A data adaptive correction and collection method and device for dynamic link monitoring

Abstract

The application discloses a kind of data adaptive correction and collection method and device for dynamic ring monitoring, comprising: configuration key data item test point, the priority of test point is classified and set update level and weight, the polling resource is allocated to each level test point and the dynamic ring monitoring data of each data item is collected;Task is created in dynamic ring monitoring system, field measurement is carried out, the difference of dynamic ring monitoring data and actual collection data is calculated and difference mode is analyzed;According to difference mode, dynamic ring monitoring system parameters are dynamically adjusted by adaptive correction algorithm, and corrected data is obtained;Selective polling strategy is used, polling priority weight and polling frequency are dynamically adjusted, and polling resource is updated and allocated;Difference data prediction model is constructed to predict future difference, to judge whether it exceeds preset safety threshold to carry out error correction;The application can identify difference mode in real time, respond to environmental and equipment state changes, improve monitoring data accuracy and system response timeliness.

Description

Technical Field

[0001] This invention relates to the field of data monitoring and calibration technology, and more specifically to a method and apparatus for adaptive correction and acquisition of data for environmental monitoring. Background Technology

[0002] Currently, environmental monitoring systems are widely used in scenarios such as data centers, industrial equipment management, and computer room monitoring to monitor environmental and equipment status data such as temperature, humidity, voltage, current, pressure, and gas concentration in real time, ensuring equipment safety and environmental stability.

[0003] However, in actual operation, environmental monitoring systems are often affected by factors such as sensor accuracy, equipment aging, and environmental interference, resulting in a large deviation between the collected data and the actual data. This affects the accuracy of the monitoring system and may lead to incorrect judgments about equipment status or environmental conditions, increasing the risk of failure and adding extra burden to operation and maintenance management.

[0004] Furthermore, the timeliness of data transmission and updates is an important performance indicator for environmental monitoring systems. Due to limitations such as device polling rate, serial port rate, and network transmission latency, the system often experiences a certain lag when acquiring and reporting field data. Especially in scenarios involving monitoring alarm data, delays may prevent the system from responding to changes in device status in a timely manner, prolonging the response time to abnormal events and posing risks to the normal operation of the system. Problems such as excessively low polling speed and communication interruptions can further exacerbate data lag, making it difficult for the monitoring system to meet the requirements of high real-time performance.

[0005] Therefore, in the face of the complex and ever-changing operating environment of environmental monitoring systems, how to correct data deviations and improve the timeliness of updates is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of the above problems, the present invention proposes a data adaptive correction and acquisition method and apparatus for environmental monitoring that overcomes or at least partially solves the above problems.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: An adaptive data correction and acquisition method for environmental monitoring includes the following steps: S1. Configure test points for key data items, classify the priority of test points and set update levels and corresponding weights, allocate polling resources for each level of test points and collect environmental monitoring data for each data item. S2. Create a task in the environmental monitoring system. The maintenance personnel will perform on-site measurements based on the task data, calculate the difference between the environmental monitoring data and the actual collected data for each data item, and analyze the difference pattern. S3. Based on the data difference pattern, the parameters of the environmental monitoring system are dynamically adjusted using an adaptive correction algorithm to obtain corrected monitoring data; S4. Adopt a selective polling strategy to dynamically adjust the polling priority weight and polling frequency of each data item, and update and allocate polling resources; S5. By analyzing the changing trends of historical data and current difference data, and through continuous polling and difference analysis, a difference data prediction model is constructed to predict future differences and determine whether they exceed a preset safety threshold for error correction.

[0008] Preferably, in step S1, the polling resources allocated to each level of test point are as follows:

[0009] in, To update the level Resource allocation For the system's overall network and transmission resources, , , , The update levels are listed from highest to lowest. , , Set the weights for each update level.

[0010] Preferably, in step S2, the difference patterns include: fixed difference pattern, random difference pattern, and sudden anomaly pattern; The difference value under the fixed difference mode is: ,

[0011] The difference value under the random difference mode is: ,

[0012] The difference values ​​under the sudden abnormal mode are: ,

[0013] in, This represents the difference between a specific data item recorded in the environmental monitoring system and the actual data collected. This refers to the data monitored by the environmental monitoring system for a specific priority data item. This is data actually collected by the device. The amount of data collected, This is the preset deviation threshold.

[0014] Preferably, step S3 includes the following: S31. Define the state vectors of various indicator parameters of each monitoring device in the environmental monitoring system; S32. Construct the state transition equations of the equipment and the observation equations that describe the relationship between the monitoring data and the actual collected data; S33. Based on the Kalman filter principle and the state transition equation, predict the next state; S34. Update the covariance matrix of the actual collected data and determine the weight of the monitoring data correction by calculating the Kalman gain; S35. The final corrected data is obtained by combining the predicted status and the data monitored by the monitoring system.

[0015] Preferably, in step S31, the state vector of each indicator parameter of the monitoring device is:

[0016] in, For at any time The first manually collected The first of the equipment Item function data; Step S32, the state transition equation of the device is:

[0017] in, Represents the current state vector State at the next moment The state transition matrix, Indicates control input The control matrix that affects the state. The random error representing the state evolution process follows a Gaussian distribution; , The calculation is as follows:

[0018] in, For the state vector The mean is set as the difference value at the previous time step. , For variance, Indicates the scaling factor. It is a positive semi-definite matrix; The observation equation is:

[0019] in, Indicates at time The environmental monitoring system monitors the first The first of the equipment Item function data, Represents the observation matrix. Indicates observation noise; Step S33, the predicted next state is:

[0020] The method for updating the covariance matrix is ​​as follows:

[0021] in, Indicates state time The covariance matrix, Let be the covariance matrix of the process noise; Weight of monitoring data correction for:

[0022] The final corrected data obtained for:

[0023] in, To predict the state, The data monitored by the monitoring system.

[0024] Preferably, in step S4, the selective polling strategy is as follows: the polling priority weight of each data item is dynamically adjusted based on the difference value and real-time importance, and the polling frequency is dynamically adjusted. For sudden anomalies and fault points, polling is temporarily skipped. The polling frequency is:

[0025]

[0026] in, For dynamically adjusted polling frequency, Indicates the basic polling frequency. This represents the highest priority weight, which is used to update the polling resources. The polling priority weights for each data item are dynamically adjusted. This represents the average value of the data after the data items were actually collected.

[0027] Preferably, in step S5, the difference data model is as follows:

[0028] in, Indicates time The difference between manually collected data and data monitored by the environmental monitoring system. and These are model parameters, matched based on historical data. This indicates the noise level; it predicts whether future discrepancies will exceed a threshold, and if so, provides an early warning to identify system problems.

[0029] An adaptive data correction and acquisition system for environmental monitoring, based on the aforementioned adaptive data correction and acquisition method for environmental monitoring, includes: a test point configuration module, a difference analysis module, a monitoring data correction module, an optimization and update module, and an early warning module; The test point configuration module is used to configure test points for key data items, classify the priority of test points and set update levels and corresponding weights, and allocate polling resources to test points of each level. The difference analysis module is used to create tasks in the environmental monitoring system. Maintenance personnel perform on-site measurements based on the task data, calculate the difference between the environmental monitoring data and the actual collected data for each data item, and analyze the difference patterns. The monitoring data correction module is used to dynamically adjust the parameters of the environmental monitoring system according to the data difference pattern using an adaptive correction algorithm, so as to obtain the corrected monitoring data. The optimization and update module is used to dynamically adjust the polling priority weight and polling frequency of each data item using a selective polling strategy, and to update and allocate polling resources. The early warning module is used to determine whether the difference in the monitored data exceeds a preset safety threshold by analyzing the changing trends of historical data and current difference data through continuous polling detection and difference analysis.

[0030] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned adaptive data correction and acquisition method for environmental monitoring.

[0031] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned adaptive data correction and acquisition method for environmental monitoring.

[0032] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a data adaptive correction and acquisition method and device for environmental monitoring. It introduces an adaptive correction algorithm based on difference analysis and a selective polling mechanism, which can identify the difference patterns between environmental monitoring system data and standard data in real time, and dynamically adjust the correction parameters and polling frequency to cope with changes in the environment and equipment status. Through error prediction and feedback mechanisms, it can continuously optimize the monitoring model, improve the accuracy of monitoring data and the timeliness of system response, reduce data lag and error accumulation, significantly improve the stability and reliability of the system in complex environments, and effectively ensure the quality of monitoring data and the efficiency of fault handling. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of a data adaptive correction and acquisition method for environmental monitoring provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the adaptive correction algorithm provided in an embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] This invention discloses a data adaptive correction and acquisition method for environmental monitoring, such as... Figure 1 As shown, it includes the following steps: S1. Configure test points for key data items, classify the priority of test points and set update levels and corresponding weights, allocate polling resources for each level of test points and collect environmental monitoring data for each data item. S2. Create a task in the environmental monitoring system. The maintenance personnel will perform on-site measurements based on the task data, calculate the difference between the environmental monitoring data and the actual collected data for each data item, and analyze the difference pattern. S3. Based on the data difference pattern, the parameters of the environmental monitoring system are dynamically adjusted using an adaptive correction algorithm to obtain corrected monitoring data; S4. Adopt a selective polling strategy to dynamically adjust the polling priority weight and polling frequency of each data item, and update and allocate polling resources; S5. By analyzing the changing trends of historical data and current difference data, and through continuous polling and difference analysis, a difference data prediction model is constructed to predict future differences and determine whether they exceed a preset safety threshold for error correction.

[0037] To further implement the above technical solution, in step S1, the polling resources allocated to each level of test point are as follows:

[0038] in, To update the level Resource allocation For the system's overall network and transmission resources, , , , The update levels are listed from highest to lowest. , , Set the weights for each update level.

[0039] To further implement the above technical solution, in step S2, a main task is created in the environmental monitoring system, which includes sub-tasks for each computer room. The maintenance personnel download the task data on the Pad and perform on-site measurements. After ensuring that the data is qualified, the data is submitted. The monitoring data of the environmental monitoring system is compared with the actual collected data, the differences are calculated, and the difference patterns are analyzed. The difference patterns include: fixed difference pattern, random difference pattern, and sudden anomaly pattern; There are relatively consistent differences between monitored data and actual data. In such cases, the difference between a certain data item in the environmental monitoring system and the actually collected data is used as the reference value. for: ,

[0040] The difference between the data monitored by the environmental monitoring system and the actual data exhibits random fluctuations and deviates from the actual data. The difference value under the random difference mode is: ,

[0041] The data monitored by the environmental monitoring system deviates significantly from the actual data at certain times. A threshold is set for this deviation. When the difference value exceeds the threshold If it is an intermittent fault, it is not used as reference data. The difference value under the sudden abnormal mode is: ,

[0042] in, This represents the difference between a specific data item recorded in the environmental monitoring system and the actual data collected. This refers to the data monitored by the environmental monitoring system for a specific priority data item. This is data actually collected by the device. The amount of data collected, This is the preset deviation threshold.

[0043] To further implement the above technical solutions, such as Figure 2 The specific content of step S3 includes: S31. Define the state vectors of various indicator parameters of each monitoring device in the environmental monitoring system; S32. Construct the state transition equations of the equipment and the observation equations that describe the relationship between the monitoring data and the actual collected data; S33. Based on the Kalman filter principle and the state transition equation, predict the next state; S34. Update the covariance matrix of the actual collected data and determine the weight of the monitoring data correction by calculating the Kalman gain; S35. The final corrected data is obtained by combining the predicted status and the data monitored by the monitoring system.

[0044] To further implement the above technical solution, in step S31, an environmental monitoring system is set to monitor... Each device needs to be monitored. Given the functional data, the state vector of each indicator parameter of the monitoring equipment is:

[0045] in, For at any time The first manually collected The first of the equipment Item function data; Step S32, the state transition equation of the device is:

[0046] in, Represents the current state vector State at the next moment The state transition matrix, Indicates control input The control matrix that affects the state. The random error representing the state evolution process follows a Gaussian distribution; , The calculation is as follows:

[0047] in, For the state vector The mean is set as the difference value at the previous time step. , For variance, Indicates the scaling factor. It is a positive semi-definite matrix; The observation equation is:

[0048] in, Indicates at time The environmental monitoring system monitors the first The first of the equipment Item function data, Represents the observation matrix. Indicates observation noise; Step S33, the predicted next state is:

[0049] The method for updating the covariance matrix is ​​as follows:

[0050] in, Indicates state time The covariance matrix, Let be the covariance matrix of the process noise. Let be the transition matrix from time k to time k+1. Let be the covariance matrix at time k+1, and T denote the transpose; Weight of monitoring data correction for:

[0051] The final corrected data obtained for:

[0052] in, To predict the state, The data monitored by the monitoring system.

[0053] To further implement the above technical solution, step S4, the selective polling strategy is as follows: dynamically adjust the polling priority weight of each data item by difference value and real-time importance, and dynamically adjust the polling frequency. For sudden anomalies and fault points, temporarily skip the polling. The polling frequency is:

[0054] in, For dynamically adjusted polling frequency, Indicates the basic polling frequency. This represents the highest priority weight, which is used to update the polling resources. The polling priority weights for each data item are dynamically adjusted. The method for dynamically adjusting priority weights based on the actual collected data is as follows:

[0055] in, This represents the difference between the data item in the environmental monitoring system and the actual collected data. This represents the average value of the data after the data items were actually collected.

[0056] To further implement the above technical solution, in step S5, by collecting the difference values ​​over a certain period of time, a historical data sequence is constructed, the data after each correction is recorded and fed back, and a prediction model is used to predict future differences. A safety threshold is set, and if the predicted future difference values ​​exceed the threshold, an early warning is issued, system problems are checked, and the system error correction capability is improved. The specific difference data model is as follows:

[0057] in, Indicates time The difference between manually collected data and data monitored by the environmental monitoring system. and These are model parameters, matched based on historical data. This indicates the noise term.

[0058] An adaptive data correction and acquisition system for environmental monitoring, based on an adaptive data correction and acquisition method for environmental monitoring, includes: a test point configuration module, a difference analysis module, a monitoring data correction module, an optimization and update module, and an early warning module; The test point configuration module is used to configure test points for key data items, classify the priority of test points and set update levels and corresponding weights, allocate polling resources for each level of test points and collect environmental monitoring data for each data item. The difference analysis module is used to create tasks in the environmental monitoring system. Maintenance personnel perform on-site measurements based on the task data, calculate the difference between the environmental monitoring data and the actual collected data for each data item, and analyze the difference patterns. The monitoring data correction module is used to dynamically adjust the parameters of the environmental monitoring system according to the data difference pattern using an adaptive correction algorithm, so as to obtain the corrected monitoring data. The optimization and update module is used to dynamically adjust the polling priority weight and polling frequency of each data item using a selective polling strategy, and to update and allocate polling resources. The early warning module is used to determine whether the difference in the monitored data exceeds a preset safety threshold by analyzing the changing trends of historical data and current difference data through continuous polling detection and difference analysis.

[0059] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a data adaptive correction and acquisition method for environmental monitoring.

[0060] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a data adaptive correction and acquisition method for environmental monitoring.

[0061] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0062] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data adaptive correction and acquisition method for environmental monitoring, characterized in that, Includes the following steps: S1. Configure test points for key data items, classify the priority of test points and set update levels and corresponding weights, allocate polling resources for each level of test points and collect environmental monitoring data for each data item. S2. Create a task in the environmental monitoring system. The maintenance personnel will perform on-site measurements based on the task data, calculate the difference between the environmental monitoring data and the actual collected data for each data item, and analyze the difference pattern. S3. Based on the data difference pattern, the parameters of the environmental monitoring system are dynamically adjusted using an adaptive correction algorithm to obtain corrected monitoring data; S4. Adopt a selective polling strategy to dynamically adjust the polling priority weight and polling frequency of each data item, and update and allocate polling resources; S5. By analyzing the changing trends of historical data and current difference data, and through continuous polling detection and difference analysis, a difference data prediction model is constructed to predict future differences and determine whether they exceed the preset safety threshold for error correction.

2. The adaptive data correction and acquisition method for environmental monitoring as described in claim 1, characterized in that, In step S1, the polling resources allocated to each level of test point are as follows: in, To update the level Resource allocation For the system's overall network and transmission resources, , , , The update levels are listed from highest to lowest. , , Set the weights for each update level.

3. The adaptive data correction and acquisition method for environmental monitoring as described in claim 1, characterized in that, The specific content of step S3 includes: S31. Define the state vectors of various indicator parameters of each monitoring device in the environmental monitoring system; S32. Construct the state transition equations of the equipment and the observation equations describing the relationship between the monitoring data and the actual collected data; S33. Based on the Kalman filter principle and the state transition equation, predict the next state; S34. Update the covariance matrix of the actual collected data and determine the weight of the monitoring data correction by calculating the Kalman gain; S35. The final corrected data is obtained by combining the predicted status and the data monitored by the monitoring system.

4. The adaptive data correction and acquisition method for environmental monitoring as described in claim 3, characterized in that, Step S31, the state vectors of the various indicator parameters of the monitoring equipment are: in, For at any time The first manually collected The first of the equipment Item function data; Step S32, the state transition equation of the device is: in, Represents the current state vector State at the next moment The state transition matrix, Indicates control input The control matrix that affects the state. The random error representing the state evolution process follows a Gaussian distribution; , The calculation is as follows: in, For the state vector The mean is set as the difference value at the previous time step. , For variance, Indicates the scaling factor. It is a positive semi-definite matrix; The observation equation is: in, Indicates at time The environmental monitoring system monitors the first The first of the equipment Item function data, Represents the observation matrix. Indicates observation noise; Step S33, the predicted next state is: The method for updating the covariance matrix is ​​as follows: in, Indicates state time The covariance matrix, Let be the covariance matrix of the process noise. Let be the transition matrix from time k to time k+1. Let be the covariance matrix at time k+1, and T denote the transpose; Weight of monitoring data correction for: The final corrected data obtained for: in, To predict the state, The data monitored by the monitoring system.

5. The adaptive data correction and acquisition method for environmental monitoring as described in claim 1, characterized in that, Step S4, the selective polling strategy is as follows: dynamically adjust the polling priority weight of each data item by the difference value and real-time importance, and dynamically adjust the polling frequency. For sudden anomalies and fault points, polling is temporarily skipped. The polling frequency is: in, For dynamically adjusted polling frequency, Indicates the basic polling frequency. This represents the highest priority weight, which is used to update the polling resources. The polling priority weights for each data item are dynamically adjusted. This represents the average value of the data collected for each data item. This represents the difference between a specific data item recorded in the environmental monitoring system and the actual data collected. , , , Set the weights for each update level.

6. The adaptive data correction and acquisition method for environmental monitoring as described in claim 1, characterized in that, Step S5, the specific difference data model is as follows: in, Indicates time The difference between manually collected data and data monitored by the environmental monitoring system. and These are model parameters, matched based on historical data. This indicates the noise level; it predicts whether future discrepancies will exceed a threshold, and if so, provides an early warning to identify system problems.

7. A data adaptive correction and acquisition system for environmental monitoring, characterized in that, A data adaptive correction and acquisition method for environmental monitoring based on any one of claims 1-6 includes: a test point configuration module, a difference analysis module, a monitoring data correction module, an optimization and update module, and an early warning module; The test point configuration module is used to configure test points for key data items, classify the priority of test points and set update levels and corresponding weights, allocate polling resources for each level of test points and collect environmental monitoring data for each data item. The difference analysis module is used to create tasks in the environmental monitoring system. Maintenance personnel perform on-site measurements based on the task data, calculate the difference between the environmental monitoring data and the actual collected data for each data item, and analyze the difference patterns. The monitoring data correction module is used to dynamically adjust the parameters of the environmental monitoring system according to the data difference pattern using an adaptive correction algorithm, so as to obtain the corrected monitoring data. The optimization and update module is used to dynamically adjust the polling priority weight and polling frequency of each data item using a selective polling strategy, and to update and allocate polling resources. The early warning module is used to determine whether the difference in the monitored data exceeds a preset safety threshold by analyzing the changing trends of historical data and current difference data through continuous polling detection and difference analysis.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a data adaptive correction and acquisition method for environmental monitoring as described in any one of claims 1 to 6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a data adaptive correction and acquisition method for environmental monitoring as described in any one of claims 1 to 6.

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