Multistage platform monitoring system and monitoring method

Abstract

The invention discloses a multi-level platform monitoring system and monitoring method, and belongs to the technical field of video monitoring, and the method comprises the steps that a server receives basic monitoring data collected by each level platform collection end, and generates data to be transmitted; transmission optimization is carried out on the data to be transmitted, and a dynamic compression strategy is adopted in the transmission optimization method; analyzing the transmitted monitoring data, performing decision-making early warning according to an analysis result, and performing integrity verification before outputting an early warning result; wherein the server is a transfer platform with data receiving, data transmission and data processing functions, and is respectively connected with the acquisition end and the data processing end of each hierarchical platform. In the implementation process of the technical scheme provided by the invention, the data acquisition end and the data transmission end are optimized, so that the data volume is reduced, the subsequent data transmission and data analysis efficiency is improved, and the problems of data overload, abnormal early warning and the like in the multi-stage platform monitoring process are avoided.

Description

Technical Field

[0001] The present application relates to the field of video surveillance technology, and specifically to a multi-level platform monitoring system and a monitoring method. Background Art

[0002] At present, the application fields of monitoring systems are becoming more and more extensive, and the forms are becoming more diversified. They are widely used in transportation, buildings and other industries.

[0003] Traditional monitoring systems are of a single dimension and can usually only collect images or video data, and then analyze the collected content manually. This method is not only time-consuming and labor-intensive, but also has limited recognition of effective content. Therefore, in recent years, monitoring systems that can automatically identify collected content have been developed. This type of monitoring system combines artificial intelligence, big data and other technologies to automatically analyze the monitored content, and then issue corresponding warnings to improve the accuracy of monitoring.

[0004] However, with the emergence of multi-level platforms and cascade structures, existing monitoring systems still face challenges in processing large-scale, multi-level cascade monitoring data. For example, the multi-level architecture of the system will lead to risks such as large data overload and excessive redundant information. When analyzing these data, it is easy to cause low analysis efficiency and even miss key information, posing certain security risks.

[0005] Therefore, it is necessary to provide a multi-level platform monitoring system and monitoring method to solve the above problems.

[0006] It should be noted that the above information disclosed in this background technology section is only for understanding the background technology of the present application concept, and therefore, it may contain information that does not constitute the prior art. Summary of the invention

[0007] Based on the above-mentioned problems existing in the prior art, the problem to be solved by the present application is: to provide a multi-level platform monitoring system and a monitoring method, so as to prevent data overload and abnormal warning problems of the multi-level platform monitoring system by optimizing the method at different stages.

[0008] The technical solution adopted by the present application to solve the technical problem is: a multi-level platform monitoring method, the method comprising:

[0009] The server receives basic monitoring data collected by the collection terminals of each level platform, pre-processes the basic monitoring data, and generates data to be transmitted;

[0010] Optimizing the transmission of the data to be transmitted, wherein the method of transmission optimization adopts a dynamic compression strategy;

[0011] Analyze the transmitted monitoring data, make decisions and early warnings based on the analysis results, and perform integrity verification before outputting the early warning results;

[0012] Among them, the server refers to a transit platform with data reception, data transmission, and data processing functions, which are respectively connected to the collection end and data processing end of each level platform.

[0013] During the implementation of the technical solution of the present application, by optimizing the data collection end and the data transmission end respectively, the data volume is reduced while improving the efficiency of subsequent data transmission and data analysis, thus avoiding problems such as data overload and warning anomalies during multi-level platform monitoring.

[0014] Furthermore, preprocessing the basic monitoring data further includes the following steps:

[0015] The basic monitoring data is classified according to data type to obtain image data and non-image data, wherein video data is regarded as image data;

[0016] Perform global pixel value recognition on image data and obtain statistical characteristics of pixel value distribution based on timestamps;

[0017] The image data is screened according to the statistical characteristics of the pixel value distribution to screen out the first data to be transmitted and the second candidate data.

[0018] Furthermore, the screening of image data specifically includes: obtaining a pixel value distribution sequence in the order of timestamps, performing adjacent timestamp fluctuation calculations on the pixel value distribution sequence to obtain a fluctuation value set, setting a fluctuation threshold, taking image data corresponding to timestamps below the fluctuation threshold as second candidate data, and image data corresponding to timestamps above the fluctuation threshold as first data to be transmitted, performing correlation analysis on non-image data, and dividing the non-image data into second data to be transmitted and second candidate data according to the analysis results.

[0019] Furthermore, a timed deletion mechanism is provided for the first candidate data and the second candidate data. If the first candidate data and the second candidate data are not extracted and traced within a set time, the first candidate data and the second candidate data are automatically deleted in the order of timestamps.

[0020] Furthermore, the dynamic compression strategy includes the following steps:

[0021] Extract features from the data to be transmitted, select at least one feature data as a benchmark, perform correlation analysis on the remaining feature data, and generate analysis results;

[0022] The characteristic data is numerically represented according to the generated analysis result, and the magnitude of the numerical value represents the relevance of the characteristic data;

[0023] The compression ratio is dynamically selected based on the relevance, and is negatively correlated with the relevance of the feature data.

[0024] Furthermore, when performing correlation analysis on the characteristic data, one or more characteristic data are selected multiple times as benchmarks, correlation analysis under different benchmarks is performed, and an average value is taken according to the results of the multiple correlation analyses.

[0025] Furthermore, when the relevance of feature data is zero, the data is directly eliminated.

[0026] Furthermore, the process of making decision-making warnings based on the analysis results includes: formulating warning rules based on thresholds or pattern recognition, issuing warning notifications according to the warning rules, and verifying the warning results.

[0027] Furthermore, the early warning result is verified by hash verification. The early warning result of the server is first hashed to generate a first hash value, and then the generated first hash value is stored in a storage medium. When an early warning is needed, the early warning data output by the server is obtained, and the data is hashed to generate a second hash value. The first hash value is then compared with the second hash value to determine whether they are consistent. If they are consistent, they are outputted. If they are inconsistent, a check signal is sent to the server.

[0028] A multi-level platform monitoring system includes: a data generation module to be transmitted, which is used for a server to receive basic monitoring data collected from platform collection terminals at each level, and pre-process the basic monitoring data to generate data to be transmitted;

[0029] A transmission optimization module, used for optimizing the transmission of data to be transmitted, wherein the transmission optimization method adopts a dynamic compression strategy;

[0030] The analysis and warning module is used to analyze the monitoring data after transmission, make decisions and warnings based on the analysis results, and perform integrity verification before outputting the warning results.

[0031] The beneficial effect of the present application is that a multi-level platform monitoring system and monitoring method provided by the present application optimizes the data acquisition end and the data transmission end respectively, thereby reducing the amount of data while improving the efficiency of subsequent data transmission and data analysis, avoiding problems such as data overload and warning anomalies during multi-level platform monitoring.

[0032] In addition to the above-described purposes, features and advantages, the present application also has other purposes, features and advantages. The present application will be further described in detail with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings in the specification, which constitute a part of the present application, are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0034] In the attached picture:

[0035] Figure 1 This is a schematic diagram of the overall process of a multi-level platform monitoring method in this application;

[0036] Figure 2 This is a schematic diagram of the module structure of a multi-level platform monitoring system in this application. DETAILED DESCRIPTION

[0037] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0039] Embodiment 1: Figure 1 As shown, the present application provides a multi-level platform monitoring method, which is applied to a multi-level platform monitoring system, and optimizes the collected monitoring data at the data acquisition end and the data transmission end, thereby reducing the overload and excessive redundant information in the monitoring data analysis and processing process. The multi-level platform monitoring method includes the following steps:

[0040] Step 10: The server receives the basic monitoring data collected from the collection terminals of each level platform, and pre-processes the basic monitoring data to generate data to be transmitted;

[0041] In the existing monitoring systems, multi-level platform monitoring systems with multi-level architecture are more common, such as road monitoring, building monitoring, etc. These multi-level platforms will collect a large amount of monitoring data at the same time, and the types of these monitoring data are different, such as image data, video data, sensor signal data, etc. The monitoring data collected by the collection end of each level platform is used as the basic monitoring data, and after pre-processing, it generates data to be transmitted;

[0042] Among them, the server refers to a transfer platform with functions such as data reception, data transmission, and data processing. It is connected to the collection end and data processing end of each level platform respectively, and pre-processes the basic monitoring data collected by the collection end. After generating the data to be transmitted, it optimizes the data to be transmitted in the process of transmitting it to the data processing end, thereby realizing dual optimization of the monitoring data from the data collection end and the data transmission end. Specifically, pre-processing the basic monitoring data further includes the following steps:

[0043] Step 101: Classify basic monitoring data according to data types, and obtain image data and non-image data, wherein video data is regarded as image data;

[0044] When preprocessing basic monitoring data, we must first classify the basic monitoring data according to the data type, because different data types require different processing methods. For image data, image processing methods are used for optimization, while for non-image data, such as sensor signal data, corresponding signal processing methods are used for optimization. In order to improve processing efficiency, the basic monitoring data is divided into image data and non-image data, and video data is also regarded as image data;

[0045] Among them, the method of classifying the basic monitoring data according to the data type is implemented by using an intelligent classification algorithm. Specifically, the algorithm can analyze the data at different time points in the basic monitoring data in the order of timestamps, and determine whether the data contains pixels or other feature information belonging to image features, thereby accurately distinguishing image data from non-image data, and classifying the data belonging to image features as image data in the order of timestamps, and classifying other data as non-image data. Among them, video data is also classified as image data because it is composed of multiple frame images;

[0046] Step 102: performing global pixel value recognition on the image data, and obtaining statistical features of pixel value distribution according to the timestamp;

[0047] Each image data has a pixel value. In order to optimize the image data, we must first perform global pixel value recognition and obtain the statistical characteristics of pixel value distribution at different timestamps. For video data, it is split into multiple image data in the form of video frames. The pixel value recognition can refer to the camera histogram. The principle is to divide the brightness level of the photo into 256 levels, from 0 (dark tone) on the left to 255 (bright tone) on the right, and then count the number of pixels at each brightness level. This information is displayed in a graphical form to obtain the pixel value of the image data.

[0048] Step 103: Screening the image data according to the statistical characteristics of the pixel value distribution to select the first data to be transmitted and the second candidate data;

[0049] After obtaining the statistical characteristics of pixel value distribution, the image data can be screened according to the statistical characteristics to screen out the first data to be transmitted and the second candidate data, wherein the first data to be transmitted is the image data to be transmitted today, and the second candidate data is the data temporarily stored in the server after screening. The second candidate data does not participate in the transmission and is only used for subsequent tracing, thereby reducing the amount of image data without affecting the monitoring data analysis results. The screening of image data according to the statistical characteristics of pixel value distribution specifically includes:

[0050] Step A: Obtain pixel value distribution sequence in the order of timestamps;

[0051] In the field of image processing, pixel values ​​determine the image's brightness, contrast, detail, and other characteristics. Pixel values ​​are the values ​​assigned when an image is digitized. In monitoring data, the pixel values ​​of image data at different times may be different, and image changes during the monitoring process will be reflected in the pixel values. At the same time, in some scenes, such as no changes in the monitoring screen and night scenes, the monitoring data has a low change range. Changes in these scenes will be reflected in the pixel value fluctuations. Therefore, it is necessary to first obtain the pixel value distribution sequence in the order of timestamps;

[0052] Step B: Calculate the fluctuation of adjacent timestamps on the pixel value distribution sequence to obtain a set of fluctuation values;

[0053] In the entire monitoring data, as the monitoring content changes, the pixel value of the image data will change. Through this change, the pixel fluctuation value under adjacent time stamps can be obtained, wherein the pixel fluctuation value is obtained by using the mean difference method, specifically, firstly calculating the average value of the pixel values ​​under all time stamps, and then performing absolute difference calculation between the average value and the pixel value difference of adjacent time stamps, and dividing the calculated result by two to obtain the pixel fluctuation value of adjacent time stamps, and according to this method, all fluctuation values ​​are obtained as a fluctuation value set;

[0054] For example, at two adjacent timestamps, the collected pixel values ​​are 200 and 220 respectively, and the average pixel value at all timestamps is 100. Then the fluctuation value of this adjacent timestamp is 40, that is: |100-(220-200)| / 2=40;

[0055] Step C: setting a fluctuation threshold, taking the image data corresponding to the timestamp below the fluctuation threshold as the second candidate data, and taking the image data corresponding to the timestamp above the fluctuation threshold as the first data to be transmitted;

[0056] After calculating the pixel fluctuation values ​​of all timestamps, the data below the fluctuation threshold is screened out by setting the fluctuation threshold as the second candidate data, which does not participate in the subsequent transmission. When tracing the source is required later, the second candidate data is extracted according to the timestamp to facilitate tracing the source. In order to save the storage space of the server, a timed deletion mechanism can be set for the server. After the second candidate data is not extracted for more than the specified time, the data according to the timestamp is automatically deleted.

[0057] For example, if the fluctuation threshold is set to 30, in the above process, the adjacent timestamps with a fluctuation value of 40 will enter the subsequent transmission process as the first data to be transmitted, and the adjacent timestamps with a fluctuation value lower than 30 will be screened as the second candidate data and stored in the server;

[0058] Step 104: performing correlation analysis on the non-image data, and dividing the non-image data into second data to be transmitted and second candidate data according to the analysis result;

[0059] After the image data is processed, the non-image data also needs to be processed and the correlation analysis is performed on these data to distinguish their importance in the monitoring scene, and the data with higher correlation is regarded as the second data to be transmitted. These data will enter the subsequent transmission link and be transmitted together with the first transmission data. For the data with lower correlation, it will be classified as the second candidate data and stored in the server at the same time as the first candidate data, waiting for subsequent tracing. In addition, the same timed deletion mechanism as the first candidate data can be selected for the second candidate data to save server storage space.

[0060] Among them, the correlation analysis of non-image data adopts a multi-dimensional analysis method, including the temporal and spatial distribution characteristics of the data, the frequency of event occurrence and its correlation with the image data, etc. For details, reference can be made to the correlation analysis of non-image data in the prior art, which will not be described in detail in this embodiment;

[0061] Step 20: Optimizing the transmission of the data to be transmitted, wherein the method of transmission optimization adopts a dynamic compression strategy;

[0062] After completing the monitoring data optimization at the data collection end, it is also necessary to optimize the data transmission end to further reduce the transmission pressure and subsequent data analysis and processing load. Among them, the transmission optimization of the transmission data is realized by using a dynamic compression strategy. The dynamic compression strategy automatically adjusts the compression ratio according to the data type and characteristics. Specifically, the dynamic compression strategy includes the following steps:

[0063] Step 201: extracting features from the data to be transmitted, selecting at least one feature data as a benchmark, performing correlation analysis on the remaining feature data, and generating analysis results;

[0064] When performing dynamic compression, the correlation between data needs to be considered. Therefore, it is necessary to extract features from the data to be transmitted and select a benchmark. In this embodiment, at least one feature data among the extracted feature data is selected as the benchmark, and other feature data are analyzed for correlation using common methods in the field such as correlation coefficient analysis, covariance analysis, heat map analysis, and principal component analysis. The specific correlation analysis method can refer to the prior art and is not described in detail in this embodiment.

[0065] In this embodiment, one or more feature data are selected as the benchmark, which can improve the randomness of the analysis results in the subsequent correlation analysis process, and multiple selections can be made to perform correlation analysis under different benchmarks, and the average value is taken according to the results of multiple correlation analyses, thereby improving the validity of the correlation analysis results.

[0066] Step 202: numerically representing the characteristic data according to the generated analysis results, wherein the numerical value represents the relevance of the characteristic data;

[0067] In order to facilitate the expression of the correlation results, it is also necessary to perform numerical representation according to the analysis results after performing correlation analysis on the feature data, so as to facilitate the subsequent selection of the compression rate. Taking the correlation coefficient analysis method as an example, after performing correlation analysis on the feature data, each analyzed feature data will generate a correlation coefficient, such as the Pearson correlation coefficient and the Spearman correlation coefficient. Since the feature data contains a linear relationship, the Pearson correlation coefficient can be used in this embodiment, and the value range of the coefficient is -1 to 1. When performing numerical conversion, the Pearson correlation coefficient can be mapped to other data ranges by the method of linear mapping of data values. For example, when the Pierman correlation coefficient of -1 to 1 needs to be mapped to the interval of 0 to 100, the Pierman correlation coefficient can be mapped to the interval of 0 to 100 by the method of (Pearman correlation coefficient+1)*50;

[0068] Step 203: dynamically selecting a compression rate according to the relevance, where the compression rate is negatively correlated with the relevance of the feature data;

[0069] When transmitting data, the data to be transmitted needs to be compressed, and the selection of compression rate needs to be made according to the characteristics of the data. After the correlation calculation, dynamic compression can be performed according to the correlation calculation result. For example, when the correlation of a certain feature data is strong, it means that the monitoring data it represents accounts for a large proportion of the analysis. Therefore, a lower compression rate needs to be selected for the feature data to ensure its integrity. When the correlation of a certain feature data is weak, it means that the monitoring data it represents accounts for a small proportion of the analysis. Therefore, a higher compression rate can be selected for the feature data to ensure that it can be transmitted normally. Even if some data is lost during the compression process, it will have a small impact on the final analysis result. Through the distributed compression method, the transmission efficiency can be improved under the premise of ensuring the integrity of the monitoring data.

[0070] The compression rate is usually expressed as a fraction. In order to match the correlation with the feature data, it can be achieved by taking the inverse of the value in the mapped interval. In particular, when the correlation of the feature data is zero, the data can be directly eliminated because it has no correlation with other data and will not affect the analysis results.

[0071] Step 30: Analyze the transmitted monitoring data, make decision warnings based on the analysis results, and perform integrity verification before outputting the warning results.

[0072] Specifically, the process of making decision-making warnings based on the analysis results includes:

[0073] Step 301: Formulate warning rules based on thresholds or pattern recognition;

[0074] After the analysis is completed, it is necessary to set warning rules based on thresholds. When the analysis results of the monitoring data exceed or fall below the set thresholds, warnings are triggered. It can also be set based on pattern recognition. When the analysis results of the monitoring data show a specific pattern or trend, warnings are triggered.

[0075] It should be noted that, in this embodiment, the warning rules need to be set or adjusted after the analysis is completed, that is, the warning rules are not static. This method can adjust the warning rules according to the different monitoring data so that the warning process matches the analysis results of the monitoring data. Compared with the traditional fixed warning mechanism, the warning behavior can better reflect the information represented by the monitoring data.

[0076] Step 302: issuing a warning notification according to the warning rules;

[0077] After completing the above steps, the analysis results of the server are compared with the early warning mechanism to output the results, and corresponding early warning notifications are issued to facilitate manual inspections and tracing. Early warning notification methods include sound alarms, light warnings, remote mobile terminal notifications, data reports or chart generation, etc., to ensure that the early warning results are received in a timely and effective manner;

[0078] Step 303: Verify the warning result.

[0079] In order to improve the response speed of early warning actions and prevent delays or losses in the transmission of analysis results, it is also necessary to perform integrity verification before early warning notification to ensure that the information being warned is directly analyzed and to improve the effectiveness of decision-making warnings.

[0080] The integrity verification can be performed in the form of hash verification. The warning result of the server is first hashed to generate a first hash value. The calculation method can be MD5, SHA-1, SHA-256, etc., which is not limited here.

[0081] Then the generated first hash value is stored in a storage medium, such as a database, a file system, etc., to ensure the security and integrity of the hash value;

[0082] When an early warning is required, the early warning data output by the server is obtained, and a hash calculation is performed on the data to generate a second hash value. The second hash value represents the hash value generated by the information for which an early warning is required. The first hash value is then compared with the second hash value to determine whether they are consistent. If they are consistent, it means that the early warning result is complete and unchanged and can be output. If they are inconsistent, it means that the early warning information represented by the second hash value is abnormal and cannot be output directly. Instead, a check signal is sent to the server and the early warning is suspended. After manual inspection and tracing, the above steps are repeated until the first hash value is consistent with the second hash value and an early warning can be output.

[0083] Through the above verification process, the integrity and authenticity of the warning results can be verified, ensuring that the warning results have not changed, and improving the system's warning accuracy.

[0084] Embodiment 2: Figure 2 As shown, the present application provides a multi-level platform monitoring system, which runs the multi-level platform monitoring method in embodiment 1, and the system includes:

[0085] The module for generating data to be transmitted is used for the server to receive basic monitoring data collected from the collection terminals of each level platform, and to pre-process the basic monitoring data to generate data to be transmitted;

[0086] A transmission optimization module, used for optimizing the transmission of data to be transmitted, wherein the transmission optimization method adopts a dynamic compression strategy;

[0087] The analysis and warning module is used to analyze the monitoring data that has been transmitted, make decisions and warnings based on the analysis results, and perform integrity verification before outputting the warning results.

[0088] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-level platform monitoring method, characterized in that: The method includes: The server receives basic monitoring data collected by the collection terminals of each level platform, pre-processes the basic monitoring data, and generates data to be transmitted; Optimizing the transmission of the data to be transmitted, wherein the method of transmission optimization adopts a dynamic compression strategy; Analyze the transmitted monitoring data, make decisions and early warnings based on the analysis results, and perform integrity verification before outputting the early warning results; Among them, the server refers to a transit platform with data reception, data transmission, and data processing functions, which are respectively connected to the collection end and data processing end of each level platform.

2. A multi-level platform monitoring method according to claim 1, characterized in that: Preprocessing the basic monitoring data further includes the following steps: The basic monitoring data is classified according to data type to obtain image data and non-image data, wherein video data is regarded as image data; Perform global pixel value recognition on image data and obtain statistical characteristics of pixel value distribution based on timestamps; The image data is screened according to the statistical characteristics of the pixel value distribution to screen out the first data to be transmitted and the second candidate data.

3. A multi-level platform monitoring method according to claim 2, characterized in that: Screening the image data specifically includes: obtaining a pixel value distribution sequence in the order of timestamps, performing adjacent timestamp fluctuation calculations on the pixel value distribution sequence to obtain a fluctuation value set, setting a fluctuation threshold, taking image data corresponding to timestamps below the fluctuation threshold as second candidate data, and image data corresponding to timestamps above the fluctuation threshold as first data to be transmitted, performing correlation analysis on non-image data, and dividing the non-image data into second data to be transmitted and second candidate data according to the analysis results.

4. A multi-level platform monitoring method according to claim 3, characterized in that: The first candidate data and the second candidate data are provided with a timed deletion mechanism. If the first candidate data and the second candidate data are not extracted and traced within the set time, the first candidate data and the second candidate data are automatically deleted in the order of timestamps.

5. A multi-level platform monitoring method according to claim 1, characterized in that: The dynamic compression strategy includes the following steps: Extract features from the data to be transmitted, select at least one feature data as a benchmark, perform correlation analysis on the remaining feature data, and generate analysis results; The characteristic data is numerically represented according to the generated analysis result, and the magnitude of the numerical value represents the relevance of the characteristic data; The compression ratio is dynamically selected based on the relevance, and is negatively correlated with the relevance of the feature data.

6. A multi-level platform monitoring method according to claim 5, characterized in that: When performing correlation analysis on feature data, one or more feature data are selected multiple times as benchmarks, correlation analysis under different benchmarks is performed, and an average value is taken based on the results of the multiple correlation analyses.

7. A multi-level platform monitoring method according to claim 5, characterized in that: When the correlation of feature data is zero, the data is directly eliminated.

8. A multi-level platform monitoring method according to claim 1, characterized in that: The process of making decision-making warnings based on the analysis results includes: formulating warning rules based on thresholds or pattern recognition, issuing warning notifications based on the warning rules, and verifying the warning results.

9. A multi-level platform monitoring method according to claim 8, characterized in that: The early warning result is verified by hash verification. First, the early warning result of the server is hashed to generate a first hash value, and then the generated first hash value is stored in a storage medium. When an early warning is needed, the early warning data output by the server is obtained, and the data is hashed to generate a second hash value. The first hash value is then compared with the second hash value to determine whether they are consistent. If they are consistent, they are output; if they are inconsistent, a check signal is sent to the server.

10. A multi-level platform monitoring system, characterized in that: For implementing the multi-level platform monitoring method according to any one of claims 1 to 8, the system comprises: The module for generating data to be transmitted is used for the server to receive basic monitoring data collected from the collection terminals of each level platform, and to pre-process the basic monitoring data to generate data to be transmitted; A transmission optimization module, used for optimizing the transmission of data to be transmitted, wherein the transmission optimization method adopts a dynamic compression strategy; The analysis and warning module is used to analyze the monitoring data that has been transmitted, make decisions and warnings based on the analysis results, and perform integrity verification before outputting the warning results.

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