A concentrator data acquisition adaptive control method and system

Abstract

The present invention proposes a concentrator data acquisition adaptive control method and system, which relates to the technical field of concentrator data acquisition adaptive control. The concentrator obtains node task acquisition data, obtains node acquisition state coefficients, and then obtains node acquisition state judgment information; adds timestamps, obtains acquisition nodes in the same state, and then obtains normal acquisition networks and abnormal acquisition networks, compares the size of network ranges, and then obtains abnormal factors; determines parameter adjustment objects according to abnormal factor judgment information, calculates corresponding adjustment coefficients, performs corresponding parameter adjustment, and triggers control analysis instructions according to the adjustment results; updates acquisition nodes in the same state according to change information, and then judges the adaptive control state and whether to trigger a cyclic control instruction. The present invention can realize abnormal location analysis of concentrator acquisition and improve the efficiency and accuracy of concentrator abnormal acquisition processing.

Description

Technical Field

[0001] The present invention proposes a concentrator data acquisition and adaptive control method and system, which relate to the field of adaptive control technology, and in particular to the field of concentrator data acquisition and adaptive control technology. Background Art

[0002] The concentrator is the core device of a data acquisition system, and its performance and stability are crucial to the efficiency and accuracy of data collection. However, actual data collection faces numerous challenges: it's difficult to distinguish between normal and abnormal data collection within the collection environment, and it's difficult to define the scope. Most existing data collection and control methods lack adaptability, employing fixed collection tasks and parameters that are incapable of real-time dynamic adjustment. Changes in the collection environment or node status are difficult to identify and control in a timely manner, impacting data accuracy and reliability. Furthermore, there are deficiencies in identifying abnormal factors, making it difficult to distinguish between concentrator and node issues. Control measures lack specificity, and blind adjustments not only waste resources but may also fail to address the actual problem. Summary of the Invention

[0003] The present invention provides a concentrator data acquisition and adaptive control method and system to solve the above problems:

[0004] The present invention proposes a concentrator data acquisition and adaptive control method and system, the method comprising:

[0005] S1. Set a data collection task, obtain the task matching node, obtain the node task collection data of the task matching node through the concentrator, obtain the node collection state coefficient based on the collection process data, and then obtain the node collection state judgment information;

[0006] S2. Add a timestamp to the node collection status determination information, obtain the collection nodes in the same state based on the timestamp, obtain the normal collection network and the abnormal collection network based on the collection nodes in the same state, compare the size of the network range, and then determine the abnormal factors of the collection network status information;

[0007] S3. Determine the parameter adjustment object based on the abnormal factor determination information, calculate the corresponding adjustment coefficient, perform the corresponding parameter adjustment, and trigger the control analysis instruction based on the adjustment result;

[0008] S4. Obtain change information of node acquisition status determination information according to different timestamps, update the same status acquisition nodes according to the change information, and then determine the adaptive control state and whether to trigger the cyclic control instruction.

[0009] Furthermore, the system includes:

[0010] The node status determination module is used to set data collection tasks, obtain task matching nodes, obtain node task collection data of task matching nodes through the concentrator, obtain node collection status coefficients based on collection process data, and then obtain node collection status determination information;

[0011] The abnormal factor analysis module is used to add a timestamp to the node collection status determination information, obtain the collection nodes in the same state based on the timestamp, obtain the normal collection network and the abnormal collection network based on the collection nodes in the same state, compare the size of the network range, and then determine the abnormal factors of the collection network status information;

[0012] The abnormal classification adjustment module is used to determine the parameter adjustment object based on the abnormal factor judgment information, calculate the corresponding adjustment coefficient, perform the corresponding parameter adjustment, and trigger the control analysis instruction according to the adjustment result;

[0013] The change update control module is used to obtain the change information of the node acquisition status determination information according to different timestamps, update the acquisition nodes in the same state according to the change information, and then judge the adaptive control state and determine whether to trigger the cyclic control instruction.

[0014] Beneficial effects of the present invention: By comparing the node acquisition state coefficient and the acquisition network range, abnormal factors in the acquisition network can be accurately located.

[0015] Parameters are adjusted based on abnormal factors, and the same-state collection nodes are updated according to changes in node collection status determination information, realizing adaptive control of the data collection process and being able to promptly respond to the impact of concentrator or node anomalies during the collection process.

[0016] By determining whether to trigger the loop control instruction, the data acquisition system can be dynamically optimized, the quality and efficiency of data acquisition can be continuously improved, and the system can be ensured to operate stably and efficiently in the event of concentrator anomalies or node acquisition anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of a concentrator data acquisition and adaptive control method. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0019] One embodiment of the present invention provides a method and system for adaptively controlling concentrator data acquisition, the method comprising:

[0020] S1. Set a data collection task, obtain the task matching node, obtain the node task collection data of the task matching node through the concentrator, obtain the node collection state coefficient based on the collection process data, and then obtain the node collection state judgment information;

[0021] S2. Add a timestamp to the node collection status determination information, obtain the collection nodes in the same state based on the timestamp, obtain the normal collection network and the abnormal collection network based on the collection nodes in the same state, compare the size of the network range, and then determine the abnormal factors of the collection network status information;

[0022] S3. Determine the parameter adjustment object based on the abnormal factor determination information, calculate the corresponding adjustment coefficient, perform the corresponding parameter adjustment, and trigger the control analysis instruction based on the adjustment result;

[0023] S4, obtain the change information of the node acquisition state determination information according to different timestamps, update the same state acquisition nodes according to the change information, and then judge the adaptive control state and whether to trigger the cyclic control instruction, such as Figure 1 shown.

[0024] The working principle and technical effect of the above technical solution are as follows: first, set the data collection task, determine the task matching nodes, the concentrator obtains the collection data of these nodes, and calculates the node collection status coefficient based on the collection process data, thereby obtaining the node collection status judgment information.

[0025] Add a timestamp to the node collection status determination information, and use the timestamp to find the collection nodes with the same status, and then divide the normal collection network into normal collection network and abnormal collection network. By comparing the size of the two network ranges, determine the abnormal factors of the collection network status information.

[0026] Based on the abnormal factor judgment information, the parameter objects that need to be adjusted are determined, the corresponding adjustment coefficients are calculated, the corresponding parameters are adjusted, and the control analysis instructions are triggered according to the adjustment results.

[0027] The changes in node acquisition status determination information are obtained using different timestamps, and based on this, the acquisition nodes in the same state are updated to judge the adaptive control status and decide whether to trigger the cyclic control instruction.

[0028] By comparing the node acquisition status coefficient and the acquisition network range, abnormal factors in the acquisition network can be accurately located.

[0029] Parameters are adjusted based on abnormal factors, and the same-state collection nodes are updated according to changes in node collection status determination information, realizing adaptive control of the data collection process and being able to promptly respond to the impact of concentrator or node anomalies during the collection process.

[0030] By determining whether to trigger the loop control instruction, the data acquisition system can be dynamically optimized, the quality and efficiency of data acquisition can be continuously improved, and the system can be ensured to operate stably and efficiently in the event of concentrator anomalies or node acquisition anomalies.

[0031] In one embodiment of the present invention, S1 includes:

[0032] Acquire data collection requirement information through the concentrator, and set data collection tasks according to the data collection requirement information;

[0033] Perform matching analysis on the data collection task and the preset collection node to obtain a matching analysis result;

[0034] Match the data collection task with the preset collection node according to the matching analysis results to obtain the task matching node;

[0035] According to the data collection task of the task matching node, the node collection parameters are set, and the data collection task of the task matching node is collected according to the node collection parameters to obtain the node task collection data; the node collection parameters include collection frequency and collection amount, etc.

[0036] Acquire the collection process data of the task matching node during the collection process, calculate the node collection status coefficient based on the collection process data, and determine the collection status of the task matching node based on the node collection status coefficient to obtain the node collection status determination information. The collection process data includes network status, collection completion, and collection quality.

[0037] The calculation formula of the node acquisition state coefficient is:

[0038]

[0039] Among them, N is the node acquisition state coefficient, TC is the current network round trip delay, T max is the maximum allowed delay, C a The actual amount of data collected, C t is the target data collection volume, BR is the bit error rate, BT is the bit error rate threshold, α, β and γ are weight coefficients, the sum of which equals 1;

[0040] The working principle and technical effect of the above technical solution are as follows: data collection requirement information is obtained through the concentrator, and specific data collection tasks are set based on the requirement information to clarify the collection objectives and requirements. What information needs to be collected is the data collection requirement information. The data collection task includes the type of data to be collected, the time of data collection, and the amount of data to be collected.

[0041] Perform matching analysis on the set data collection tasks and the preset collection nodes, taking into account factors such as task characteristics and node capabilities, and obtain matching analysis results. Matching includes matching of collection types and collection time, etc.

[0042] Based on the matching analysis results, the node that is most suitable for executing the data collection task is selected from the preset collection nodes, namely the task matching node. The most matching node is the task matching node;

[0043] For the data collection task of the task matching node, set the corresponding node collection parameters, such as collection frequency, collection volume, etc., and then collect data from the task matching node according to these parameters to obtain the node task collection data.

[0044] During the collection process, the collection process data of the task matching node is obtained, including network status, collection completion and collection quality, etc., the node collection status coefficient is calculated, and then the collection status of the task matching node is judged to obtain the node collection status judgment information.

[0045] Through task and node matching analysis, data collection tasks can be accurately assigned to appropriate collection nodes, improving collection efficiency and accuracy and avoiding resource waste.

[0046] Setting collection parameters, such as collection frequency and collection volume, according to the specific circumstances of the task matching node can meet different collection requirements and achieve flexible and efficient data collection.

[0047] By using the data from the collection process to calculate the node collection state coefficient and perform state judgment, we can understand the collection state of the task matching node in real time and promptly discover the collection state situation during the collection process.

[0048] By comprehensively considering multiple aspects such as collection requirements, node matching, parameter settings and status determination, the monitoring and guarantee of data collection quality are greatly improved.

[0049] In one embodiment of the present invention, S2 includes:

[0050] Adding a timestamp to the node acquisition status determination information to obtain the node acquisition status timestamp information;

[0051] Obtaining the node collection state timestamp information of all preset collection nodes, obtaining the time nodes of the same collection state with the same timestamp according to the node collection state timestamp information of all preset collection nodes, and obtaining the collection nodes with the same state;

[0052] The same-state collection nodes include normal-state collection nodes and abnormal-state collection nodes;

[0053] Connect the normal state acquisition nodes to obtain a normal acquisition network;

[0054] Connect the abnormal state collection nodes to obtain the abnormality collection network;

[0055] Compare the ranges of the normal acquisition network and the abnormal acquisition network to obtain the relatively large range and the relatively small range;

[0056] When the range of the normal acquisition network is sufficient to cover the range of the abnormal acquisition network, the normal acquisition network is determined to be relatively large and the abnormal acquisition network is determined to be relatively small. Otherwise, the abnormal acquisition network is determined to be relatively large and the normal acquisition network is determined to be relatively small.

[0057] Abnormal factors are determined based on the collected network status information in a relatively large range and a relatively small range to obtain abnormal factor determination information; the abnormal factor determination information includes concentrator collection abnormalities and node collection abnormalities.

[0058] The determining of abnormal factors based on the collected network status information in a relatively large range and a relatively small range, and obtaining abnormal factor determination information includes:

[0059] When a relatively large area is a normal collection network and a relatively small area is an abnormal collection network, it is determined that the concentrator collection is abnormal;

[0060] When a relatively large area is an abnormal collection network and a relatively small area is a normal collection network, it is determined that the node collection is abnormal.

[0061] The working principle and technical effect of the above technical solution are: adding a timestamp to the node acquisition status determination information to obtain the node acquisition status timestamp information.

[0062] Collect the node collection status timestamp information of all preset collection nodes, find the nodes with the same collection status at the same timestamp, and form the same-status collection nodes. These nodes are divided into normal state collection nodes and abnormal state collection nodes. This enables visualization of the normal collection range and abnormal collection range to improve comparability.

[0063] The normal state acquisition nodes and the abnormal state acquisition nodes are connected respectively to construct a normal acquisition network and an abnormal acquisition network.

[0064] Compare the ranges of the normal and abnormal collection networks to determine the relatively large and relatively small ranges. If the range of the normal collection network covers the abnormal collection network, the normal collection network is relatively large and the abnormal collection network is relatively small; conversely, the abnormal collection network is relatively large and the normal collection network is relatively small.

[0065] The abnormal factors are determined based on the state information of the relatively large and relatively small collection networks. When the normal collection network is relatively large, it is determined that the concentrator collection is abnormal; when the abnormal collection network is relatively large, it is determined that the node collection is abnormal, thereby obtaining abnormal factor determination information.

[0066] Through timestamp marking and same-state node analysis, combined with network range comparison, it is possible to accurately, simply and efficiently distinguish whether it is a concentrator collection anomaly or a node collection anomaly.

[0067] Analyzing the collection status from the two dimensions of time and space (network scope) can provide a more comprehensive and in-depth understanding of the operating status of the data collection system.

[0068] Quickly and accurately determine abnormal factors, improve the reliability and stability of the data acquisition system, ensure the continuity and accuracy of data collection, optimize system resource allocation, and improve system operation efficiency.

[0069] In one embodiment of the present invention, S3 includes:

[0070] Determine the parameter adjustment object based on the abnormal factor judgment information, and obtain the corresponding adjustment coefficient for different parameter adjustment objects;

[0071] When the parameter adjustment object is a concentrator, the abnormal adjustment coefficient of the concentrator is calculated, and the abnormal parameters of the concentrator are adjusted according to the abnormal adjustment coefficient to obtain the concentrator adjustment result;

[0072] The calculation formula of the concentrator abnormal adjustment coefficient is:

[0073]

[0074] Among them, ZQ is the concentrator abnormality adjustment coefficient, m is the concentrator abnormality impact type, E ci is the actual collected data of the abnormal impact type of the i-th concentrator, E ti is the limit value of the abnormal impact type of the i-th concentrator, q i is the preset weight data of the influence category of the i-th concentrator;

[0075] Concentrator abnormality impacts include temperature, packet loss, and queue backlog duration.

[0076] When ZQ is greater than 1, the analysis is considered severe abnormality.

[0077] The adjustment coefficient is the adjustment standard for each parameter.

[0078] When the parameter adjustment object is a preset collection node, the node abnormality adjustment coefficient is calculated, and the abnormal parameters of the preset collection node are adjusted according to the node abnormality adjustment coefficient to obtain the node adjustment result; the adjustment of the preset collection node includes parameter adjustment of the collection process between the concentrator and the node.

[0079] The calculation formula of the node abnormality adjustment coefficient is:

[0080]

[0081] Among them, ED is the node abnormality adjustment coefficient, f is the node abnormality impact type, K ca is the actual collected data of the a-th type of node abnormal impact, K ta is the limit value of the type of abnormal impact of the a-th node, W a The preset weight data for the influence type of the a-th node;

[0082] Types of node abnormality impacts include signal attenuation data and noise interference data.

[0083] The control and analysis instructions are triggered based on the concentrator adjustment results. The control and analysis instructions are triggered after the concentrator is adjusted.

[0084] The working principle and technical effect of the above technical solution are: based on the abnormal factor determination information obtained previously, the objects that need to adjust parameters are clearly identified, including the concentrator and the preset collection node.

[0085] Calculate the corresponding abnormal adjustment coefficient for each parameter adjustment object. If the parameter adjustment object is a concentrator, calculate the concentrator abnormal adjustment coefficient; if it is a preset collection node, calculate the node abnormal adjustment coefficient.

[0086] The calculated adjustment coefficient is used to adjust the abnormal parameters of the corresponding object. When the adjustment object is a concentrator, the concentrator's abnormal adjustment coefficient is used to adjust the concentrator; when the adjustment object is a preset collection node, the node's abnormal adjustment coefficient is used to adjust the node. The adjustment of the preset collection node is to adjust the parameters of the collection process between the concentrator and the node.

[0087] After completing the abnormal parameter adjustment of the concentrator, the control analysis instruction is triggered immediately.

[0088] Based on the abnormal factor judgment information, the parameter adjustment object is accurately determined, and the corresponding adjustment coefficient is calculated for adjustment, which realizes targeted processing of different abnormal situations and improves the effectiveness and accuracy of the adjustment.

[0089] Adjustment of preset collection nodes covers adjustment of collection process parameters between the concentrator and the nodes, which can improve collection efficiency and quality.

[0090] After the concentrator is adjusted, the control analysis instruction is triggered, which can timely analyze and evaluate the concentrator adjustment results to determine whether the adjustment has achieved the expected effect.

[0091] In one embodiment of the present invention, the S4 includes:

[0092] When the control analysis instruction is triggered, the node collection state determination information of each preset collection node at different timestamps is obtained, and then the change information of the node collection state determination information of each preset collection node is obtained;

[0093] When the node acquisition state determination information changes, obtain the timestamp corresponding to the change in the node acquisition state determination information, and obtain the corresponding timestamp of the node;

[0094] When at least half of the preset collection nodes in the abnormal state collection nodes obtain the node corresponding timestamp, the collection nodes in the same state are updated to obtain the update information of the collection nodes in the same state;

[0095] Perform adaptive control state judgment based on the updated information of the same state acquisition node to obtain the adaptive control state judgment result;

[0096] The cyclic control instruction is triggered according to the result of the adaptive control state judgment. After the cyclic control instruction is triggered, it is further judged whether the abnormal factor is the concentrator collection abnormality.

[0097] The working principle and technical effect of the above technical solution are: after the control analysis instruction is triggered, the node collection status judgment information of each preset collection node at different timestamps is collected, and by comparing the information at different timestamps, the change of the collection status judgment information of each preset collection node is obtained.

[0098] If the node acquisition status determination information changes, the timestamp corresponding to the change is recorded, that is, the node corresponding timestamp.

[0099] When at least half of the preset collection nodes in the abnormal state collection node have obtained the node corresponding timestamp (at the same timestamp), the collection nodes in the same state are updated to obtain the update information of the collection nodes in the same state.

[0100] According to the updated information of the same-state acquisition nodes, the adaptive control state is judged and the adaptive control state judgment result is obtained.

[0101] The cyclic control instruction is triggered according to the result of the adaptive control state judgment. After the cyclic control instruction is triggered, it is further judged whether the abnormal factor is the concentrator collection abnormality.

[0102] By obtaining the node collection status determination information and its change information at different timestamps, the status changes of each preset collection node can be monitored in real time, and new situations that arise during the collection process can be discovered in a timely manner.

[0103] The acquisition nodes in the same state are updated under the condition that a certain proportion of the nodes in the abnormal state obtain the corresponding timestamp, which ensures the dynamic and accuracy of the information of the acquisition nodes in the same state and enables the system to reflect the actual state of the acquisition nodes in a timely manner.

[0104] Adaptive control state judgment based on the updated information of the same-state acquisition nodes can accurately evaluate the effect of the control measures and determine whether the system has achieved the expected control goals.

[0105] Triggering loop control instructions and continuously determining whether the abnormal factor is due to concentrator collection anomalies enables continuous optimization of the data collection system. If the abnormal factor is still due to concentrator collection anomalies, further adjustments to concentrator parameters or other measures can be taken.

[0106] In one embodiment of the present invention, the system includes:

[0107] The node status determination module is used to set data collection tasks, obtain task matching nodes, obtain node task collection data of task matching nodes through the concentrator, obtain node collection status coefficients based on collection process data, and then obtain node collection status determination information;

[0108] The abnormal factor analysis module is used to add a timestamp to the node collection status determination information, obtain the collection nodes in the same state based on the timestamp, obtain the normal collection network and the abnormal collection network based on the collection nodes in the same state, compare the size of the network range, and then determine the abnormal factors of the collection network status information;

[0109] The abnormal classification adjustment module is used to determine the parameter adjustment object based on the abnormal factor judgment information, calculate the corresponding adjustment coefficient, perform the corresponding parameter adjustment, and trigger the control analysis instruction according to the adjustment result;

[0110] The change update control module is used to obtain the change information of the node acquisition status determination information according to different timestamps, update the acquisition nodes in the same state according to the change information, and then judge the adaptive control state and determine whether to trigger the cyclic control instruction.

[0111] The working principle and technical effect of the above technical solution are as follows: first, set the data collection task, determine the task matching nodes, the concentrator obtains the collection data of these nodes, and calculates the node collection status coefficient based on the collection process data, thereby obtaining the node collection status judgment information.

[0112] Add a timestamp to the node collection status determination information, and use the timestamp to find the collection nodes with the same status, and then divide the normal collection network into normal collection network and abnormal collection network. By comparing the size of the two network ranges, determine the abnormal factors of the collection network status information.

[0113] Based on the abnormal factor judgment information, the parameter objects that need to be adjusted are determined, the corresponding adjustment coefficients are calculated, the corresponding parameters are adjusted, and the control analysis instructions are triggered according to the adjustment results.

[0114] The changes in node acquisition status determination information are obtained using different timestamps, and based on this, the acquisition nodes in the same state are updated to judge the adaptive control status and decide whether to trigger the cyclic control instruction.

[0115] By comparing the node acquisition status coefficient and the acquisition network range, abnormal factors in the acquisition network can be accurately located.

[0116] Parameters are adjusted based on abnormal factors, and the same-state collection nodes are updated according to changes in node collection status determination information, realizing adaptive control of the data collection process and being able to promptly respond to the impact of concentrator or node anomalies during the collection process.

[0117] By determining whether to trigger the loop control instruction, the data acquisition system can be dynamically optimized, the quality and efficiency of data acquisition can be continuously improved, and the system can be ensured to operate stably and efficiently in the event of concentrator anomalies or node acquisition anomalies.

[0118] In one embodiment of the present invention, the node status determination module includes:

[0119] The node collection module is used to obtain data collection requirement information through the concentrator and set data collection tasks according to the data collection requirement information;

[0120] Perform matching analysis on the data collection task and the preset collection node to obtain a matching analysis result;

[0121] Match the data collection task with the preset collection node according to the matching analysis results to obtain the task matching node;

[0122] According to the data collection task of the task matching node, the node collection parameters are set, and the data collection task of the task matching node is collected according to the node collection parameters to obtain the node task collection data; the node collection parameters include collection frequency and collection amount, etc.

[0123] The status analysis module is used to obtain the collection process data of the task matching node during the collection process, calculate the node collection state coefficient based on the collection process data, and determine the collection state of the task matching node based on the node collection state coefficient to obtain node collection state determination information. The collection process data includes network status, collection completion degree, and collection quality.

[0124] The calculation formula of the node acquisition state coefficient is:

[0125]

[0126] Among them, N is the node acquisition state coefficient, TC is the current network round trip delay, T max is the maximum allowed delay, C a The actual amount of data collected, C t is the target data collection volume, BR is the bit error rate, BT is the bit error rate threshold, α, β and γ are weight coefficients, the sum of which equals 1;

[0127] The working principle and technical effect of the above technical solution are as follows: data collection requirement information is obtained through the concentrator, and specific data collection tasks are set based on the requirement information to clarify the collection objectives and requirements. What information needs to be collected is the data collection requirement information. The data collection task includes the type of data to be collected, the time of data collection, and the amount of data to be collected.

[0128] Perform matching analysis on the set data collection tasks and the preset collection nodes, taking into account factors such as task characteristics and node capabilities, and obtain matching analysis results. Matching includes matching of collection types and collection time, etc.

[0129] Based on the matching analysis results, the node that is most suitable for executing the data collection task is selected from the preset collection nodes, namely the task matching node. The most matching node is the task matching node;

[0130] For the data collection task of the task matching node, set the corresponding node collection parameters, such as collection frequency, collection volume, etc., and then collect data from the task matching node according to these parameters to obtain the node task collection data.

[0131] During the collection process, the collection process data of the task matching node is obtained, including network status, collection completion and collection quality, etc., the node collection status coefficient is calculated, and then the collection status of the task matching node is judged to obtain the node collection status judgment information.

[0132] Through task and node matching analysis, data collection tasks can be accurately assigned to appropriate collection nodes, improving collection efficiency and accuracy and avoiding resource waste.

[0133] Setting collection parameters, such as collection frequency and collection volume, according to the specific circumstances of the task matching node can meet different collection requirements and achieve flexible and efficient data collection.

[0134] By using the data from the collection process to calculate the node collection state coefficient and perform state judgment, we can understand the collection state of the task matching node in real time and promptly discover the collection state situation during the collection process.

[0135] By comprehensively considering multiple aspects such as collection requirements, node matching, parameter settings and status determination, the monitoring and guarantee of data collection quality are greatly improved.

[0136] In one embodiment of the present invention, the abnormal factor analysis module includes:

[0137] A network acquisition module is used to add a timestamp to the node acquisition status determination information and obtain the node acquisition status timestamp information;

[0138] Obtaining the node collection state timestamp information of all preset collection nodes, obtaining the time nodes of the same collection state with the same timestamp according to the node collection state timestamp information of all preset collection nodes, and obtaining the collection nodes with the same state;

[0139] The same-state collection nodes include normal-state collection nodes and abnormal-state collection nodes;

[0140] Connect the normal state acquisition nodes to obtain a normal acquisition network;

[0141] Connect the abnormal state collection nodes to obtain the abnormality collection network;

[0142] Range comparison module, used to compare the ranges of normal acquisition network and abnormal acquisition network to obtain relatively large range and relatively small range;

[0143] When the range of the normal acquisition network is sufficient to cover the range of the abnormal acquisition network, the normal acquisition network is determined to be relatively large and the abnormal acquisition network is determined to be relatively small. Otherwise, the abnormal acquisition network is determined to be relatively large and the normal acquisition network is determined to be relatively small.

[0144] The abnormality determination module is used to determine abnormal factors based on the collected network status information in a relatively large range and a relatively small range, and obtain abnormal factor determination information; the abnormal factor determination information includes concentrator collection abnormalities and node collection abnormalities.

[0145] The determining of abnormal factors based on the collected network status information in a relatively large range and a relatively small range, and obtaining abnormal factor determination information includes:

[0146] When a relatively large area is a normal collection network and a relatively small area is an abnormal collection network, it is determined that the concentrator collection is abnormal;

[0147] When a relatively large area is an abnormal collection network and a relatively small area is a normal collection network, it is determined that the node collection is abnormal.

[0148] The working principle and technical effect of the above technical solution are: adding a timestamp to the node acquisition status determination information to obtain the node acquisition status timestamp information.

[0149] Collect the node collection status timestamp information of all preset collection nodes, find the nodes with the same collection status at the same timestamp, and form the same-status collection nodes. These nodes are divided into normal state collection nodes and abnormal state collection nodes. This enables visualization of the normal collection range and abnormal collection range to improve comparability.

[0150] The normal state acquisition nodes and the abnormal state acquisition nodes are connected respectively to construct a normal acquisition network and an abnormal acquisition network.

[0151] Compare the ranges of the normal and abnormal collection networks to determine the relatively large and relatively small ranges. If the range of the normal collection network covers the abnormal collection network, the normal collection network is relatively large and the abnormal collection network is relatively small; conversely, the abnormal collection network is relatively large and the normal collection network is relatively small.

[0152] The abnormal factors are determined based on the state information of the relatively large and relatively small collection networks. When the normal collection network is relatively large, it is determined that the concentrator collection is abnormal; when the abnormal collection network is relatively large, it is determined that the node collection is abnormal, thereby obtaining abnormal factor determination information.

[0153] Through timestamp marking and same-state node analysis, combined with network range comparison, it is possible to accurately, simply and efficiently distinguish whether it is a concentrator collection anomaly or a node collection anomaly.

[0154] Analyzing the collection status from the two dimensions of time and space (network scope) can provide a more comprehensive and in-depth understanding of the operating status of the data collection system.

[0155] Quickly and accurately determine abnormal factors, improve the reliability and stability of the data acquisition system, ensure the continuity and accuracy of data collection, optimize system resource allocation, and improve system operation efficiency.

[0156] In one embodiment of the present invention, the abnormal classification adjustment module includes:

[0157] The object corresponding module is used to determine the parameter adjustment object according to the abnormal factor judgment information, and obtain the corresponding adjustment coefficient for different parameter adjustment objects;

[0158] The concentrator adjustment module is used to calculate the abnormal adjustment coefficient of the concentrator when the parameter adjustment object is the concentrator, adjust the abnormal parameters of the concentrator according to the abnormal adjustment coefficient of the concentrator, and obtain the concentrator adjustment result;

[0159] The calculation formula of the concentrator abnormal adjustment coefficient is:

[0160]

[0161] Among them, ZQ is the concentrator abnormality adjustment coefficient, m is the concentrator abnormality impact type, E ci is the actual collected data of the abnormal impact type of the i-th concentrator, E ti is the limit value of the abnormal impact type of the i-th concentrator, q i is the preset weight data of the influence category of the i-th concentrator;

[0162] Concentrator abnormality impacts include temperature, packet loss, and queue backlog duration.

[0163] When ZQ is greater than 1, the analysis is considered severe abnormality.

[0164] The adjustment coefficient is the adjustment standard for each parameter.

[0165] The node adjustment module is used to calculate the node abnormality adjustment coefficient when the parameter adjustment object is a preset collection node, and adjust the abnormal parameters of the preset collection node according to the node abnormality adjustment coefficient to obtain the node adjustment result; the adjustment of the preset collection node includes parameter adjustment of the collection process between the concentrator and the node.

[0166] The calculation formula of the node abnormality adjustment coefficient is:

[0167]

[0168] Among them, ED is the node abnormality adjustment coefficient, f is the node abnormality impact type, K ca is the actual collected data of the a-th type of node abnormal impact, K ta is the limit value of the type of abnormal impact of the a-th node, W a The preset weight data for the influence type of the a-th node;

[0169] The analysis trigger module is used to trigger the control analysis instruction according to the concentrator adjustment result. The control analysis instruction is triggered after the concentrator is adjusted.

[0170] The working principle and technical effect of the above technical solution are: based on the abnormal factor determination information obtained previously, the objects that need to adjust parameters are clearly identified, including the concentrator and the preset collection node.

[0171] Calculate the corresponding abnormal adjustment coefficient for each parameter adjustment object. If the parameter adjustment object is a concentrator, calculate the concentrator abnormal adjustment coefficient; if it is a preset collection node, calculate the node abnormal adjustment coefficient.

[0172] The calculated adjustment coefficient is used to adjust the abnormal parameters of the corresponding object. When the adjustment object is a concentrator, the concentrator's abnormal adjustment coefficient is used to adjust the concentrator; when the adjustment object is a preset collection node, the node's abnormal adjustment coefficient is used to adjust the node. The adjustment of the preset collection node is to adjust the parameters of the collection process between the concentrator and the node.

[0173] After completing the abnormal parameter adjustment of the concentrator, the control analysis instruction is triggered immediately.

[0174] Based on the abnormal factor judgment information, the parameter adjustment object is accurately determined, and the corresponding adjustment coefficient is calculated for adjustment, which realizes targeted processing of different abnormal situations and improves the effectiveness and accuracy of the adjustment.

[0175] Adjustment of preset collection nodes covers adjustment of collection process parameters between the concentrator and the nodes, which can improve collection efficiency and quality.

[0176] After the concentrator is adjusted, the control analysis instruction is triggered, which can timely analyze and evaluate the concentrator adjustment results to determine whether the adjustment has achieved the expected effect.

[0177] In one embodiment of the present invention, the change update control module includes:

[0178] A change analysis module is used to obtain node collection status determination information of each preset collection node at different timestamps after the control analysis instruction is triggered, and further obtain change information of the node collection status determination information of each preset collection node;

[0179] When the node acquisition status determination information changes, obtain the timestamp corresponding to the change in the node acquisition status determination information, and update the determination module to obtain the corresponding timestamp of the node;

[0180] When at least half of the preset collection nodes in the abnormal state collection nodes obtain the node corresponding timestamp, the collection nodes in the same state are updated to obtain the update information of the collection nodes in the same state;

[0181] Perform adaptive control state judgment based on the updated information of the same state acquisition node to obtain the adaptive control state judgment result;

[0182] The cyclic control instruction is triggered according to the result of the adaptive control state judgment. After the cyclic control instruction is triggered, it is further judged whether the abnormal factor is the concentrator collection abnormality.

[0183] The working principle and technical effect of the above technical solution are: after the control analysis instruction is triggered, the node collection status judgment information of each preset collection node at different timestamps is collected, and by comparing the information at different timestamps, the change of the collection status judgment information of each preset collection node is obtained.

[0184] If the node acquisition status determination information changes, the timestamp corresponding to the change is recorded, that is, the node corresponding timestamp.

[0185] When at least half of the preset collection nodes in the abnormal state collection node have obtained the node corresponding timestamp (at the same timestamp), the collection nodes in the same state are updated to obtain the update information of the collection nodes in the same state.

[0186] According to the updated information of the same-state acquisition nodes, the adaptive control state is judged and the adaptive control state judgment result is obtained.

[0187] The cyclic control instruction is triggered according to the result of the adaptive control state judgment. After the cyclic control instruction is triggered, it is further judged whether the abnormal factor is the concentrator collection abnormality.

[0188] By obtaining the node collection status determination information and its change information at different timestamps, the status changes of each preset collection node can be monitored in real time, and new situations that arise during the collection process can be discovered in a timely manner.

[0189] The acquisition nodes in the same state are updated under the condition that a certain proportion of the nodes in the abnormal state obtain the corresponding timestamp, which ensures the dynamic and accuracy of the information of the acquisition nodes in the same state and enables the system to reflect the actual state of the acquisition nodes in a timely manner.

[0190] Adaptive control state judgment based on the updated information of the same-state acquisition nodes can accurately evaluate the effect of the control measures and determine whether the system has achieved the expected control goals.

[0191] Triggering loop control instructions and continuously determining whether the abnormal factor is due to concentrator collection anomalies enables continuous optimization of the data collection system. If the abnormal factor is still due to concentrator collection anomalies, further adjustments to concentrator parameters or other measures can be taken.

[0192] Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention is intended to include such modifications and variations.

Claims

1. A concentrator data acquisition adaptive control method, characterized in that: The method comprises: S1. Set a data collection task, obtain the task matching node, obtain the node task collection data of the task matching node through the concentrator, obtain the node collection state coefficient based on the collection process data, and then obtain the node collection state judgment information; S2. Add a timestamp to the node collection status determination information, obtain the collection nodes in the same state based on the timestamp, obtain the normal collection network and the abnormal collection network based on the collection nodes in the same state, compare the size of the network range, and then determine the abnormal factors of the collection network status information; Wherein, the S2 includes: Adding a timestamp to the node acquisition status determination information to obtain the node acquisition status timestamp information; Obtaining the node collection state timestamp information of all preset collection nodes, obtaining the time nodes of the same collection state with the same timestamp according to the node collection state timestamp information of all preset collection nodes, and obtaining the collection nodes with the same state; The same-state collection nodes include normal-state collection nodes and abnormal-state collection nodes; Connect the normal state acquisition nodes to obtain a normal acquisition network; Connect the abnormal state collection nodes to obtain the abnormality collection network; Compare the ranges of the normal acquisition network and the abnormal acquisition network to obtain the relatively large range and the relatively small range; Determine abnormal factors based on the collected network status information in a relatively large range and a relatively small range, and obtain abnormal factor determination information; S3. Determine the parameter adjustment object based on the abnormal factor determination information, calculate the corresponding adjustment coefficient, perform the corresponding parameter adjustment, and trigger the control analysis instruction based on the adjustment result; S4. Obtain change information of node acquisition status determination information according to different timestamps, update the same status acquisition nodes according to the change information, and then determine the adaptive control state and whether to trigger the cyclic control instruction.

2. The concentrator data acquisition adaptive control method according to claim 1, characterized in that: Said S1 comprises: Acquire data collection requirement information through the concentrator, and set data collection tasks according to the data collection requirement information; Perform matching analysis on the data collection task and the preset collection node to obtain a matching analysis result; Match the data collection task with the preset collection node according to the matching analysis results to obtain the task matching node; According to the data collection task of the task matching node, node collection parameters are set, and data collection tasks are performed on the task matching node according to the node collection parameters to obtain node task collection data; Acquire the collection process data of the task matching node during the collection process, calculate the node collection state coefficient according to the collection process data, determine the collection state of the task matching node according to the node collection state coefficient, and obtain the node collection state determination information.

3. The concentrator data acquisition adaptive control method according to claim 1, characterized in that: Determining abnormal factors based on the collected network status information in a relatively large range and a relatively small range, and obtaining abnormal factor determination information includes: When a relatively large area is a normal collection network and a relatively small area is an abnormal collection network, it is determined that the concentrator collection is abnormal; When a relatively large area is an abnormal collection network and a relatively small area is a normal collection network, it is determined that the node collection is abnormal.

4. The concentrator data acquisition adaptive control method according to claim 1, characterized in that: The S3 includes: Determine the parameter adjustment object based on the abnormal factor judgment information, and obtain the corresponding adjustment coefficient for different parameter adjustment objects; When the parameter adjustment object is a concentrator, the abnormal adjustment coefficient of the concentrator is calculated, and the abnormal parameters of the concentrator are adjusted according to the abnormal adjustment coefficient to obtain the concentrator adjustment result; When the parameter adjustment object is a preset collection node, the node abnormality adjustment coefficient is calculated, and the abnormal parameters of the preset collection node are adjusted according to the node abnormality adjustment coefficient to obtain the node adjustment result; Trigger control analysis instructions based on the concentrator adjustment results.

5. The concentrator data acquisition adaptive control method according to claim 1, characterized in that: The S4 includes: When the control analysis instruction is triggered, the node collection state determination information of each preset collection node at different timestamps is obtained, and then the change information of the node collection state determination information of each preset collection node is obtained; When the node acquisition state determination information changes, obtain the timestamp corresponding to the change in the node acquisition state determination information, and obtain the corresponding timestamp of the node; When at least half of the preset collection nodes in the abnormal state collection nodes obtain the node corresponding timestamp, the collection nodes in the same state are updated to obtain the update information of the collection nodes in the same state; Perform adaptive control state judgment based on the updated information of the same state acquisition node to obtain the adaptive control state judgment result; The cyclic control instruction is triggered according to the adaptive control state judgment result.

6. A concentrator data acquisition and adaptive control system, characterized in that: The system comprises: The node status determination module is used to set data collection tasks, obtain task matching nodes, obtain node task collection data of task matching nodes through the concentrator, obtain node collection status coefficients based on collection process data, and then obtain node collection status determination information; The abnormal factor analysis module is used to add a timestamp to the node collection status determination information, obtain the collection nodes in the same state based on the timestamp, obtain the normal collection network and the abnormal collection network based on the collection nodes in the same state, compare the size of the network range, and then determine the abnormal factors of the collection network status information; The abnormal factor analysis module includes: A network acquisition module is used to add a timestamp to the node acquisition status determination information and obtain the node acquisition status timestamp information; Obtaining the node collection state timestamp information of all preset collection nodes, obtaining the time nodes of the same collection state with the same timestamp according to the node collection state timestamp information of all preset collection nodes, and obtaining the collection nodes with the same state; The same-state collection nodes include normal-state collection nodes and abnormal-state collection nodes; Connect the normal state acquisition nodes to obtain a normal acquisition network; Connect the abnormal state collection nodes to obtain the abnormality collection network; Range comparison module, used to compare the ranges of normal acquisition network and abnormal acquisition network to obtain relatively large range and relatively small range; When the range of the normal acquisition network is sufficient to cover the range of the abnormal acquisition network, the normal acquisition network is determined to be relatively large and the abnormal acquisition network is determined to be relatively small. Otherwise, the abnormal acquisition network is determined to be relatively large and the normal acquisition network is determined to be relatively small. An abnormality determination module is used to determine abnormal factors based on the collected network status information in a relatively large range and a relatively small range, and obtain abnormal factor determination information; The abnormal classification adjustment module is used to determine the parameter adjustment object based on the abnormal factor judgment information, calculate the corresponding adjustment coefficient, perform the corresponding parameter adjustment, and trigger the control analysis instruction according to the adjustment result; The change update control module is used to obtain the change information of the node acquisition status determination information according to different timestamps, update the acquisition nodes in the same state according to the change information, and then judge the adaptive control state and determine whether to trigger the cyclic control instruction.

7. The concentrator data acquisition and adaptive control system according to claim 6, characterized in that: The node status determination module includes: The node collection module is used to obtain data collection requirement information through the concentrator and set data collection tasks according to the data collection requirement information; Perform matching analysis on the data collection task and the preset collection node to obtain a matching analysis result; Match the data collection task with the preset collection node according to the matching analysis results to obtain the task matching node; According to the data collection task of the task matching node, node collection parameters are set, and data collection tasks are performed on the task matching node according to the node collection parameters to obtain node task collection data; The state analysis module is used to obtain the collection process data of the task matching node during the collection process, calculate the node collection state coefficient according to the collection process data, determine the collection state of the task matching node according to the node collection state coefficient, and obtain the node collection state determination information.

8. The concentrator data acquisition and adaptive control system according to claim 6, characterized in that: Determining abnormal factors based on the collected network status information in a relatively large range and a relatively small range, and obtaining abnormal factor determination information includes: When a relatively large area is a normal collection network and a relatively small area is an abnormal collection network, it is determined that the concentrator collection is abnormal; When a relatively large area is an abnormal collection network and a relatively small area is a normal collection network, it is determined that the node collection is abnormal.

9. The concentrator data acquisition and adaptive control system according to claim 6, characterized in that: The abnormal classification adjustment module includes: The object corresponding module is used to determine the parameter adjustment object according to the abnormal factor judgment information, and obtain the corresponding adjustment coefficient for different parameter adjustment objects; The concentrator adjustment module is used to calculate the abnormal adjustment coefficient of the concentrator when the parameter adjustment object is the concentrator, adjust the abnormal parameters of the concentrator according to the abnormal adjustment coefficient of the concentrator, and obtain the concentrator adjustment result; The node adjustment module is used to calculate the node abnormality adjustment coefficient when the parameter adjustment object is a preset collection node, and adjust the abnormal parameters of the preset collection node according to the node abnormality adjustment coefficient to obtain the node adjustment result; The analysis trigger module is used to trigger the control analysis instruction according to the concentrator adjustment result.

10. The concentrator data acquisition and adaptive control system according to claim 6, characterized in that: The change update control module includes: A change analysis module is used to obtain node collection status determination information of each preset collection node at different timestamps after the control analysis instruction is triggered, and further obtain change information of the node collection status determination information of each preset collection node; When the node acquisition status determination information changes, obtain the timestamp corresponding to the change in the node acquisition status determination information, and update the determination module to obtain the corresponding timestamp of the node; When at least half of the preset collection nodes in the abnormal state collection nodes obtain the node corresponding timestamp, the collection nodes in the same state are updated to obtain the update information of the collection nodes in the same state; Perform adaptive control state judgment based on the updated information of the same state acquisition node to obtain the adaptive control state judgment result; The cyclic control instruction is triggered according to the adaptive control state judgment result.

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