adjusts the collection frequency of the devices in the second group according to the data collected by the devices; the above monitoring data collection horizontal expansion method calculates the memory space required by the monitoring system according to the data amount, collection interval time and data retention time of each collection point after the initial data collection of the preset monitoring instance is completed. If the available memory space of the monitoring system is less than the required memory space, the horizontal expansion of the monitoring system is triggered, the preset monitoring instance becomes the main monitoring instance, and a number of slave monitoring instances are derived, where the number of slave monitoring instances is calculated based on the comparison between the required memory space and the available memory space, and each monitoring instance reserves a buffer space of a preset threshold. The above two patents do not solve the problem of how to obtain monitoring data for fusion processing and predictive analysis, store the fused monitoring data and monitoring results on the cloud platform, and visualize the monitoring data and monitoring results. To solve this problem, the present invention proposes a real-time review monitoring and data acquisition storage system and method. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the problems existing in the above-mentioned existing real-time review monitoring and data acquisition storage system and method, the present invention is proposed.

[0008] Therefore, the object of the present invention is to provide a real-time review monitoring and data acquisition storage system and method.

[0009] In order to solve the above technical problems, the present invention provides a real-time review monitoring and data acquisition storage system, including: a data acquisition module, a data fusion module, a monitoring and analysis module, a data storage module, and an interactive feedback module;

[0010] The data acquisition module is used to acquire monitoring data and control the acquisition frequency of monitoring data through a dynamic acquisition strategy;

[0011] The data fusion module is used to perform fusion processing on the acquired monitoring data through a data fusion strategy;

[0012] The monitoring and analysis module is used to perform predictive analysis on the fused monitoring data through a predictive analysis strategy to generate monitoring results;

[0013] The data storage module is used to store the integrated monitoring data and monitoring results to the cloud platform through a secure storage strategy;

[0014] The interactive feedback module is used to receive and visually display monitoring data and monitoring results through a feedback display strategy.

[0015] As a preferred solution of the real-time review monitoring and data acquisition storage system of the present invention, wherein: the monitoring data is obtained, and the acquisition frequency of the monitoring data is controlled by a dynamic acquisition strategy, and the dynamic acquisition strategy includes:

[0016] Configure monitoring data thresholds and resource usage thresholds, obtain the measured values ​​and resource usage rates of the monitoring data at different times in real time, and calculate the data acquisition frequency at different times. The function expression of the data acquisition frequency is as follows:

[0017] ;

[0018] In the formula, Indicates time The frequency of data acquisition, Indicates time The frequency of data acquisition, represents the data adjustment coefficient, Indicates taking the maximum value, Indicates monitoring data at time The measured value of Indicates the monitoring data threshold. represents the resource adjustment factor, Indicates resource utilization. Indicates the resource usage threshold.

[0019] As a preferred solution of the real-time review monitoring and data acquisition storage system described in the present invention, the dynamic acquisition strategy also includes:

[0020] like , that is, the monitoring data at time If the measured value is greater than the monitoring data threshold, the acquisition frequency of monitoring data is increased to obtain more monitoring data for subsequent analysis;

[0021] like , that is, if the resource usage rate is greater than the resource usage threshold, the frequency of obtaining monitoring data is reduced to avoid system overload.

[0022] As a preferred solution of the real-time review monitoring and data acquisition storage system of the present invention, the acquired monitoring data is fused by the data fusion strategy, and the data fusion strategy includes:

[0023] Extract the features of the monitoring data, divide the monitoring data into regional blocks, each regional block includes a set of features of the monitoring data, pre-process the monitoring data in each regional block, calculate the feature similarity between each regional block, and fuse the monitoring data of each regional block through a regional fusion formula to obtain fused monitoring data.

[0024] As a preferred solution of the real-time review monitoring and data acquisition storage system described in the present invention, the influence of the regional block is determined, the initial fusion weight and influence threshold of the regional block are configured, and the initial fusion weight is adjusted according to the feature similarity between the regional blocks. The function expression for adjusting the initial fusion weight is as follows:

[0025] ;

[0026] ;

[0027] In the formula, Represents the weight adjustment factor for adjusting the initial fusion weight, Indicates area block and area blocks The feature similarity between represents the impact threshold, Indicates area block The adjusted fusion weights, Indicates area block The initial fusion weights, Indicates that the area block With other non-regional blocks The weight adjustment factor is multiplied by .

[0028] As a preferred solution of the real-time review monitoring and data acquisition storage system of the present invention, the monitoring data of each regional block is fused through the regional fusion formula, and the calculation expression of the regional fusion formula is as follows:

[0029] ;

[0030] In the formula, represents the fused monitoring data, Indicates the total number of region blocks, Indicates area block A collection of features of monitoring data.

[0031] As a preferred solution of the real-time review monitoring and data acquisition storage system described in the present invention, wherein: the fused monitoring data is predicted and analyzed by the prediction and analysis strategy to generate monitoring results, and the prediction and analysis strategy includes:

[0032] Extract the features of monitoring data from the fused monitoring data, predict the monitoring results through the predictive analysis model, train and optimize the predictive analysis model through the fused monitoring data, generate monitoring results, evaluate the monitoring results and adjust the predictive analysis model.

[0033] As a preferred solution of the real-time review monitoring and data acquisition storage system described in the present invention, the integrated monitoring data and monitoring results are stored in the cloud platform through the secure storage strategy, and the secure storage strategy includes:

[0034] Monitor the network status and transmission time during the transmission of monitoring data and monitoring results. According to the network status and transmission time, dynamically adjust the size of the regional block to optimize the transmission speed and reduce the error rate, set a retransmission mechanism, and configure the transmission time threshold. If the transmission time of the monitoring data and monitoring results is less than or equal to the transmission time threshold, the monitoring data and monitoring results are successfully transmitted.

[0035] If the transmission time of the monitoring data and monitoring results is greater than the transmission time threshold and the monitoring data and monitoring results are not transmitted successfully, the cloud platform sends a retransmission signal and the monitoring data and monitoring results are retransmitted.

[0036] As a preferred solution of the real-time review monitoring and data acquisition storage system of the present invention, the calculation formula for dynamically adjusting the size of the area block is as follows:

[0037] ;

[0038] In the formula, Indicates the size of the region block, Indicates the available bandwidth of the network. Indicates the transmission time threshold, Indicates network delay, Indicates the packet loss rate.

[0039] A real-time review monitoring and data collection and storage method, comprising:

[0040] S1. Acquire monitoring data and control the acquisition frequency of monitoring data through dynamic acquisition strategy;

[0041] S2, fusing the acquired monitoring data through data fusion strategy;

[0042] S3, perform predictive analysis on the integrated monitoring data through predictive analysis strategies to generate monitoring results;

[0043] S4, storing the integrated monitoring data and monitoring results to the cloud platform through a secure storage strategy;

[0044] S5. Receive and visualize monitoring data and monitoring results through feedback display strategies.

[0045] The beneficial effects of the present invention are as follows: the present invention acquires monitoring data through a data acquisition module, controls the acquisition frequency of monitoring data through a dynamic acquisition strategy, and adjusts the frequency of data acquisition according to actual needs, thereby optimizing resource use and improving data processing efficiency, ensuring that the system can flexibly respond to different monitoring scenarios, while reducing the collection of useless data and improving overall efficiency; the data fusion module performs fusion processing on the acquired monitoring data through a data fusion strategy, provides more comprehensive monitoring data for subsequent analysis and use, provides a comprehensive data perspective, provides a richer and more accurate information basis for subsequent analysis and decision-making, and is conducive to revealing the deeper state and trend of the monitored object; the monitoring analysis module performs predictive analysis on the fused monitoring data through a predictive analysis strategy, generates monitoring results, and performs in-depth analysis on the fused data, which can achieve result prediction, and helps enterprises take measures in advance to avoid losses and optimize resource allocation; the data storage module stores the fused monitoring data and monitoring results to a cloud platform through a secure storage strategy, ensuring the security and privacy of the data, while ensuring the persistence and reliability of the data; the interactive feedback module receives and visually displays the monitoring data and monitoring results through a feedback display strategy, enhances the user experience and the interactivity of the system, and the user can easily understand complex data and make quick decisions based on the displayed information.

[0046] By automating the data acquisition, analysis and storage processes, the system reduces the need for manual intervention, lowers operation and maintenance costs, and together improves the intelligence level of the entire monitoring system, enabling it to more effectively support real-time decision-making, risk management and optimal resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0048] Figure 1 A system structure diagram of a real-time review monitoring and data acquisition storage system of the present invention;

[0049] Figure 2A data fusion strategy flow chart of a real-time review monitoring and data acquisition storage system of the present invention;

[0050] Figure 3 A flow chart of a security storage strategy for a real-time review monitoring and data acquisition storage system of the present invention;

[0051] Figure 4 The present invention is a method flow chart of a real-time review monitoring and data acquisition and storage method. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0055] Example 1

[0056] In this embodiment, a system structure diagram of a real-time review monitoring and data acquisition storage system is provided, such as Figure 1 As shown, a real-time review monitoring and data acquisition storage system includes a data acquisition module, a data fusion module, a monitoring and analysis module, a data storage module, and an interactive feedback module.

[0057] The data acquisition module is used to acquire monitoring data. It controls the acquisition frequency of monitoring data through dynamic acquisition strategies and adjusts the frequency of data acquisition according to actual needs, thereby optimizing resource utilization and improving data processing efficiency.

[0058] Acquire monitoring data and control the acquisition frequency of monitoring data through dynamic acquisition strategies. Dynamic acquisition strategies include:

[0059] Configure monitoring data thresholds and resource usage thresholds, obtain the measured values ​​and resource usage rates of monitoring data at different times in real time, and calculate the data acquisition frequency at different times. The function expression of data acquisition frequency is as follows:

[0060] ;

[0061] In the formula, Indicates time The frequency of data acquisition, Indicates time The frequency of data acquisition, represents the data adjustment coefficient, Indicates taking the maximum value, Indicates monitoring data at time The measured value of Indicates the monitoring data threshold. represents the resource adjustment factor, Indicates resource utilization. Indicates the resource usage threshold.

[0062] What needs to be explained is: Data adjustment coefficient Resource adjustment coefficient used to control the impact on data acquisition frequency when the measured value of monitoring data exceeds the monitoring data threshold Used to control the impact of resource overload on data acquisition frequency. Resource utilization includes the usage of CPU, memory, and network bandwidth.

[0063] Dynamic acquisition strategies also include:

[0064] like , that is, the monitoring data at time If the measured value is greater than the monitoring data threshold, the acquisition frequency of monitoring data is increased to obtain more monitoring data for subsequent analysis;

[0065] like , that is, if the resource usage rate is greater than the resource usage threshold, the frequency of obtaining monitoring data is reduced to avoid system overload.

[0066] The real-time review monitoring and data acquisition storage system acquires monitoring data through the existing electronic bidding transaction system and audio and video remote conference monitoring system. The real-time monitoring data includes the evaluation expert's scoring data and the number of times the bidding documents are viewed. The monitoring data is preliminarily analyzed to identify the fluctuation patterns and trends of the monitoring data, set thresholds for each monitoring data, and evaluate the current system resource usage including CPU, memory, and network bandwidth to obtain resource utilization. The frequency of monitoring data acquisition is dynamically adjusted based on the feedback of monitoring data and system resource utilization. At the same time, when the measured value of the monitoring data is greater than the monitoring data threshold, the frequency of monitoring data acquisition needs to be increased to obtain more monitoring data for subsequent analysis. When the resource utilization rate is greater than the resource utilization threshold, the frequency of monitoring data acquisition needs to be reduced to avoid system overload.

[0067] The data fusion module is used to fuse the acquired monitoring data through data fusion strategies, provide more comprehensive monitoring data for subsequent analysis, provide a comprehensive data perspective, and provide a richer and more accurate information basis for subsequent analysis and decision-making, which is conducive to revealing the deeper status and trends of the monitored objects.

[0068] The acquired monitoring data is fused through data fusion strategy. The data fusion strategy is as follows: Figure 2 As shown, specifically including:

[0069] Extract the features of the monitoring data and divide the monitoring data into regional blocks. Each regional block includes a set of monitoring data features, which come from one or more data sources. At the same time, ensure that the monitoring data in the same time period is allocated to the corresponding regional blocks to maintain the time consistency of the monitoring data. Preprocess the monitoring data in each regional block, including data cleaning, normalization and other operations, to prepare for the fusion of monitoring data, calculate the feature similarity between each regional block, determine the influence of the regional block, and fuse the monitoring data of each regional block through the regional fusion formula to obtain the fused monitoring data.

[0070] The calculation formula for the feature similarity between each region block is as follows:

[0071] ;

[0072] In the formula, Indicates area block and area blocks The feature similarity between Indicates area block No. eigenvalues, Indicates area block No. eigenvalues, Represents the total number of features of the monitoring data.

[0073] It should be explained that: when the difference between the feature values ​​of the area blocks is smaller, it means that the two area blocks are more similar, and the features of the monitoring data with high feature similarity and non-critical are eliminated according to the feature similarity between the area blocks.

[0074] Determine the influence of the regional block, configure the initial fusion weight and influence threshold of the regional block, adjust the initial fusion weight according to the feature similarity between the regional blocks, and the function expression for adjusting the initial fusion weight is as follows:

[0075] ;

[0076] ;

[0077] In the formula, Represents the weight adjustment factor for adjusting the initial fusion weight, represents the impact threshold, Indicates area block The adjusted fusion weights, Indicates area block The initial fusion weights, Indicates that the area block With other non-regional blocks The weight adjustment factor is multiplied by .

[0078] What needs to be explained is: the weight adjustment factor for adjusting the initial fusion weight It is based on the feature similarity between the regional blocks to reduce the influence of the corresponding regional blocks, the impact threshold It is used to determine when the influence of the regional block should be reduced. The influence of the regional block refers to the influence of a single regional block on the final fusion result. The initial fusion weight of the regional block is the fusion weight assigned according to the importance and reliability of the regional block. .

[0079] The monitoring data of each regional block is fused through the regional fusion formula. The calculation expression of the regional fusion formula is as follows:

[0080] ;

[0081] In the formula, represents the fused monitoring data, Indicates the total number of region blocks, Indicates area block A collection of features of monitoring data.

[0082] By solving the weight adjustment factor of the initial fusion weight to better fuse the monitoring data of each regional block, the fused monitoring data can provide the system with an accurate and comprehensive monitoring data view, thereby improving the quality of overall analysis and decision-making. At the same time, by eliminating the features of highly similar and non-critical monitoring data, the redundancy of monitoring data can be reduced and the efficiency and accuracy of data processing can be improved.

[0083] The monitoring and analysis module is used to perform predictive analysis on the fused monitoring data through predictive analysis strategies, generate monitoring results, and conduct in-depth analysis on the fused data. It can achieve result prediction, help enterprises take measures in advance, avoid losses and optimize resource allocation.

[0084] The fused monitoring data is predicted and analyzed through the prediction analysis strategy to generate monitoring results. The prediction analysis strategy includes:

[0085] Extract the features of monitoring data from the fused monitoring data, predict the monitoring results through the predictive analysis model, train and optimize the predictive analysis model through the fused monitoring data, generate monitoring results, evaluate the monitoring results and adjust the predictive analysis model.

[0086] The function expression of the predictive analysis model is as follows:

[0087] ;

[0088] In the formula, Represents the predicted monitoring results, represents the model parameters, represents the feature vector of the fused monitoring data, represents the error term.

[0089] The prediction analysis model is constructed by the linear regression method. The above model parameters and error terms can be estimated by the least squares method. The training goal is to minimize the error between the predicted monitoring results and the actual monitoring results. The fused monitoring data is input to train the prediction analysis model. The monitoring results that need to be predicted here are the results of real-time review, and the fused monitoring data is divided into a validation set to verify the accuracy of the prediction analysis model. The performance of the prediction analysis model is evaluated by the mean square error or the mean absolute error. The trained prediction analysis model is used to perform prediction analysis on new real-time monitoring data to generate monitoring results. The accuracy and reliability of the monitoring results are regularly evaluated to ensure the continued effectiveness of the prediction analysis model. If the performance deteriorates, the prediction analysis model is retrained or the parameters of the prediction analysis model are adjusted. The results of the real-time review are predicted by historical monitoring data and real-time monitoring data.

[0090] The data storage module is used to store the integrated monitoring data and monitoring results to the cloud platform through a secure storage strategy to ensure the security and privacy of the data while ensuring the persistence and reliability of the data.

[0091] The integrated monitoring data and monitoring results are stored in the cloud platform through a secure storage strategy. The secure storage strategy is as follows: Figure 3 As shown, specifically including:

[0092] Monitor the network status and transmission time during the transmission of monitoring data and monitoring results. According to the network status and transmission time, dynamically adjust the size of the regional block to optimize the transmission speed and reduce the error rate, set a retransmission mechanism, and configure the transmission time threshold. If the transmission time of the monitoring data and monitoring results is less than or equal to the transmission time threshold, the monitoring data and monitoring results are successfully transmitted.

[0093] If the transmission time of the monitoring data and monitoring results is greater than the transmission time threshold and the monitoring data and monitoring results are not transmitted successfully, the cloud platform sends a retransmission signal and the monitoring data and monitoring results are retransmitted.

[0094] The calculation formula for dynamically adjusting the size of the region block is as follows:

[0095] ;

[0096] In the formula, Indicates the size of the region block, Indicates the available bandwidth of the network. Indicates the transmission time threshold, Indicates network delay, Indicates the packet loss rate.

[0097] What needs to be explained is: the available bandwidth of the network Can be obtained through network performance monitoring tools, network delay It is the transmission time of monitoring data from the sender to the receiver, the packet loss rate It refers to the proportion of regional blocks lost during network transmission, and the transmission time threshold Refers to the maximum allowed transmission time of monitoring data from the sender to the receiver.

[0098] Monitor the available bandwidth, network delay and packet loss rate of the network in real time, calculate the optimal regional block size according to the above formula, and adjust the regional block size of data transmission according to the calculated regional block size to optimize the transmission speed and reduce the error rate. If the transmission time of the monitoring data and monitoring results is greater than the transmission time threshold and the monitoring data and monitoring results are not transmitted successfully, the cloud platform will send a retransmission signal, and the monitoring data and monitoring results will be retransmitted according to the new regional block size to ensure that the transmission of monitoring data and monitoring results remains efficient and reliable in the ever-changing network environment.

[0099] The interactive feedback module is used to receive and visualize monitoring data and monitoring results through feedback display strategies, which enhances the user experience and the interactivity of the system. Users can easily understand complex data and make quick decisions based on the displayed information.

[0100] Feedback display strategies include:

[0101] After receiving the transmitted monitoring data and monitoring results, the cloud platform automatically identifies and processes the data format of the monitoring data and monitoring results, and converts them into a unified display format. At the same time, it creates dynamic charts to reflect the changes in monitoring data and monitoring results in real time. At the same time, it configures the query function of historical monitoring data and historical monitoring results. When the historical monitoring data and historical monitoring results do not meet the expected results, an alarm is automatically triggered and a visual prompt is given.

[0102] Example 2

[0103] In this embodiment, a method flow chart of a real-time review monitoring and data collection and storage method is provided, such as Figure 4 As shown, a real-time review monitoring and data collection and storage method includes:

[0104] S1. Acquire monitoring data and control the acquisition frequency of monitoring data through dynamic acquisition strategy.

[0105] S2. The acquired monitoring data is fused through data fusion strategy.

[0106] The acquired monitoring data is fused through data fusion strategies, which include:

[0107] Extract the features of the monitoring data and divide the monitoring data into regional blocks. Each regional block includes a set of monitoring data features, which come from one or more data sources. At the same time, ensure that the monitoring data in the same time period is allocated to the corresponding regional blocks to maintain the time consistency of the monitoring data. Preprocess the monitoring data in each regional block, including data cleaning, normalization and other operations, to prepare for the fusion of monitoring data, calculate the feature similarity between each regional block, determine the influence of the regional block, and fuse the monitoring data of each regional block through the regional fusion formula to obtain the fused monitoring data.

[0108] S3. Perform predictive analysis on the integrated monitoring data through predictive analysis strategies to generate monitoring results.

[0109] S4. Store the integrated monitoring data and monitoring results to the cloud platform through a secure storage strategy.

[0110] The integrated monitoring data and monitoring results are stored in the cloud platform through secure storage strategies. The secure storage strategies include:

[0111] Monitor the network status and transmission time during the transmission of monitoring data and monitoring results. According to the network status and transmission time, dynamically adjust the size of the regional block to optimize the transmission speed and reduce the error rate, set a retransmission mechanism, and configure the transmission time threshold. If the transmission time of the monitoring data and monitoring results is less than or equal to the transmission time threshold, the monitoring data and monitoring results are successfully transmitted.

[0112] If the transmission time of the monitoring data and monitoring results is greater than the transmission time threshold and the monitoring data and monitoring results are not transmitted successfully, the cloud platform sends a retransmission signal and the monitoring data and monitoring results are retransmitted.

[0113] S5. Receive and visualize monitoring data and monitoring results through feedback display strategies.

[0114] For the specific contents of a real-time review monitoring and data acquisition and storage method, please refer to a real-time review monitoring and data acquisition and storage system, which will not be repeated here.

[0115] Example 3

[0116] In this embodiment, a computer device is provided, including a memory and a processor, the memory is used to store instructions, and the processor is used to execute the instructions, so that the computer device executes the steps of implementing the above-mentioned real-time review monitoring and data acquisition and storage method.

[0117] Example 4

[0118] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the steps of the above-mentioned real-time review monitoring and data acquisition and storage method are implemented.

[0119] The computer-readable storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and other media for storing program codes.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A real-time review monitoring and data acquisition storage system, characterized by: include: Data acquisition module, data fusion module, monitoring and analysis module, data storage module, interactive feedback module; The data acquisition module is used to acquire monitoring data and control the acquisition frequency of monitoring data through a dynamic acquisition strategy; The data fusion module is used to perform fusion processing on the acquired monitoring data through a data fusion strategy; The acquired monitoring data is fused by the data fusion strategy, and the data fusion strategy includes: Extracting features of the monitoring data, dividing the monitoring data into regional blocks, each regional block including a set of features of the monitoring data, preprocessing the monitoring data in each regional block, calculating the feature similarity between each regional block, fusing the monitoring data of each regional block through a regional fusion formula, and obtaining fused monitoring data; Determine the influence of the regional block, configure the initial fusion weight and influence threshold of the regional block, and adjust the initial fusion weight according to the feature similarity between the regional blocks. The function expression for adjusting the initial fusion weight is as follows: ； ； In the formula, Represents the weight adjustment factor for adjusting the initial fusion weight, Indicates area block and area blocks The feature similarity between represents the impact threshold, Indicates area block The adjusted fusion weights, Indicates area block The initial fusion weights, Indicates that the area block With other non-regional blocks Multiply the weight adjustment factor by ; The monitoring and analysis module is used to perform predictive analysis on the fused monitoring data through a predictive analysis strategy to generate monitoring results; The data storage module is used to store the integrated monitoring data and monitoring results to the cloud platform through a secure storage strategy; The interactive feedback module is used to receive and visually display monitoring data and monitoring results through a feedback display strategy.

2. A real-time review monitoring and data acquisition storage system as claimed in claim 1, characterized in that: Acquire the monitoring data, and control the acquisition frequency of the monitoring data through a dynamic acquisition strategy, wherein the dynamic acquisition strategy includes: Configure monitoring data thresholds and resource usage thresholds, obtain the measured values ​​and resource usage rates of the monitoring data at different times in real time, and calculate the data acquisition frequency at different times. The function expression of the data acquisition frequency is as follows: ； In the formula, Indicates time The frequency of data acquisition, Indicates time The frequency of data acquisition, represents the data adjustment coefficient, Indicates taking the maximum value, Indicates monitoring data at time The measured value of Indicates the monitoring data threshold. represents the resource adjustment factor, Indicates resource utilization. Indicates the resource usage threshold.

3. A real-time review monitoring and data acquisition storage system as claimed in claim 2, characterized in that: The dynamic acquisition strategy also includes: like , that is, the monitoring data at time If the measured value is greater than the monitoring data threshold, the acquisition frequency of monitoring data is increased to obtain more monitoring data for subsequent analysis; like , that is, if the resource usage rate is greater than the resource usage threshold, the frequency of obtaining monitoring data is reduced to avoid system overload.

4. A real-time review monitoring and data acquisition storage system as claimed in claim 3, characterized in that: The monitoring data of each regional block is fused through the regional fusion formula, and the calculation expression of the regional fusion formula is as follows: ； In the formula, represents the fused monitoring data, Indicates the total number of region blocks, Indicates area block A collection of features of monitoring data.

5. A real-time review monitoring and data acquisition storage system as claimed in claim 4, characterized in that: The fused monitoring data is predicted and analyzed by the prediction and analysis strategy to generate monitoring results. The prediction and analysis strategy includes: Extract the features of monitoring data from the fused monitoring data, predict the monitoring results through the predictive analysis model, train and optimize the predictive analysis model through the fused monitoring data, generate monitoring results, evaluate the monitoring results and adjust the predictive analysis model.

6. A real-time review monitoring and data acquisition storage system as claimed in claim 5, characterized in that: The integrated monitoring data and monitoring results are stored in the cloud platform through the secure storage strategy, and the secure storage strategy includes: Monitor the network status and transmission time during the transmission of monitoring data and monitoring results. According to the network status and transmission time, dynamically adjust the size of the regional block to optimize the transmission speed and reduce the error rate, set a retransmission mechanism, and configure the transmission time threshold. If the transmission time of the monitoring data and monitoring results is less than or equal to the transmission time threshold, the monitoring data and monitoring results are successfully transmitted. If the transmission time of the monitoring data and monitoring results is greater than the transmission time threshold and the monitoring data and monitoring results are not transmitted successfully, the cloud platform sends a retransmission signal and the monitoring data and monitoring results are retransmitted.

7. A real-time review monitoring and data acquisition storage system as claimed in claim 6, characterized in that: The calculation formula for dynamically adjusting the size of the region block is as follows: ； In the formula, Indicates the size of the region block, Indicates the available bandwidth of the network. Indicates the transmission time threshold, Indicates network delay, Indicates the packet loss rate.

8. A real-time review monitoring and data collection and storage method, implemented based on a real-time review monitoring and data collection and storage system according to any one of claims 1 to 7, characterized in that: include: S1. Acquire monitoring data and control the acquisition frequency of monitoring data through dynamic acquisition strategy; S2, fusing the acquired monitoring data through data fusion strategy; S3, perform predictive analysis on the integrated monitoring data through predictive analysis strategies to generate monitoring results; S4, storing the integrated monitoring data and monitoring results to the cloud platform through a secure storage strategy; S5. Receive and visualize monitoring data and monitoring results through feedback display strategies.

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