An equipment operation and maintenance monitoring system based on the Internet of Things

Abstract

The present invention provides an equipment operation maintenance monitoring system based on the Internet of Things, which relates to the technical field of equipment monitoring; the present invention can ensure high-frequency collection and sufficient resource allocation of key parameters by dynamically dividing the weight levels of equipment operation parameters and setting an adaptive collection and processing mechanism, and divides other operation parameters around each security core parameter to reduce the collection frequency and resource allocation in a step-by-step manner, thereby effectively improving resource utilization efficiency, while ensuring stable monitoring of operation parameters and reducing losses caused by excessive or insufficient maintenance; secondly, by recording and encrypting operation parameter data through blockchain technology and implementing a step-by-step data encryption strategy, data security and integrity are ensured while saving resources, effectively preventing data leakage.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring, and in particular to an equipment operation maintenance monitoring system based on the Internet of Things. Background Art

[0002] In modern industrial production and daily life, equipment is an important infrastructure and production tool, and its operating status is directly related to production efficiency, product quality, and people's life and property safety. However, with the increasing complexity of equipment and the diversification of the use environment, equipment operation and maintenance monitoring faces many challenges. For example, the traditional equipment operation and maintenance monitoring method often adopts regular inspections and manual recording, which is not only inefficient but also easy to miss key information. At the same time, high-frequency collection and resource allocation for all operating parameters not only wastes a lot of resources, but also may cause difficulties in data processing and analysis due to the large amount of data. Secondly, in the process of equipment operation and maintenance monitoring, the data generated often contains sensitive information such as the equipment's operating status and fault history. Once these data are leaked or tampered with, they will pose a serious threat to production safety and privacy protection. Traditional data encryption methods often adopt a unified encryption strategy, which is difficult to meet the security requirements of data at different levels. Therefore, how to implement a step-by-step data encryption strategy to save resources while ensuring data security has become an important technical issue. In practical applications, excessive maintenance will waste a lot of resources and increase maintenance costs; while insufficient maintenance may lead to frequent equipment failures, affecting production efficiency and product quality. Therefore, how to accurately predict maintenance needs based on the monitoring results of equipment operating parameters and avoid the risks of over-maintenance and under-maintenance has become an important research direction.

[0003] Therefore, it is necessary to provide an equipment operation and maintenance monitoring system based on the Internet of Things to solve the above technical problems. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides an equipment operation and maintenance monitoring system based on the Internet of Things to solve the problems faced by equipment operation and maintenance monitoring in terms of resource utilization efficiency, data security and integrity, and the risks of over-maintenance and under-maintenance.

[0005] The present invention provides an equipment operation and maintenance monitoring system based on the Internet of Things, the monitoring system comprising:

[0006] The historical data acquisition module is used to use the Internet of Things technology to obtain the historical parameter data of the equipment and the corresponding equipment maintenance data, and pre-process them to obtain the historical data set;

[0007] The model building module is used to obtain the correlation between the equipment operation parameter changes and equipment maintenance based on the historical data set using data analysis methods, and to build a monitoring and early warning model based on this;

[0008] The weight division module is used to dynamically divide the weight levels of the equipment's operating parameters according to the results of the correlation between the equipment's operating parameter changes and equipment maintenance, and regularly divide the weight levels of the equipment's operating parameters according to the degree of impact of the fault on safety, to obtain operating parameters of high and low weight levels;

[0009] A data analysis module is used to identify the operating parameters of the advanced weight level as the safety core parameters, and obtain the correlation between the safety core parameters and other operating parameters through the data analysis method according to the historical data set;

[0010] A level classification module is used to perform a step-by-step level classification of other operating parameters associated with each security core parameter based on the correlation between the security core parameter and other operating parameters, with each security core parameter as the center and through a preset correlation threshold segment, to obtain the level of other operating parameters associated with each security core parameter;

[0011] The collection definition module is used to set an adaptive collection processing mechanism based on the tiered levels of other operating parameters associated with each safety core parameter;

[0012] The processing and encryption module is used to obtain the equipment's operating parameter inputs and the monitoring and early warning model output prediction results in real time based on the setting of an adaptive collection and processing mechanism. At the same time, it records and encrypts the operating parameter data through blockchain technology and implements step-by-step data encryption.

[0013] Preferably, the specific steps of using the Internet of Things technology to obtain the historical parameter data of the equipment and the corresponding equipment maintenance data, and preprocessing them to obtain the historical data set are:

[0014] Use IoT technology to obtain historical parameter data of equipment and corresponding equipment maintenance data, where historical parameter data includes but is not limited to temperature, pressure, vibration, and speed operating parameters, and corresponding equipment maintenance data includes maintenance records and fault history;

[0015] The historical parameter data of the acquired equipment and the corresponding equipment maintenance data are preprocessed, including cleaning and filling operations, and the preprocessed data are sorted into a structured historical data set in chronological order.

[0016] Preferably, the specific steps of using data analysis methods to obtain the correlation between equipment operation parameter changes and equipment maintenance based on historical data sets and constructing a monitoring and early warning model are:

[0017] Through correlation analysis or cluster analysis in data analysis methods, the corresponding relationship between the operating parameter changes and maintenance data in the historical data set is identified to obtain the correlation law between the operating parameter changes of the equipment and the equipment maintenance;

[0018] The correlation between the changes in equipment operating parameters and equipment maintenance is used as a training set, and a neural network model is selected for training to obtain a monitoring and early warning model.

[0019] Preferably, according to the result of the association law between the change of the equipment operating parameters and the equipment maintenance, the weight level of the equipment operating parameters is dynamically divided according to the impact of the fault on the safety on a regular basis, and the specific steps of obtaining the operating parameters of the high-level and low-level weight levels are:

[0020] Regularly evaluate the impact of each fault in the historical data set to obtain the evaluation results of the fault impact;

[0021] According to the evaluation results of the impact of the fault, two weight levels, high and low, are set;

[0022] According to the set standards of the two weight levels, high and low, and based on the correlation between the change of the equipment's operating parameters and equipment maintenance, the equipment's operating parameters are divided into weight levels to obtain operating parameters of high and low weight levels.

[0023] Preferably, the specific steps of identifying the operating parameters of the advanced weight level as the safety core parameters and obtaining the correlation between the safety core parameters and other operating parameters by a data analysis method according to the historical data set are:

[0024] From the operating parameters at the advanced weight level, a group is selected as the safety core parameters;

[0025] Parameter record data related to the safety core parameters are extracted from the historical data set, and the parameter record data are preprocessed. Then, the correlation between the safety core parameters and other operating parameters is calculated through data analysis methods.

[0026] Preferably, the correlation between the core security parameters and other operating parameters is centered on each core security parameter and is divided into ladder-type levels for other operating parameters associated with each core security parameter through a preset correlation threshold segment to obtain the ladder-type levels of other operating parameters associated with each core security parameter. The specific steps are:

[0027] Based on the correlation between the safety core parameter and other operating parameters, multiple correlation threshold sections are set;

[0028] Based on the set multiple correlation threshold segments, the correlation between the core safety parameters and other operating parameters is initially divided into different levels;

[0029] According to the preliminary classification results and combined with the monitoring requirements of the equipment, determine the classification level based on the correlation between the core safety parameters and other operating parameters.

[0030] Preferably, the setting of the adaptive acquisition and processing mechanism specifically includes a high acquisition frequency of the security core parameters and more data processing allocation resources, while the acquisition frequency and data processing allocation resources of other operating parameters corresponding to each security core parameter are downgraded according to the corresponding step level.

[0031] Preferably, the specific steps of setting up an adaptive acquisition and processing mechanism to obtain the equipment's operating parameters in real time and input the monitoring and early warning model to output the prediction results, and recording and encrypting the operating parameter data through blockchain technology to implement the step-by-step data encryption are:

[0032] According to the adaptive acquisition and processing mechanism, various operating parameters of the equipment are acquired in real time and preprocessed to obtain real-time operating parameter data;

[0033] Input the real-time operation parameter data into the monitoring and early warning model, and output the prediction results through analysis and calculation of the monitoring and early warning model;

[0034] Based on the hierarchical levels of other operating parameters associated with each core security parameter, corresponding encryption strategies are set through blockchain technology, including that the encryption key length is reduced as the hierarchical level is downgraded;

[0035] Each operating parameter data is encrypted accordingly through the encryption strategy.

[0036] Compared with the related art, the equipment operation maintenance monitoring system based on the Internet of Things provided by the present invention has the following beneficial effects:

[0037] The present invention can ensure high-frequency collection and sufficient resource allocation of key parameters by dynamically dividing the weight levels of equipment operating parameters and setting up an adaptive collection and processing mechanism. It divides other operating parameters around each core security parameter to reduce the collection frequency and resource allocation in a step-by-step manner, effectively improving resource utilization efficiency while ensuring stable monitoring of operating parameters and reducing losses caused by excessive or insufficient maintenance. Secondly, the blockchain technology is used to record and encrypt operating parameter data and implement a step-by-step data encryption strategy, which saves resources while ensuring data security and integrity, effectively preventing data leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1The system block diagram of an equipment operation, maintenance and monitoring system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0040] Example

[0041] like Figure 1 As shown, an equipment operation and maintenance monitoring system based on the Internet of Things specifically includes:

[0042] The historical data acquisition module is used to use the Internet of Things technology to obtain the historical parameter data of the equipment and the corresponding equipment maintenance data, and pre-process them to obtain the historical data set;

[0043] In the specific implementation process, the historical data acquisition module is used to:

[0044] Use IoT technology to obtain historical parameter data of equipment and corresponding equipment maintenance data, where historical parameter data includes but is not limited to temperature, pressure, vibration, and speed operating parameters, and corresponding equipment maintenance data includes maintenance records and fault history;

[0045] Specifically, through various sensors such as temperature sensors, pressure sensors, vibration sensors and speed sensors, various operating parameters of the equipment are monitored and recorded, and through the Internet of Things technology, various operating parameters of the equipment with historical monitoring records and corresponding equipment maintenance data log records are searched and obtained;

[0046] Perform preprocessing operations on the historical parameter data of the acquired equipment and the corresponding equipment maintenance data, including cleaning and filling processing operations, and organize the preprocessed data into a structured historical data set in chronological order;

[0047] Specifically, preprocessing operations are performed on the historical parameter data of the acquired equipment and the corresponding equipment maintenance data, including using data cleaning tools or writing scripts to remove duplications and process outliers on the historical parameter data and the corresponding equipment maintenance data, and using mean filling or interpolation to fill in the data according to the missing historical parameter data and the corresponding equipment maintenance data; and the preprocessed historical parameter data and the corresponding equipment maintenance data are sorted in chronological order and converted into a format suitable for analysis, specifically CSV or Excel format, and stored as a historical data set.

[0048] The model building module is used to obtain the correlation between the equipment operation parameter changes and equipment maintenance based on the historical data set using data analysis methods, and to build a monitoring and early warning model based on this;

[0049] In the specific implementation process, the model building module is used to:

[0050] Through correlation analysis or cluster analysis in data analysis methods, the corresponding relationship between the operating parameter changes and maintenance data in the historical data set is identified to obtain the correlation law between the operating parameter changes of the equipment and the equipment maintenance;

[0051] Specifically, in this embodiment, correlation analysis is selected to specifically identify which operating parameters in the equipment have significant linear or nonlinear relationships with maintenance data; and a cluster analysis method is used to classify similar operating parameters or maintenance data in the equipment into one category, and obtain the association law results between the changes in the operating parameters of the equipment and the equipment maintenance. For example, in wind power generation equipment, its operation and maintenance data set is obtained, which includes the temperature, vibration, speed and other operating parameters of the wind turbine and the corresponding maintenance records. The correlation between the operating parameters such as temperature, vibration, speed and maintenance data is calculated through the existing technology correlation analysis. If it is found through calculation that the correlation between temperature and maintenance data is the highest, it means that temperature is one of the key factors affecting the maintenance of wind turbines. Then, the temperature data is clustered according to different ranges, and the maintenance status of wind turbines in different temperature ranges is observed. Through cluster analysis, it is found that the failure rate of wind turbines in certain temperature ranges is higher;

[0052] The correlation between the equipment's operating parameter changes and equipment maintenance is used as a training set, and a neural network model is selected for training to obtain a monitoring and early warning model.

[0053] Specifically, in this embodiment, a recurrent neural network (RNN) is selected as the model framework, and the correlation between the change of equipment operating parameters and equipment maintenance is used as the training set. Specifically, the equipment operating parameters are arranged in chronological order and used as the input features of the RNN model. At the same time, the equipment maintenance is used as the output label of the model for supervised learning in the training process, and finally the monitoring and early warning model is obtained through training;

[0054] The weight division module is used to dynamically divide the weight levels of the equipment's operating parameters according to the results of the correlation between the equipment's operating parameter changes and equipment maintenance, and regularly divide the weight levels of the equipment's operating parameters according to the degree of impact of the fault on safety, to obtain operating parameters of high and low weight levels;

[0055] In the specific implementation process, the weight division module is used to:

[0056] Regularly evaluate the impact of each fault in the historical data set to obtain the evaluation results of the fault impact;

[0057] Specifically, the failure rate, downtime caused by failures and the impact of failures on production safety in historical data are regularly used as three evaluation criteria, specifically: record the total number of failures within a period of time, and divide the number of failures by the total operating time or total production batches to obtain the failure rate; each time a failure occurs, record the total downtime from the discovery of the failure to the repair of the failure, and add up the downtime of all failures, and then divide it by the number of failures to obtain the average downtime, that is, the downtime caused by the failure; according to the consequences of the failure, the failure is divided into different risk levels, including casualties as the third-level risk, equipment damage as the second-level risk, and environmental pollution as the first-level risk, where the higher the risk value, the greater the impact of the failure on production safety; finally, multiply the quantitative value of each evaluation criterion by the corresponding weight in a weighted summation manner, and then add up all the weighted values ​​to obtain the evaluation result of the impact of the failure;

[0058] According to the evaluation results of the impact of the fault, two weight levels, high and low, are set;

[0059] Specifically, according to the evaluation results of the fault impact, the faults are sorted from high to low according to the impact, and according to the sorting results, a weight threshold is set as a standard for dividing the two weight levels of high and low. It should be noted that the weight threshold is customized according to the needs;

[0060] According to the set standards of the high-level and low-level weight levels, and based on the correlation between the change of the equipment's operating parameters and equipment maintenance, the equipment's operating parameters are divided into weight levels to obtain the operating parameters of the high-level and low-level weight levels;

[0061] A data analysis module is used to identify the operating parameters of the advanced weight level as the safety core parameters, and obtain the correlation between the safety core parameters and other operating parameters through the data analysis method according to the historical data set;

[0062] In the specific implementation process, the data analysis module is used to:

[0063] From the operating parameters at the advanced weight level, a group is selected as the safety core parameters;

[0064] Extract parameter record data related to the safety core parameters from the historical data set, pre-process the parameter record data, and then calculate the correlation between the safety core parameters and other operating parameters through data analysis methods;

[0065] Specifically, in this embodiment, the Pearson correlation coefficient is selected as the correlation analysis method to calculate the correlation between the safety core parameters and other operating parameters. Through the calculation, a set of correlation values ​​is obtained for subsequent safety risk assessment and fault prediction;

[0066] A level classification module is used to perform a step-by-step level classification of other operating parameters associated with each security core parameter based on the correlation between the security core parameter and other operating parameters, with each security core parameter as the center and through a preset correlation threshold segment, to obtain the level of other operating parameters associated with each security core parameter;

[0067] In the specific implementation process, the level classification module is used to:

[0068] Based on the correlation between the safety core parameter and other operating parameters, multiple correlation threshold sections are set;

[0069] Based on the set multiple correlation threshold segments, the correlation between the core safety parameters and other operating parameters is initially divided into different levels;

[0070] Specifically, each correlation value of the correlation between the safety core parameter and other operating parameters is compared with the threshold segment to determine the ladder level to which it belongs, and for the correlation value falling within a certain threshold segment, it is divided into the corresponding ladder level;

[0071] According to the preliminary classification results and combined with the monitoring requirements of the equipment, determine the classification level of the correlation between the core safety parameters and other operating parameters;

[0072] Specifically, according to the preliminary classification results and the monitoring requirements of the equipment, the classification of the levels will be reviewed by experts in the equipment field, and the levels of correlation between the core safety parameters and other operating parameters will be finally determined;

[0073] The collection definition module is used to set an adaptive collection processing mechanism based on the tiered levels of other operating parameters associated with each safety core parameter;

[0074] In the specific implementation process, the adaptive collection and processing mechanism is specifically set as follows: the adaptive collection and processing mechanism is set, which specifically includes a high collection frequency of the security core parameters and more data processing allocation resources, and the collection frequency and data processing allocation resources of other operating parameters corresponding to each security core parameter are downgraded according to the corresponding ladder level;

[0075] Specifically, in this embodiment, the equipment is equipped with corresponding sensors and data acquisition equipment, and the initial acquisition frequency and data processing resource allocation are set. After the system is started, the equipment starts to collect data of the safety core parameters in real time and transmits it to the data center for storage and processing; as time goes by, the system gradually accumulates a large amount of operation data, and calculates the current ladder level of other operation parameters associated with each safety core parameter, and adjusts the acquisition frequency and data processing resource allocation of each operation parameter through deep learning algorithm training in this process. For example, for temperature parameters at a higher ladder level, the system increases its acquisition frequency and data processing resource allocation; and for pressure parameters at a lower ladder level, the acquisition frequency and data processing resource allocation are correspondingly reduced;

[0076] The processing and encryption module is used to obtain the equipment's operating parameters in real time based on the setting of an adaptive acquisition and processing mechanism, input the monitoring and early warning model to output the prediction results, and record and encrypt the operating parameter data through blockchain technology to implement step-by-step data encryption;

[0077] In the specific implementation process, the processing and encryption module is used to:

[0078] According to the adaptive acquisition and processing mechanism, various operating parameters of the equipment are acquired in real time and preprocessed to obtain real-time operating parameter data;

[0079] Input the real-time operation parameter data into the monitoring and early warning model, and output the prediction results through analysis and calculation of the monitoring and early warning model;

[0080] Based on the hierarchical levels of other operating parameters associated with each core security parameter, corresponding encryption strategies are set through blockchain technology, including that the encryption key length is reduced as the hierarchical level is downgraded;

[0081] Each operating parameter data is encrypted accordingly through encryption strategy;

[0082] Specifically, in this embodiment, a 256-bit key length is used for the operating parameters classified as the first level, and they are encrypted using the AES algorithm; a 128-bit key length is used for the operating parameters classified as the second level, and they are encrypted using the AES algorithm; and a 64-bit key length is used for the operating parameters classified as the third level, and they are encrypted using the DES algorithm.

[0083] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0085] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. An equipment operation and maintenance monitoring system based on the Internet of Things, characterized in that: The monitoring system comprises: The historical data acquisition module is used to use the Internet of Things technology to obtain the historical parameter data of the equipment and the corresponding equipment maintenance data, and pre-process them to obtain the historical data set; The model building module is used to obtain the correlation between the equipment operation parameter changes and equipment maintenance based on the historical data set using data analysis methods, and to build a monitoring and early warning model based on this; The specific steps of using data analysis methods to obtain the correlation between equipment operation parameter changes and equipment maintenance based on historical data sets and constructing a monitoring and early warning model are as follows: Through correlation analysis or cluster analysis in data analysis methods, the corresponding relationship between the operating parameter changes and maintenance data in the historical data set is identified to obtain the correlation law between the operating parameter changes of the equipment and the equipment maintenance; The correlation between the equipment's operating parameter changes and equipment maintenance is used as a training set, and a neural network model is selected for training to obtain a monitoring and early warning model. The weight division module is used to dynamically divide the weight levels of the equipment's operating parameters according to the results of the correlation between the equipment's operating parameter changes and equipment maintenance, and regularly divide the weight levels of the equipment's operating parameters according to the degree of impact of the fault on safety, to obtain operating parameters of high and low weight levels; According to the result of the correlation between the change of the equipment's operating parameters and the equipment maintenance, the weight levels of the equipment's operating parameters are dynamically divided regularly according to the degree of impact of the fault on safety, and the specific steps of obtaining the operating parameters of the high and low weight levels are as follows: Regularly evaluate the impact of each fault in the historical data set to obtain the evaluation results of the fault impact; According to the evaluation results of the impact of the fault, two weight levels, high and low, are set; According to the set standards of the high-level and low-level weight levels, and based on the correlation between the change of the equipment's operating parameters and equipment maintenance, the equipment's operating parameters are divided into weight levels to obtain the operating parameters of the high-level and low-level weight levels; A data analysis module is used to identify the operating parameters of the advanced weight level as the safety core parameters, and obtain the correlation between the safety core parameters and other operating parameters through the data analysis method according to the historical data set; The specific steps of identifying the operating parameters of the advanced weight level as the safety core parameters and obtaining the correlation between the safety core parameters and other operating parameters through the data analysis method according to the historical data set are: From the operating parameters at the advanced weight level, a group is selected as the safety core parameters; Extract parameter record data related to the safety core parameters from the historical data set, pre-process the parameter record data, and then calculate the correlation between the safety core parameters and other operating parameters through data analysis methods; A level classification module is used to perform a step-by-step level classification of other operating parameters associated with each security core parameter based on the correlation between the security core parameter and other operating parameters, with each security core parameter as the center and through a preset correlation threshold segment, to obtain the level of other operating parameters associated with each security core parameter; The collection definition module is used to set an adaptive collection processing mechanism based on the tiered levels of other operating parameters associated with each safety core parameter; The processing and encryption module is used to obtain the equipment's operating parameter inputs and the monitoring and early warning model output prediction results in real time based on the setting of an adaptive collection and processing mechanism. At the same time, it records and encrypts the operating parameter data through blockchain technology and implements step-by-step data encryption.

2. According to the equipment operation maintenance monitoring system based on the Internet of Things according to claim 1, it is characterized in that: The specific steps of using the Internet of Things technology to obtain the historical parameter data of the equipment and the corresponding equipment maintenance data, and preprocessing them to obtain the historical data set are as follows: Use IoT technology to obtain historical parameter data of equipment and corresponding equipment maintenance data, where historical parameter data includes but is not limited to temperature, pressure, vibration, and speed operating parameters, and corresponding equipment maintenance data includes maintenance records and fault history; The historical parameter data of the acquired equipment and the corresponding equipment maintenance data are preprocessed, including cleaning and filling operations, and the preprocessed data are sorted into a structured historical data set in chronological order.

3. According to the equipment operation maintenance monitoring system based on the Internet of Things according to claim 1, it is characterized in that: Based on the correlation between the security core parameters and other operating parameters, each security core parameter is taken as the center, and other operating parameters associated with each security core parameter are divided into a ladder-like level through a preset correlation threshold section to obtain the ladder level of other operating parameters associated with each security core parameter. The specific steps are: Based on the correlation between the safety core parameter and other operating parameters, multiple correlation threshold sections are set; Based on the set multiple correlation threshold segments, the correlation between the core safety parameters and other operating parameters is initially divided into different levels; According to the preliminary classification results and combined with the monitoring requirements of the equipment, determine the classification level based on the correlation between the core safety parameters and other operating parameters.

4. The equipment operation maintenance monitoring system based on the Internet of Things according to claim 1 is characterized in that: The setting of the adaptive collection and processing mechanism specifically includes a high collection frequency of the security core parameters and a large number of data processing allocation resources, while the collection frequency and data processing allocation resources of other operating parameters corresponding to each security core parameter are downgraded according to the corresponding step level.

5. The equipment operation maintenance monitoring system based on the Internet of Things according to claim 1 is characterized in that: The specific steps of setting up an adaptive acquisition and processing mechanism to obtain the equipment's operating parameters in real time and input the monitoring and early warning model to output the prediction results, and at the same time record and encrypt the operating parameter data through blockchain technology to implement the step-by-step data encryption are as follows: According to the adaptive acquisition and processing mechanism, various operating parameters of the equipment are acquired in real time and preprocessed to obtain real-time operating parameter data; Input the real-time operation parameter data into the monitoring and early warning model, and output the prediction results through analysis and calculation of the monitoring and early warning model; Based on the hierarchical levels of other operating parameters associated with each security core parameter, corresponding encryption strategies are set through blockchain technology, including that the encryption key length is reduced as the hierarchical level is downgraded; Each operating parameter data is encrypted accordingly through the encryption strategy.

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