A remote monitoring method and system for operating status of electromechanical equipment

Abstract

The present invention discloses a remote monitoring method and system for the running state of electromechanical equipment, which relates to the field of automation control technology. The method comprises S1, acquiring real-time running data of electromechanical equipment and transmitting the data to a monitoring server; S2, preprocessing the real-time running data and extracting key running parameters; S3, performing distributed processing on the key running parameters based on cloud computing technology to ensure the response speed of the system under high concurrent access conditions; S4, comparing the processed running parameters with preset thresholds to generate alarm information; S5, pushing the alarm information to terminal user devices to achieve real-time monitoring and early warning. The remote monitoring method and system for the running state of electromechanical equipment solve the problem that when load balancing faces sudden high concurrent traffic, bottlenecks still occur due to too many requests, resulting in a decrease in response speed, and when users request to frequently refresh real-time data, the server still needs to bear greater pressure.

Description

Technical Field

[0001] The present application relates to the field of automation control technology, and in particular to a remote monitoring method and system for the operating status of electromechanical equipment. Background Art

[0002] In current network applications, as the number of users continues to grow, the system usually needs to handle high-concurrency access to ensure that users can quickly and stably obtain services. However, in the case of high-concurrency access, the system server often has a long response time due to excessive load, and even service unavailability may occur. The existing server architecture mainly improves processing power by increasing hardware resources, but this method cannot effectively solve the problem of slow system response speed, especially when a large number of requests appear in a short period of time.

[0003] At the same time, in order to cope with high-concurrency scenarios, many systems use technologies such as load balancing and caching mechanisms to optimize performance. Load balancing technology distributes requests to multiple servers to reduce the pressure on a single server; the caching mechanism reduces the number of server requests by caching static content. However, when faced with sudden high-concurrency traffic, load balancing still encounters bottlenecks due to too many requests, resulting in a decrease in response speed. The caching mechanism has limited effect on dynamic data. When users request to frequently refresh real-time data, the server still needs to bear greater pressure. Summary of the invention

[0004] The purpose of the present invention is to provide a remote monitoring method and system for the operating status of electromechanical equipment, so as to solve the problem that when load balancing faces sudden high concurrent traffic, there is still a bottleneck due to too many requests, resulting in a decrease in response speed. The cache mechanism has limited effect on dynamic data. When users request to frequently refresh real-time data, the server still needs to bear a lot of pressure.

[0005] To achieve the above object, the present invention provides the following technical solution: a remote monitoring method for the operating status of electromechanical equipment, the method comprising:

[0006] S1, obtain the real-time operation data of electromechanical equipment and transmit it to the monitoring server;

[0007] S2. Preprocess the real-time operation data and extract key operation parameters;

[0008] S3. Distribute key operating parameters based on cloud computing technology to ensure the system's response speed under high concurrent access. Specifically, a large number of key operating parameters are divided into multiple partitions and distributed to different computing nodes. The data of each node is trend-fitted according to time, the attenuation or growth trend of the data is processed, and the analysis results of different nodes are merged. The specific formula is:

[0009] ;

[0010] in, Indicates time The remaining key data, represents the initial data value, represents the data change rate constant, Indicates time;

[0011] S4. Compare the processed operating parameters with the preset threshold value and generate alarm information. Specifically, the signal discrimination is calculated for the processed data to determine whether the current signal is a real abnormal signal. The alarm trigger threshold is set according to the calculation result of the signal discrimination. When the signal discrimination value exceeds the set alarm threshold, an alarm information is generated. The calculation formula of the signal discrimination is:

[0012] ;

[0013] in, represents the discriminability of the signal, represents the signal mean, represents the noise mean, represents the standard deviation of signal and noise;

[0014] S5. Push the alarm information to the end-user device to achieve real-time monitoring and early warning.

[0015] Preferably, the S1 comprises:

[0016] Get the real-time data of the equipment, perform spectrum analysis on the vibration signal, decompose different frequency components to determine the operating status of the equipment, and transmit the key data after spectrum decomposition to the monitoring server. The specific formula for spectrum analysis of vibration signals is:

[0017] ;

[0018] in, represents the frequency domain signal, represents the time domain signal value, represents the total number of sampling points, represents the imaginary unit, and Indicates the index number.

[0019] Preferably, S2 includes:

[0020] Set temperature and vibration frequency as two variables to evaluate the impact of temperature and vibration frequency on the overall equipment status. The specific evaluation formula is:

[0021] , ;

[0022] in, and Represents the two related operating parameters of temperature and vibration frequency. is the influence coefficient.

[0023] Preferably, S5 includes:

[0024] Add the alarm information to the message queue, sort it by priority, control the push interval according to the current number of alarms, and push the generated alarms to the user's terminal device. The specific formula for controlling the push interval is:

[0025] ;

[0026] in, Indicates the push interval time. Indicates the minimum push interval. Indicates the push frequency control coefficient, Indicates the current number of alarm messages. Indicates the limit threshold for the number of alerts.

[0027] Preferably, S3 further includes:

[0028] Split the operating parameters;

[0029] Calculate the amount of data D for each segment;

[0030] When D>M, the partitioning is further refined to reduce the load on a single computing node, where D represents the amount of data and M represents the maximum allowed amount of data.

[0031] Preferably, S3 further includes:

[0032] Set a load factor K for each computing node;

[0033] The actual load of each computing node is calculated according to the formula L=D×K, where D represents the amount of data and K represents the load factor;

[0034] When L>N, an alarm is triggered and the standby node is started to assist in processing tasks, where L represents the actual load and N represents the load threshold.

[0035] Preferably, S3 further includes:

[0036] Monitor the processing time T of each computing node in real time;

[0037] The processing efficiency is calculated according to the formula Q=T / C, where T represents the processing time and C represents the standard processing time;

[0038] When Q < S, reduce the processing priority of the node, where Q represents the processing efficiency and S represents the efficiency threshold.

[0039] Preferably, the S3 further includes:

[0040] Statistically count the task completion status P of each computing node by time period;

[0041] Calculate the task completion rate according to the formula A = P / T, where P represents the number of completed tasks and T represents the total number of tasks;

[0042] When A < F, request manual intervention, where A represents the task completion rate and F represents the task completion rate threshold.

[0043] Preferably, the S3 further includes:

[0044] Each computing node records its own error log;

[0045] Calculate the error rate according to the formula B = E / H, where E represents the number of errors and H represents the total number of tasks;

[0046] When B > G, repair the node failure, where B represents the error rate and G represents the error rate threshold.

[0047] A remote monitoring system for the operating state of electromechanical equipment, adopting the remote monitoring method for the operating state of electromechanical equipment, the system includes:

[0048] A data acquisition module, configured to acquire the real-time operating data of the electromechanical equipment and transmit it to the monitoring server;

[0049] A data preprocessing module, configured to preprocess the real-time operating data and extract key operating parameters;

[0050] A distributed processing module, which performs distributed processing on the key operating parameters based on cloud computing technology to ensure the response speed of the system under high-concurrency access;

[0051] A comparison module, configured to compare the processed operating parameters with a preset threshold and generate an alarm message;

[0052] A push module, configured to push the alarm message to the terminal user device to achieve real-time monitoring and early warning.

[0053] As can be seen from the above technical solutions, the present invention has the following beneficial effects:

[0054] The remote monitoring method and system of the operation status of electromechanical equipment obtains the real-time operation data of the electromechanical equipment and transmits it to the monitoring server, pre-processes the real-time operation data, extracts key operation parameters, and performs distributed processing on the key operation parameters based on cloud computing technology to ensure the response speed of the system under high concurrent access conditions, compares the processed operation parameters with the preset threshold, generates alarm information, and pushes the alarm information to the terminal user device to achieve real-time monitoring and early warning, realizes dynamic regulation of request processing, reduces the problem of extended response time caused by excessive load of the system, can maintain the stability of system services, avoid service unavailability caused by bottlenecks, achieves higher operation efficiency, reduces the cost of hardware expansion, improves the dynamic data processing strategy, greatly reduces the server load, and ensures the efficient update and accurate return of real-time data, solving the problem that load balancing still has bottlenecks due to too many requests when facing sudden high concurrent traffic, resulting in a decrease in response speed. The cache mechanism has limited effect on dynamic data. When users request to refresh real-time data frequently, the server still needs to bear greater pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application.

[0056] Figure 1 is a flow chart of the method of the present invention;

[0057] Figure 2 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0058] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0059] like Figure 1 As shown, the present invention provides a technical solution: a remote monitoring method for the operating status of electromechanical equipment, the method comprising:

[0060] S1, obtain the real-time operation data of electromechanical equipment and transmit it to the monitoring server;

[0061] S2. Preprocess the real-time operation data and extract key operation parameters;

[0062] S3. Distribute key operating parameters based on cloud computing technology to ensure the system's response speed under high concurrent access. Specifically, a large number of key operating parameters are divided into multiple partitions and distributed to different computing nodes. The data of each node is trend-fitted according to time, the attenuation or growth trend of the data is processed, and the analysis results of different nodes are merged. The specific formula is:

[0063] ;

[0064] in, Indicates time The remaining key data, represents the initial data value, represents the data change rate constant, Indicates time;

[0065] S4. Compare the processed operating parameters with the preset threshold value and generate alarm information. Specifically, the signal discrimination is calculated for the processed data to determine whether the current signal is a real abnormal signal. The alarm trigger threshold is set according to the calculation result of the signal discrimination. When the signal discrimination value exceeds the set alarm threshold, an alarm information is generated. The calculation formula of the signal discrimination is:

[0066] ;

[0067] in, represents the discriminability of the signal, represents the signal mean, represents the noise mean, represents the standard deviation of signal and noise;

[0068] S5. Push the alarm information to the end-user device to achieve real-time monitoring and early warning.

[0069] In the above implementation, the operation data of electromechanical equipment is acquired in real time and transmitted to the monitoring server to ensure the timeliness of information transmission. The key operation parameters are extracted by using data preprocessing technology to reduce the complexity of the original data and enhance the analysis efficiency. The distributed processing solution based on cloud computing technology divides and distributes a large number of key operation parameters to multiple computing nodes, analyzes the change trend of the data through the trend fitting method, and calculates the change trend through the formula. The attenuation of key parameter data is indicated to ensure the system response speed under high concurrency. At the same time, the processed data is compared with the preset threshold and the signal discrimination is calculated. Accurately judge abnormal signals, detect potential abnormal conditions, and generate corresponding alarm information. Finally, push the alarm to the end user device to achieve remote real-time monitoring and efficient early warning. This method effectively solves the delay problem of data processing under high concurrent access through distributed processing of cloud computing technology and improves the response speed of the system. Using trend fitting model and data decay formula , which realizes the accurate monitoring and status prediction of the operation status of electromechanical equipment. At the same time, the signal discrimination formula The introduction of significantly enhances the accuracy of abnormal signal recognition, reduces the false alarm rate, and ensures the credibility of generated alarms. By pushing alarms to terminal devices, the function of remote real-time monitoring is realized, providing users with a convenient operation status management tool.

[0070] S1 includes obtaining real-time data of the equipment, performing spectrum analysis on the vibration signal, decomposing different frequency components to determine the operating status of the equipment, and transmitting the key data after spectrum decomposition to the monitoring server. The specific formula for spectrum analysis of the vibration signal is:

[0071] ;

[0072] in, represents the frequency domain signal, represents the time domain signal value, represents the total number of sampling points, represents the imaginary unit, and Indicates the index number.

[0073] In this implementation, the vibration signal generated during the operation of the equipment is first collected, and spectrum analysis is performed using digital signal processing technology to convert the time domain signal into a frequency domain signal. Based on discrete Fourier transform, the signal can be decomposed into different frequency components, thereby reflecting the specific vibration mode under the operation state of the equipment. By extracting specific frequency components and their corresponding amplitudes and phases, it is possible to identify whether the equipment is in an abnormal operation state. Subsequently, the key spectrum data obtained from the analysis is packaged and transmitted to the monitoring server for further processing and judgment. This method can convert complex time domain data into frequency domain data through spectrum analysis of vibration signals, making the monitoring of equipment operation status more intuitive and accurate. Discrete Fourier transform formula It provides an efficient data processing method, which can accurately decompose the different frequency components in the signal, effectively improving the sensitivity and accuracy of equipment operation status monitoring. By extracting key spectrum information and transmitting it to the monitoring server, it simplifies the complexity of subsequent data analysis and improves the efficiency of the remote monitoring system.

[0074] S2 includes setting temperature and vibration frequency as two variables to evaluate the impact of temperature and vibration frequency on the overall equipment status. The specific evaluation formula is:

[0075] , ;

[0076] in, and Represents the two related operating parameters of temperature and vibration frequency. is the influence coefficient.

[0077] In this embodiment, by taking temperature and vibration frequency as two main variables, a set of coupled differential equations are used to evaluate their dynamic effects on the operating state of the electromechanical device. Wherein, the formula:

[0078] , ; describes the interaction between temperature and vibration frequency;

[0079] The term ax in the first equation represents the natural tendency of temperature x to increase in the system, while the term bxy reflects the inhibitory effect of vibration frequency y on the temperature rise;

[0080] The −cy term in the second equation represents the natural attenuation trend of the vibration frequency y, while the dxy term indicates the effect of temperature x on the enhancement of the vibration frequency. In this way, the coupled changes of temperature and vibration frequency can be dynamically simulated, and their potential impact on the overall operating state of the equipment can be further analyzed. This model helps to accurately capture the nonlinear state changes that may occur in the equipment during operation. By setting temperature and vibration frequency as interrelated variables and introducing coupled differential equations, this method can accurately evaluate their dynamic impact on the equipment state. Compared with the traditional independent variable analysis method, the model proposed in the present invention can better reflect the nonlinear coupling effect within the system and help predict the abnormal state of the equipment in advance. The coefficients in the differential equation It can be calibrated according to the actual equipment characteristics to improve the accuracy of the analysis. This model not only enhances the monitoring capability of complex operating conditions, but also provides a reliable theoretical basis for subsequent equipment operation optimization.

[0081] S5 includes adding the alarm information to the message queue, sorting it according to the priority, controlling the push interval according to the current number of alarms, and pushing the generated alarm to the user terminal device. The specific formula for controlling the push interval is:

[0082] ;

[0083] in, Indicates the push interval time. Indicates the minimum push interval. Indicates the push frequency control coefficient, Indicates the current number of alarm messages. Indicates the limit threshold for the number of alerts.

[0084] In this implementation, alarm information is added to the message queue in the order of generation time, and sorted according to preset priorities (such as severity, impact range, etc.) to ensure that critical alarms can be pushed to users first. To avoid overloading of terminal devices due to a large number of alarms being pushed at the same time, the system dynamically adjusts the push interval time. To control the push frequency. Exceeding the limit threshold Push interval time If the number of alerts is small, the push interval will be kept short to quickly respond to user needs. This dynamic adjustment mechanism is based on the formula: Ensure that the push interval will not be lower than the minimum value allowed by the system. Through this mechanism, dynamic optimization and balance of alarm push is achieved. Through message queue management and dynamic push interval adjustment, this method can effectively alleviate the impact of high-frequency alarms on terminal devices and improve the stability of the system and user experience. The dynamic adjustment mechanism of push interval ensures the responsiveness of the system when the number of alarms surges, and reduces the pressure on network bandwidth and user devices. At the same time, the priority sorting mechanism ensures the timeliness of important alarms and significantly enhances the efficiency and pertinence of alarm push. Through the formulaic control method, the adjustment of push frequency is more accurate and efficient, meeting the diverse alarm push requirements in complex operating environments.

[0085] S3 also includes dividing the operating parameters; calculating the data volume D of each divided part; when D>M, further refining the division to reduce the load of a single computing node, where D represents the data volume and M represents the maximum allowed data volume.

[0086] In this embodiment, in order to improve the efficiency of distributed computing, the operating parameter data is first divided into multiple parts and distributed to different computing nodes. The data volume D of each segmented part is monitored by real-time computing. If the data volume D of a segmented part exceeds the maximum allowed data volume M, the system will further refine and segment the data. The process of refinement and segmentation aims to reduce the computing load of a single computing node, thereby balancing the resource usage of the entire distributed system. By dynamically adjusting the granularity of data segmentation, it is ensured that each computing node can efficiently process data within its computing capacity. This process not only optimizes the parallel computing performance of the system, but also reduces the risk of response delay or computing failure caused by overload operation of a single node. This method solves the problem of single node overload in a distributed computing system by dynamically segmenting and real-time monitoring of operating parameters. The granularity optimization mechanism in the segmentation process makes the system more flexible and efficient when facing large-scale data processing. The limiting mechanism of the data volume D of each segmented part ensures the reasonable allocation of computing tasks, avoids computing bottlenecks caused by excessive load, and improves the stability and computing efficiency of the overall system. Further refining the partitioning strategy enhances the fault tolerance of the distributed computing system and reduces the imbalance in resource utilization caused by differences in computing node performance.

[0087] S3 also includes setting a load factor K for each computing node; calculating the actual load of each computing node according to the formula L=D×K, where D represents the amount of data and K represents the load factor; when L>N, triggering an alarm and starting a backup node to assist in processing tasks, where L represents the actual load and N represents the load threshold.

[0088] In this embodiment, by setting a load factor K for each computing node and comprehensively considering factors such as the performance, processing capacity, and current state of the node, the actual load of each node in the distributed system is accurately evaluated. The calculation formula: L = D × K combines the amount of data D allocated to a certain node with its load factor K to obtain the actual load L of the node. If the actual load L of a certain node exceeds the preset load threshold N, the system will trigger an alarm and dynamically start a standby node to share the computing tasks of the overloaded node. The startup mechanism of the standby node reduces the pressure on the overloaded node by quickly transferring part of the data from the overloaded node to the standby node for processing, ensuring the stable operation of the system. In addition, the setting of the load factor K can be dynamically adjusted according to the hardware configuration of the node (such as CPU, memory), the current task volume, and the system resource usage to achieve more flexible load management. By setting the load factor K and calculating the actual load L of each node, this method significantly improves the task allocation accuracy of the distributed computing system and avoids the situation of uneven load among nodes. The load monitoring and alarm mechanism ensures the real-time response ability of the system under high load conditions and reduces the calculation failures or performance degradation caused by overloaded operation of nodes. At the same time, the dynamic startup strategy of the standby node enhances the fault tolerance and task processing efficiency of the system, effectively improving the reliability of the overall system. The setting of the load threshold N further optimizes the resource utilization rate of the node and ensures the adjustment of the calculation task allocation within a reasonable range. With the above improvements, this method can significantly improve the performance and stability of distributed computing, especially suitable for large-scale data processing scenarios.

[0089] S3 also includes real-time monitoring of the processing time T of each computing node; calculating the processing efficiency according to the formula Q = T / C, where T represents the processing time and C represents the standard processing time; when Q < S, reducing the processing priority of the node, where Q represents the processing efficiency and S represents the efficiency threshold.

[0090] In this embodiment, by monitoring the real-time processing time T of each computing node and comparing it with the standard processing time C, the processing efficiency Q of the computing node is calculated. The formula: Q = T / C indicates that the processing efficiency Q is determined by the ratio of the actual time T taken by the node to complete the task to the standard time C. When the processing efficiency Q of the computing node is lower than the preset efficiency threshold S, the system will dynamically reduce the processing priority of this node and reduce the amount of task allocation, thereby preventing low-efficiency nodes from dragging down the overall system performance. In this way, the operating state of low-performance nodes can be effectively identified and adjusted, and more computing resources can be allocated to high-efficiency nodes. At the same time, the system will update the processing efficiency of the nodes in real time to ensure the dynamics and accuracy of the priority adjustment, so as to maintain the efficient operation of the entire distributed system. This method optimizes the resource utilization rate and overall performance of the distributed system by calculating the processing efficiency Q of the computing node in real time and dynamically adjusting the priority allocation. The setting of the efficiency threshold S ensures that the task load of low-efficiency nodes can be reduced in time, thus avoiding waste of system resources. Compared with the traditional fixed task allocation mechanism, this dynamic priority adjustment strategy significantly improves the adaptability of the system to node performance fluctuations. In addition, by preferentially supporting high-efficiency nodes, this method can quickly complete key tasks, improve the system response speed and processing ability, and at the same time reduce the risk of performance degradation caused by task backlog for nodes. This mechanism maximally balances the system load while ensuring the operating efficiency of the computing node, and is applicable to large-scale data processing and complex computing environments.

[0091] S3 also includes statistically counting the task completion situation P of each computing node by time period; calculating the task completion rate according to the formula A = P / T, where P represents the number of completed tasks and T represents the total number of tasks; when A < F, request manual intervention, where A represents the task completion rate and F represents the task completion rate threshold.

[0092] In this embodiment, the task completion rate A is calculated by counting the number of tasks PPP and the total number of tasks T completed by each computing node within a specified time period. The formula A=P / T is used to quantify the task execution efficiency of the node within the time period. When the task completion rate A of the node is lower than the set completion rate threshold F, the system will automatically request manual intervention. The specific operations of manual intervention may include: checking the hardware status of the node, optimizing the allocation strategy, adjusting the node priority or suspending the node operation, etc. By monitoring the task completion rate, the system can effectively identify the abnormal state of the computing node and quickly take response measures to avoid affecting the normal operation of the entire distributed system due to the unfinished node task. This method can quickly discover abnormal node operation problems by counting the task completion status of each computing node and calculating the task completion rate A in real time. The setting of the completion rate threshold F provides a clear judgment standard for the system, so that inefficient or faulty nodes can be identified in time and request manual intervention. Compared with a fully automated system, adding a manual intervention mechanism can further ensure the accuracy and flexibility of task processing, especially in complex or sudden situations. In addition, by counting the completion of node tasks within a time period, this method can evaluate the operating efficiency and reliability of nodes with quantitative indicators, providing data support for system optimization and maintenance. Overall, this method improves the controllability and stability of the distributed system, while reducing the risk of task delays caused by node anomalies.

[0093] S3 also includes each computing node recording its own error log; calculating the error rate according to the formula B=E / H, where E represents the number of errors and H represents the total number of tasks; repairing the node failure when B>G, where B represents the error rate and G represents the error rate threshold.

[0094] In this embodiment, each computing node records an error log in real time during operation, including error events and related information that occur during task execution, such as error type, occurrence time, and impact range. By counting the number of errors E and the total number of tasks H processed by the node, the system calculates the error rate B of the node, and the formula is as follows: B=E / H When the error rate B of the node exceeds the set error rate threshold G, the system automatically triggers the node repair process. The repair process may include operations such as restarting the node, clearing the cache, updating the software, or reallocating tasks to quickly restore the normal operation of the node. By recording and analyzing the node error log, the method can effectively locate and quantify the problems in the node operation, and take timely repair measures when the error rate is too high to avoid further expansion of the problem or affect other nodes. The method realizes dynamic monitoring of the node operation status and quantitative evaluation of problems by recording the node error log in real time and calculating the error rate B. By setting the error rate threshold G, the potential faults of the node can be quickly discovered and the repair measures can be automatically started, thereby reducing the system downtime and improving the reliability of distributed computing. The recording of the error log provides detailed data support for subsequent problem troubleshooting and system optimization, making fault handling more accurate and efficient. In addition, by dynamically adjusting the error rate threshold G, the system can flexibly respond to tasks of different complexity and environmental changes according to actual operating conditions, further enhancing the adaptability and stability of the system.

[0095] A remote monitoring system for the operating status of electromechanical equipment is also provided. The remote monitoring method for the operating status of electromechanical equipment is adopted. The system comprises:

[0096] A data acquisition module is used to obtain real-time operating data of electromechanical equipment and transmit it to a monitoring server;

[0097] Data preprocessing module, used to preprocess real-time operation data and extract key operation parameters;

[0098] Distributed processing module, which performs distributed processing of key operating parameters based on cloud computing technology to ensure the system's response speed under high concurrent access conditions;

[0099] A comparison module, used to compare the processed operating parameters with the preset thresholds and generate alarm information;

[0100] The push module is used to push alarm information to end-user devices to achieve real-time monitoring and early warning.

[0101] In this embodiment, the system obtains the operating data of the electromechanical equipment in real time through the data acquisition module, and uses the data preprocessing module to process the data, such as removing abnormal data points, unifying the data format, extracting key parameters, etc. The preprocessed data is passed to the distributed processing module, which uses cloud computing technology to perform distributed processing on the data, dynamically adjusts the task allocation strategy, ensures efficient processing of a large number of concurrent tasks, and monitors and manages the load of the processing node in real time. The processed data is passed to the comparison module, which analyzes the data according to a preset threshold or model. For example, the vibration frequency, temperature and other parameters are combined to determine whether the equipment is operating normally, and generate corresponding alarm information. The push module pushes the generated alarm information to the user terminal device, so that the user can understand the equipment operation status in a timely manner and make necessary operations or decisions. The system provides an integrated solution through modular design combined with the specific method described. The data acquisition module works in conjunction with the preprocessing module, effectively improving the accuracy and processing efficiency of data acquisition; the introduction of the distributed processing module significantly enhances the system's responsiveness under high concurrency conditions; the comparison module combines signal discrimination and multi-level threshold analysis methods to greatly improve the accuracy of alarm generation; the push module supports multi-channel information push, ensuring that users can obtain alarm information in a timely manner and achieve real-time monitoring and early warning. Overall, the system is efficient, reliable, and flexible, and is suitable for electromechanical equipment monitoring scenarios in various complex environments.

[0102] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present disclosure. It should be understood that the above description is only the specific implementation method of the embodiments of the present disclosure and is not intended to limit the protection scope of the embodiments of the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the protection scope of the embodiments of the present disclosure.

Claims

1. A remote monitoring method for the operating status of electromechanical equipment, characterized in that: The method includes: S1. Obtain the real-time operation data of the electromechanical equipment and transmit it to the monitoring server; S2. Preprocess the real-time operation data and extract the key operation parameters; S3. Based on cloud computing technology, perform distributed processing on the key operation parameters to ensure the response speed of the system under high-concurrency access conditions. Specifically, divide a large number of key operation parameters into multiple partitions, distribute them to different computing nodes, perform trend fitting on the data of each node by time, process the attenuation or growth trend of the data, and merge the analysis results of different nodes. The specific formula is: ； in, Indicates time The remaining key data, represents the initial data value, represents the data change rate constant, Indicates time; S4. Compare the processed operation parameters with the preset threshold to generate alarm information. Specifically, calculate the signal discriminability of the processed data to determine whether the current signal is a real abnormal signal, set the alarm trigger threshold according to the calculation result of the signal discriminability, and generate alarm information when the signal discriminability value exceeds the set alarm threshold. The calculation formula of the signal discriminability is: ； in, represents the discriminability of the signal, represents the signal mean, represents the noise mean, represents the standard deviation of signal and noise; S5. Push the alarm information to the terminal user device to achieve real-time monitoring and early warning; The S1 includes: Obtain the real-time data of the device, perform spectrum analysis on the vibration signal, decompose different frequency components to determine the operation state of the device, and transmit the key data after spectrum decomposition to the monitoring server. The specific formula for performing spectrum analysis on the vibration signal is: ； in, represents the frequency domain signal, represents the time domain signal value, represents the total number of sampling points, represents the imaginary unit, and Indicates the index number; The S2 includes: Set temperature and vibration frequency as two variables, and evaluate the influence degree of temperature and vibration frequency on the overall device state. The specific evaluation formula is: ， ； in, and Represents the two related operating parameters of temperature and vibration frequency. is the influence coefficient.

2. A remote monitoring method for the operating status of electromechanical equipment according to claim 1, characterized in that: The S5 includes: Add the alarm information to the message queue, sort it according to the priority, control the push interval according to the current number of alarms, and push the generated alarms to the user terminal device. The specific formula for controlling the push interval is: ； in, Indicates the push interval time. Indicates the minimum push interval. Indicates the push frequency control coefficient, Indicates the current number of alarm messages. Indicates the limit threshold for the number of alerts.

3. A remote monitoring method for the operating status of electromechanical equipment according to claim 1, characterized in that: The S3 further includes: Divide the operation parameters; Calculate the data volume D of each divided part; When D > M, further refine the division to reduce the load of a single computing node, where D represents the data volume and M represents the maximum allowable data volume.

4. A remote monitoring method for the operating status of electromechanical equipment according to claim 3, characterized in that: The S3 further includes: Set a load coefficient K for each computing node; Calculate the actual load of each computing node according to the formula L = D × K, where D represents the data volume and K represents the load coefficient; When L > J, trigger an alarm and start a standby node to assist in processing tasks, where L represents the actual load and J represents the load threshold.

5. A remote monitoring method for the operating status of electromechanical equipment according to claim 4, characterized in that: The S3 further includes: Real-time monitor the processing time T of each computing node; Calculate the processing efficiency according to the formula Q = T / C, where T represents the processing time and C represents the standard processing time; When Q < S, reduce the processing priority of the node, where Q represents the processing efficiency and S represents the efficiency threshold.

6. A remote monitoring method for the operating status of electromechanical equipment according to claim 5, characterized in that: The S3 further includes: Count the task completion situation P of each computing node by time period; Calculate the task completion rate according to the formula A = P / O, where P represents the number of completed tasks and O represents the total number of tasks; When A < F, request manual intervention, where A represents the task completion rate and F represents the task completion rate threshold.

7. A remote monitoring method for the operating status of electromechanical equipment according to claim 6, characterized in that: The S3 further includes: Each computing node records its own error log; Calculate the error rate according to the formula B = E / H, where E represents the number of errors and H represents the total number of tasks; When B>G, the node failure is repaired, where B represents the error rate and G represents the error rate threshold.

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