Metrology switch data acquisition and analysis system

Abstract

The application relates to the technical field of power system monitoring, and discloses a measurement switch data acquisition and analysis system, which comprises a data acquisition network construction module, an information fusion processing module, a strategy generation module and a remote control module. The system constructs a comprehensive acquisition network through measurement switch nodes, communication gateways, edge processing units and a central server, and collects switch state data and electrical parameter data in real time. The information fusion processing module analyzes operation characteristics and calculates an abnormal risk level; the strategy generation module dynamically divides early warning or emergency control periods according to the risk level, and optimizes acquisition resource configuration; and the remote control module adjusts the working modes of Internet of Things terminals and calibration devices according to the strategy. The application realizes real-time monitoring of the operation state of measurement switches, accurate abnormality determination and self-adaptive resource scheduling, and effectively improves the reliability and response efficiency of power grid monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system monitoring, in particular to a measurement switch data acquisition and analysis system. BACKGROUND

[0002] In modern power systems, measurement switches, as key devices, their operating status directly affects the stability and reliability of the power grid. Traditional measurement switch monitoring mainly relies on manual inspection or single sensor data collection, which has problems such as incomplete data coverage, poor real-time performance, and delayed abnormality detection. With the expansion of the power grid scale and the increasing demand for intelligentization, traditional methods are difficult to meet the precise monitoring needs of measurement switches in complex environments.

[0003] In the prior art, some schemes achieve data acquisition by deploying a sensor network, but lack the ability to fuse multi-source data, resulting in insufficient accuracy of abnormality detection. For example, relying only on single dimensions of switch state data or electrical parameter data cannot fully reflect the switch operating characteristics. In addition, existing systems usually use centralized data processing architecture, resulting in high data transmission delay and difficulty in achieving real-time risk determination and rapid response. In terms of resource allocation, traditional methods mostly use fixed strategies, which cannot dynamically adjust the collection resources according to the actual operating state, resulting in resource waste or missing of key data.

[0004] Another type of improved scheme attempts to introduce edge computing units, but does not solve the problems of data traceability and risk level quantification. For example, some systems only use threshold values to determine abnormalities without considering the influence of historical operating characteristics and environmental factors, resulting in high false alarm rate. At the same time, remote control functions are usually limited to simple instruction issuance, lacking linkage mechanism with strategy generation, making it difficult to achieve precise control.

[0005] Therefore, a measurement switch data acquisition and analysis system capable of constructing a three-dimensional acquisition network, realizing multi-source data fusion analysis, dynamically generating control strategies and supporting remote precise control is needed to solve the above technical problems. SUMMARY

[0006] The purpose of the present application is to provide a measurement switch data acquisition and analysis system to solve the problems raised in the background.

[0007] To achieve the above purpose, the present application provides the following technical scheme: a measurement switch data acquisition and analysis system, the system comprising:

[0008] a data acquisition network construction module, an information fusion processing module, a strategy generation module, and a remote control module;

[0009] The data acquisition network construction module constructs a three-dimensional data acquisition network of the measurement switch of the target area based on the measurement switch node, the communication gateway, the edge processing unit, the central server, the calibration device and the Internet of Things terminal deployed in the target area;

[0010] The information fusion processing module analyzes the operation characteristic distribution of the measurement switch of the target area according to the switch state data and the electrical parameter data collected by the measurement switch node, and determines the abnormal risk level of the measurement switch of the target area in real time according to the analysis result;

[0011] The strategy generation module is used to define the early warning control period and the emergency control period of the target area, and analyze the acquisition resource configuration of the target area in combination with the historical operation record of the measurement switch node.

[0012] The remote control module remotely controls the working mode of the calibration device and the Internet of Things terminal according to the acquisition resource configuration of the target area analyzed by the strategy generation module.

[0013] Preferably, the output end of the Internet of Things terminal and the calibration device is connected with the input end of the communication gateway through an industrial bus, and the communication gateway is used to collect the signal quality data, the connection state data of the Internet of Things terminal and the calibration state data of the calibration device, the signal quality data including the received signal strength and the transmission power, and the connection state data being a numerical value 1 or a numerical value 0, when the connection state data is the numerical value 1, indicating that the Internet of Things terminal or the calibration device is in a normal mode, and when the connection state data is the numerical value 0, indicating that the Internet of Things terminal or the calibration device is in an abnormal mode.

[0014] The output end of the measurement switch node is connected with the input end of the edge processing unit through a wireless transmission link, and the edge processing unit is used to collect the operation data of the measurement switch node, the operation data including the switch opening and closing state, the current amplitude and the voltage effective value.

[0015] The output end of the edge processing unit and the communication gateway is connected with the input end of the central server through a special communication channel, and the output end of the central server is connected with the input end of the Internet of Things terminal and the input end of the calibration device through a special communication channel.

[0016] Preferably, the information fusion processing module includes a state abnormality detection unit, a parameter traceability unit and a risk level determination unit.

[0017] The state abnormality detection unit calculates the total amount of switch operation that can be effectively collected in the target area and the total amount of switch operation actually operated in the target area according to the switch state data and the electrical parameter data collected by the measurement switch node, and determines whether there is a state abnormality in the target area according to the calculation result.

[0018] The parameter tracing unit calculates the real-time parameter untracing coefficient of the target area according to the judgment result of the state anomaly detection unit on whether the target area has a state anomaly;

[0019] The risk level determination unit determines the real-time measurement switch anomaly risk level of the target area according to the real-time operation characteristic value of the switch in the target area.

[0020] Preferably, the specific method for the state anomaly detection unit to calculate the total number of switch operations that can be effectively collected in the target area and the total number of switches actually operated in the target area is as follows:

[0021] A collection time point is randomly selected, and the switch state data and electrical parameter data collected by the measurement switch node are collected at an interval duration; the time point and the interval duration together constitute a temporary collection time period;

[0022] The sum of the on-off state cumulative value and the current amplitude cumulative value of the measurement switch node at the time point is calculated, and the product of the sum, the collection coverage range of the measurement switch node at the time point, and the interval duration is calculated to obtain the total number of switch operations that can be effectively collected in the target area within the time period;

[0023] The product of the voltage effective value, the environmental temperature and humidity, and the collection coverage range collected by the measurement switch node at the time point is calculated, and the product of the product and the interval duration is calculated to obtain the total number of switches actually operated in the target area within the time period;

[0024] If the total number of switch operations that can be effectively collected is greater than the total number of switches actually operated, it is considered that the target area has a state anomaly within the time period;

[0025] If the total number of switch operations that can be effectively collected is less than or equal to the total number of switches actually operated, it is considered that the target area does not have a state anomaly within the time period.

[0026] Preferably, the specific method for the parameter tracing unit to calculate the real-time parameter untracing coefficient of the target area is as follows:

[0027] When the target area has a state anomaly within the time period, the difference between the collectable operation total and the actual operation total is calculated, and the ratio between the difference and the collectable operation total is calculated to obtain the parameter untracing coefficient of the target area within the time period, which is greater than 0 and less than 1;

[0028] When the target area does not have a state anomaly within the time period, the parameter untracing coefficient of the target area within the time period is 0.

[0029] Preferably, the specific method for determining the real-time measurement switch abnormal risk level of the target area by the risk level determining unit is as follows:

[0030] The parameter untraceable coefficient of the target area in the time period is taken as the operation characteristic value of the target area in the time period, and the historical operation characteristic value of the target area in the historical time period is marked in the coordinate system, wherein the current time value represents the current time;

[0031] In the coordinate system, the nearest demarcation time point of the historical time period is searched, and the specific searching method is as follows: the nearest demarcation time point is taken as a specific time point, the operation characteristic value of the target area in the interval time length before the specific time point is 0, and the operation characteristic value of the target area in the time period from the specific time point to the current time value is greater than 0;

[0032] The interval time length between the specific time point and the current time value is calculated, and the ratio between the interval time length and the parameter continuous abnormal limit time length is calculated to obtain the measurement switch abnormal risk coefficient of the target area at the current time value.

[0033] Preferably, the strategy generating module comprises a regulation type identifying unit and a configuration analyzing unit.

[0034] The regulation type identifying unit identifies the regulation type of the target area at the current time value according to the real-time measurement switch abnormal risk coefficient of the target area, when the risk coefficient is greater than 0 and less than 0.8, identifies the regulation type of the target area at the current time value as early warning regulation, and when the risk coefficient is greater than or equal to 0.8 and less than or equal to 1, identifies the regulation type of the target area at the current time value as emergency regulation.

[0035] The configuration analyzing unit analyzes the collection resource configuration situation of the target area in the current time period according to the regulation type of the target area at the current time value and the operation record of the measurement switch node in the historical time period.

[0036] Preferably, the specific method for analyzing the collection resource configuration situation of the target area by the configuration analyzing unit is as follows:

[0037] When the identified regulation type is early warning regulation:

[0038] The total amount of switch operation that can be collected by the target area in the historical time period is calculated, the total amount of switches actually operated by the target area in the historical time period is determined, the ratio between the interval time length and the total amount of switches actually operated by the target area in the historical time period is calculated, and the signal quality adjustment value of the Internet of Things terminal at the current time value is obtained, wherein the signal attenuation represents the signal attenuation generated in the process of transmitting the operation data from the measurement switch node to the center server;

[0039] The total amount of switch operation that can be collected in the current time period is: the product of the signal quality adjustment value of the Internet of Things terminal at the current time value and the interval duration, and the amount of collection of the target area by the backup collection device at the current time value is 0, and the amount of collection of the target area by the measurement switch node is the total amount of switch operation that can be collected in the current time period;

[0040] When the identified regulation type is emergency regulation:

[0041] The amount of supplementary collection of the target area by the backup collection device at the current time value is a specific value, and the amount of collection of the target area by the measurement switch node is 0, wherein the signal attenuation represents the signal attenuation generated in the process of transmitting the collected data from the backup collection device to the central server.

[0042] Preferably, when the identified regulation type is early warning regulation, the remote control module remotely regulates the signal quality of the Internet of Things terminal through the central server at the current time value, and the regulated signal quality value is the signal quality adjustment value.

[0043] When the identified regulation type is emergency regulation, the working mode of the calibration device is regulated through the central server at the current time value, and the state data of the calibrated device after regulation is zero.

[0044] Preferably, the specific steps of constructing the measurement switch data three-dimensional collection network of the target area include: optimizing the deployment position of the measurement switch node according to the topological characteristics, switch distribution density and environmental condition characteristics of the target area, to ensure that the collection network covers all main switch nodes and key monitoring areas in the target area; and redundantly configuring the communication links of the communication gateway, edge processing unit and central server to form a dual-link communication architecture of the main link and backup link.

[0045] Compared with the prior art, the beneficial effects of the present application are:

[0046] The present application realizes full coverage collection of measurement switch data in the target area by constructing a three-dimensional collection network. The data collection network construction module optimizes node deployment based on topological characteristics and switch distribution density, and combines redundant communication link design to ensure the integrity of data collection and transmission reliability. The information fusion processing module comprehensively utilizes switch state data and electrical parameter data to analyze the distribution of operating characteristics, significantly improving the accuracy of abnormality detection. The state abnormality detection unit quickly locates state abnormalities by dynamically calculating the relationship between the total amount of collectable operation and the actual total amount of operation; and the parameter tracing unit further quantifies the untraced coefficient to provide a scientific basis for risk judgment.

[0047] The risk level determination unit introduces historical operation characteristic values and time dimension analysis, realizes dynamic quantification of abnormal risk levels, and solves the problem of high false alarm rate of the traditional threshold method. The strategy generation module intelligently identifies the regulation type according to the real-time risk coefficient, accurately divides the early warning regulation period and the emergency regulation period, and provides clear guidance for resource allocation. The configuration analysis unit dynamically adjusts the signal quality or enables the standby device in combination with the historical operation record, optimizes the utilization rate of the collection resource, and avoids resource waste.

[0048] The remote control module is linked with the strategy generation module to realize accurate regulation and control of the Internet of Things terminal and the calibration device. The early warning regulation adjusts the signal quality to improve the data collection efficiency, and the emergency regulation quickly switches to the standby device and closes the abnormal node to ensure the stability of the system in extreme conditions. In addition, the remote regulation function of the calibration device further ensures the accuracy of data collection. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The working principle diagram of the measurement switch data acquisition and analysis system described in the application;

[0050] Figure 2 The design diagram of the state abnormality detection unit calculation logic;

[0051] Figure 3 The design diagram of the parameter traceability unit calculation logic;

[0052] Figure 4 The design diagram of the risk level determination unit determination logic;

[0053] Figure 5 The design diagram of the configuration analysis unit analysis logic. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0055] Please refer to Figures 1-5 The application relates to a measurement switch data acquisition and analysis system, which comprises a data acquisition network construction module, an information fusion processing module, a strategy generation module and a remote control module. The specific implementation steps are as follows:

[0056] The data acquisition network construction module constructs a three-dimensional data acquisition network of the measurement switch of the target area based on the measurement switch nodes, the communication gateway, the edge processing unit, the central server, the calibration device and the Internet of Things terminal deployed in the target area. The specific steps are: according to the topological characteristics, the switch distribution density and the environmental condition characteristics of the target area, the deployment position of the measurement switch node is optimized to ensure that the acquisition network covers all the main switch nodes and the key monitoring areas in the target area; the communication links of the communication gateway, the edge processing unit and the central server are redundantly configured to form a dual-link communication architecture of the main link and the standby link.

[0057] The information fusion processing module analyzes the operation characteristic distribution of the measurement switch of the target area according to the switch state data and the electrical parameter data collected by the measurement switch node, and determines the abnormal risk level of the measurement switch of the target area in real time according to the analysis result.

[0058] The strategy generation module is used to define the early warning control period and the emergency control period of the target area, and analyze the acquisition resource configuration of the target area combined with the historical operation record of the measurement switch node.

[0059] The remote control module remotely controls the working mode of the calibration device and the Internet of Things terminal according to the acquisition resource configuration of the target area analyzed by the strategy generation module.

[0060] Embodiment 1:

[0061] The output end of the Internet of Things terminal and the calibration device in the system is connected with the input end of the communication gateway through the industrial bus, and the communication gateway is used to collect the signal quality data, the connection state data of the Internet of Things terminal and the calibration state data of the calibration device. Among them, the signal quality data includes the received signal strength and the transmission power, and the connection state data is in the form of a numerical value. When the connection state data is a numerical value 1, it indicates that the Internet of Things terminal or the calibration device is in a normal mode and can normally perform data transmission or calibration operation; when the connection state data is a numerical value 0, it indicates that the Internet of Things terminal or the calibration device is in an abnormal mode, which may cause communication interruption, equipment failure and the like, and the system needs to identify and handle the abnormality at this time.

[0062] The output end of the measurement switch node is connected with the input end of the edge processing unit through the wireless transmission link, and the edge processing unit undertakes the acquisition task of the operation data of the measurement switch node. These operation data specifically include the switch opening and closing state, the current amplitude and the voltage effective value. The switch opening and closing state is used to reflect whether the working state of the switch is closed or open, and the current amplitude and the voltage effective value are important indicators for measuring whether the electrical parameters are normal. Through the acquisition and analysis of these data, the running condition of the measurement switch can be mastered in real time.

[0063] The output end of the edge processing unit and the communication gateway is connected with the input end of the center server through a special communication channel. The special communication channel has high stability and security, which can ensure the accuracy and integrity of data during transmission, and avoid data loss or interference. The center server, as the data processing core of the system, its output end is connected with the input end of the Internet of Things terminal and the input end of the calibration device through a special communication channel, so as to realize the remote control and management of the Internet of Things terminal and the calibration device.

[0064] During data transmission, the industrial bus serves as a connecting bridge between the Internet of Things terminal, the calibration device and the communication gateway, has reliable physical connection and standardized communication protocol, and can ensure stable data transmission. The wireless transmission link provides a flexible connection method for data interaction between the measurement switch node and the edge processing unit, which is suitable for the scene where the switch nodes are distributed relatively dispersed in the target area, and can realize real-time collection of switch node data in different positions.

[0065] The communication gateway plays a role of intermediate hub in the whole data acquisition network. It is responsible for collecting signal quality data and connection state data of the Internet of Things terminal, as well as calibration state data of the calibration device, and needs to preliminarily process and integrate these data, and then transmit them to the center server through a special communication channel. After receiving the data from the edge processing unit and the communication gateway, the center server will comprehensively analyze and process all the data to form a comprehensive understanding of the operation of the measurement switch in the target area.

[0066] When the center server needs to regulate and control the Internet of Things terminal or the calibration device, it sends control instructions to the corresponding device through a special communication channel. For example, when the signal quality of the Internet of Things terminal is found to be poor, the center server can send instructions to adjust the signal quality, and the Internet of Things terminal will execute corresponding operations after receiving the instructions to optimize the signal transmission effect; when the calibration device needs to calibrate or adjust the working mode, the center server will also send corresponding instructions to ensure that the calibration device can work normally and ensure the accuracy of the measurement data.

[0067] The whole data acquisition and transmission process forms a complete closed loop system. The measurement switch node collects switch state and electrical parameter data in real time, and sends them to the edge processing unit through the wireless transmission link; the edge processing unit preliminarily processes the data and transmits them to the center server through a special communication channel; the communication gateway collects data of the Internet of Things terminal and the calibration device, and also transmits them to the center server through a special communication channel; the center server analyzes and processes all the data, and sends regulation and control instructions to the Internet of Things terminal and the calibration device through a special communication channel according to needs, to realize real-time monitoring and management of the whole system.

[0068] In this system, the connection between components and data transmission follow strict specifications and protocols, ensuring the system can run stably and reliably. Whether it is an industrial bus, wireless transmission link or dedicated communication channel, it is carefully designed and configured to adapt to the environmental conditions and data transmission requirements of the target area. At the same time, the system monitors the data collection, processing and transmission process in real time, and can identify and handle abnormal situations in time to ensure the normal operation of the system and the accuracy of the data.

[0069] For example, when the communication gateway detects that the connection state data of the Internet of Things terminal is 0, it will immediately transmit this abnormal information to the central server. After receiving the information, the central server will analyze the possible causes of the fault and take appropriate measures, such as attempting to re-establish the connection, sending fault repair instructions, etc. If the fault cannot be repaired automatically, the central server will also issue a warning signal to remind relevant personnel to conduct manual troubleshooting and maintenance.

[0070] Similarly, when the edge processing unit collects abnormal switch opening and closing state, current amplitude or voltage effective value, it will immediately transmit the abnormal data to the central server. Through analysis of the abnormal data, the central server determines whether the measuring switch has a fault or abnormal operation, and takes appropriate measures according to the degree of abnormality, such as adjusting the collection frequency, starting the standby measuring switch node, etc., to ensure effective monitoring of the operation of the measuring switch in the target area.

[0071] Example 2:

[0072] The information fusion processing module includes a state anomaly detection unit, a parameter traceability unit and a risk level determination unit, which work together to analyze and determine the risk of the measuring switch in the target area. The state anomaly detection unit calculates the total number of switches that can be effectively collected and the total number of switches that are actually running in the target area based on the switch state data and electrical parameter data collected by the measuring switch node, and then determines whether there is a state anomaly. Specifically, a collection time point is randomly selected, and the data of the measuring switch node is collected continuously at a fixed interval. At this time point, the sum of the opening and closing state cumulative value and the current amplitude cumulative value of the measuring switch node is calculated, and then multiplied by the collection coverage range and interval at this time point to obtain the total number of switches that can be effectively collected in this time period. At the same time, the product of the voltage effective value, environmental temperature and humidity, and collection coverage range collected at this time point is multiplied by the interval to obtain the total number of switches that are actually running. If the total number of switches that can be effectively collected is greater than the total number of switches that are actually running, it means that part of the switch running state has not been effectively collected, i.e. there is a state anomaly in the target area during this time period; if the former is less than or equal to the latter, it is considered that the collection is normal and there is no state anomaly.

[0073] The parameter tracing unit calculates the real-time parameter untracing coefficient according to the judgment result of the state anomaly detection unit. When there is a state anomaly in the target area, the difference between the collectable running total and the actual running total is calculated, and then the difference is compared with the collectable running total to obtain the ratio, which is the parameter untracing coefficient. The coefficient value ranges from 0 to 1, reflecting the proportion of parameters that have not been effectively traced. If there is no state anomaly, the untracing coefficient is 0, indicating that all parameters have been effectively collected and traced.

[0074] The risk level determination unit determines the abnormal risk level according to the real-time running characteristic value of the switch in the target area. The specific operation is to take the parameter untracing coefficient of the current time period as the running characteristic value, and mark the historical running characteristic value in the historical time period in the coordinate system, with the current time value as the identification of the current time. Find the nearest dividing time point in the historical time period in the coordinate system. This time point needs to meet the following conditions: the running characteristic value in the previous interval is 0, and the running characteristic value in the time period from this time point to the current time value is greater than 0. After determining this specific time point, calculate the interval length between it and the current time value, and then compare the length with the parameter continuous anomaly limit time to obtain the measurement switch abnormal risk coefficient of the current time value.

[0075] In actual operation, the time point selection and interval length setting of the state anomaly detection unit need to be determined according to the switch distribution density and data collection requirements of the target area to ensure the comprehensiveness and timeliness of data collection. For example, for areas with dense switches and frequent running state changes, the interval length may need to be shortened to increase the data collection frequency to more accurately capture the changes in switch running state. The collection coverage is related to the deployment location of the measurement switch node and the signal coverage capability. By reasonably optimizing the node deployment, the collection coverage can be expanded, and the calculation accuracy of the collectable switch running total can be improved.

[0076] When the state anomaly detection unit determines that there is a state anomaly, the untracing coefficient calculated by the parameter tracing unit can intuitively reflect the degree of data loss. For example, if the untracing coefficient is 0.3, it means that 30% of the switch running parameters have not been effectively collected and traced. At this time, the system needs to focus on the source of these untraced parameters, which may be caused by measurement switch node failure, signal transmission obstruction, or insufficient collection coverage. Through further investigation, the specific problem can be determined, providing a basis for subsequent fault repair or system optimization.

[0077] In the risk level determination unit, the coordinate system annotation method helps to intuitively present the trend of the historical operation characteristic value. By searching for the recent boundary time point, the specific time of the abnormal start can be determined, and then the duration of the abnormality is calculated. The parameter duration abnormality limit duration is a threshold value set according to the system design requirements and actual operation experience, which is used to measure the influence degree of the abnormal duration on the system risk. For example, if the parameter duration abnormality limit duration is 12 hours, and the interval duration between the specific time point and the current time value is 8 hours, the risk coefficient is 8 / 12≈0.67, indicating that the current risk is in a medium risk state, and corresponding warning measures need to be taken.

[0078] In the whole information fusion processing process, the data interaction between each unit is close, and the calculation result of the state abnormality detection unit provides input for the parameter tracing unit, and the output of the parameter tracing unit is also an important basis for the risk level determination unit. Through real-time collection, calculation and analysis, the system realizes dynamic monitoring of the operation state of the measurement switch in the target area, discovers potential abnormal risks in time, and provides accurate decision support for subsequent strategy generation and remote control.

[0079] In addition, the collection of environmental temperature and humidity and other parameters also has an impact on the calculation of the actual operation total. For example, when the environmental temperature is too high or the humidity is too large, it may affect the normal operation of the switch, causing the actual operation total of the switch to change. At this time, by collecting environmental parameters and including them in the calculation, the calculation of the actual operation total can be more accurate, thereby improving the accuracy of the state abnormality judgment.

[0080] Example 3:

[0081] The strategy generation module is composed of a regulation type identification unit and a configuration analysis unit, which cooperate with each other to identify the regulation type of the target area and analyze the configuration of the collected resources. The regulation type identification unit identifies the regulation type of the current time value according to the real-time measurement switch abnormal risk coefficient of the target area. When the risk coefficient is greater than 0 and less than 0.8, it is determined to be a pre-warning regulation period, at which time the abnormal risk of the measurement switch in the target area is within a controllable range, but measures need to be taken in advance to prevent the risk from further expanding; when the risk coefficient is greater than or equal to 0.8 and less than or equal to 1, it is identified as an emergency regulation period, indicating that the abnormal risk of the target area is high, and the emergency mechanism needs to be started immediately for processing.

[0082] The configuration analysis unit analyzes the collection resource configuration of the target area in the current time period according to the regulation type determined by the regulation type identification unit and the operation record of the measurement switch node in the historical time period. Specifically, when the regulation type is early warning regulation, the total amount of switch operation that can be collected in the target area in the historical time period is first calculated. The calculation is based on the total amount of switch operation in the historical time period and is processed in combination with the interval length. The signal quality adjustment value of the Internet of Things terminal at the current time value is obtained by calculation. The signal attenuation here refers to the loss of signal strength in the process of transmitting the operation data from the measurement switch node to the center server. The determination of the signal quality adjustment value aims to reduce signal attenuation and improve the effectiveness of data transmission by optimizing the signal quality of the Internet of Things terminal.

[0083] In the early warning regulation state, the total amount of switch operation that can be collected in the target area in the current time period is obtained by multiplying the signal quality adjustment value of the Internet of Things terminal at the current time value by the interval length. At this time, the supplementary collection amount of the backup collection device for the target area is 0, and the measurement switch node undertakes the main collection task, and the collection amount of the target area by the measurement switch node is the total amount of switch operation that can be effectively collected in the current time period. This configuration mode not only guarantees the basic demand of data collection, but also reasonably utilizes existing resources and avoids waste of resources.

[0084] When the regulation type is emergency regulation, the collection resource configuration mode changes. At the current time value, the supplementary collection amount of the backup collection device for the target area is set to a specific value, which is determined in advance according to system design requirements and emergency processing needs, aiming to quickly supplement the missing data due to the failure of the measurement switch node to normally collect data. At this time, the collection amount of the target area by the measurement switch node is 0, which may be due to the serious failure of the measurement switch node or the high risk state, and the measurement switch node needs to be temporarily stopped to avoid further problems. The signal attenuation here refers to the signal loss in the process of transmitting the collected data from the backup collection device to the center server. In the emergency regulation, the signal transmission of the backup collection device needs to be monitored and optimized to ensure the accuracy and integrity of the emergency collection data.

[0085] In practical application, the judgment of the risk coefficient by the regulation type identification unit needs to be combined with the specific situation of the target area. For example, different target areas may have different threshold values of risk coefficient due to factors such as switch importance and system safety requirements. However, in this embodiment, 0.8 is uniformly used as the dividing point between early warning regulation and emergency regulation to ensure the consistency and operability of the system judgment standard. The selection of the historical time period also affects the accuracy of the configuration analysis. Generally, according to the periodic characteristics of switch operation and data statistical requirements of the target area, a time period that can reflect the recent operation trend is selected as a reference, such as the last 24 hours, the last week, etc.

[0086] When in the early warning regulation stage, the calculation of the Internet of Things terminal signal quality adjustment value needs to consider the signal attenuation in the historical data and the current risk coefficient. For example, if the signal attenuation is serious in the historical time period, resulting in the total amount of switch operation that can be collected being lower than the actual total amount, at this time, by increasing the signal quality adjustment value, the transmission power of the Internet of Things terminal can be enhanced or the receiving sensitivity can be optimized, thereby expanding the signal coverage range and improving the effectiveness of data collection. At the same time, the operation records of the measurement switch node in the historical time period, such as the switch on-off frequency, current and voltage fluctuation, etc., will also affect the calculation of the collection amount in the current time period. By analyzing these records, the collection capacity of the measurement switch node in the current time period can be predicted, and the collection resources can be reasonably configured.

[0087] In the emergency regulation state, the specific supplementary collection amount of the backup collection device needs to be determined according to the total amount of switches in the target area and the emergency handling demand. For example, if there are 100 switch nodes in the target area, the supplementary collection amount of the backup collection device may be set to 80 in the emergency regulation, to ensure that the data of most switch nodes can be collected in time to support emergency decision-making. At the same time, it needs to be noted whether the communication link between the backup collection device and the center server is smooth, to avoid distortion or loss of collected data due to excessive signal attenuation. When switching to the backup collection device for collection, the system also needs to uniformly convert the format and standard of the collected data to ensure consistency with the data collected by the measurement switch node, so as to facilitate comprehensive analysis and processing by the center server.

[0088] The working process of the strategy generation module is a dynamic adjustment process. With the passage of time and the change of the running state of the target area, the regulation type may be converted between early warning regulation and emergency regulation, and the collection resource configuration will be adjusted accordingly. For example, when the risk coefficient gradually decreases to below 0.8 after the implementation of the emergency regulation measures, the system will automatically switch to the early warning regulation mode, restore the collection task of the measurement switch node, and gradually reduce the supplementary collection amount of the backup collection device. This dynamic adjustment mechanism can flexibly configure the collection resources according to the actual situation, and improve the adaptability and reliability of the system.

[0089] The analysis of the collection resource configuration also needs to consider the environmental conditions and equipment running state of the target area. For example, when the target area is in severe weather or the equipment is aging, etc., it may cause signal attenuation to intensify or the probability of measurement switch node failure to increase, at this time, the signal quality adjustment value needs to be appropriately increased or the supplementary collection amount of the backup collection device needs to be increased in the configuration analysis, to ensure the stability of data collection.

[0090] The policy generation module realizes dynamic configuration and optimization of the target area collection resource by coordinating the work of the regulation type identification unit and the configuration analysis unit, can adjust the collection strategy in time according to the change of the measurement switch abnormal risk level, ensure the effectiveness and reliability of data collection, provide accurate policy basis for the regulation operation of the remote control module, and thus guarantee the stable operation of the entire measurement switch data collection and analysis system.

[0091] Embodiment 4:

[0092] The remote control module undertakes the key task of remotely regulating the equipment according to the analysis results of the policy generation module in the entire system, and its work flow is closely related to the regulation type, which needs to be understood in combination with the specific scene.

[0093] When the policy generation module identifies the regulation type as early warning regulation, the remote control module needs to regulate the signal quality of the Internet of Things terminal through the center server. For example, assuming that the signal of a measurement switch node in the target area is attenuated due to environmental interference, so that the total amount of switch operation that can be effectively collected is less than the actual total amount of operation, the state abnormality detection unit determines that there is a state abnormality, the parameter tracing unit calculates that the non-tracing coefficient is 0.2, and the risk level determination unit obtains that the risk coefficient is 0.5 (between 0 and 0.8), at this time the regulation type identification unit determines early warning regulation. After receiving the analysis results of the policy generation module, the remote control module, the center server will calculate the signal quality adjustment value of the Internet of Things terminal at the current time value according to the historical operation record, such as adjusting the received signal strength from-70dBm to-60dBm and increasing the transmission power from 10dBm to 15dBm. During the regulation process, the center server sends the regulation instruction to the Internet of Things terminal through the special communication channel, and the Internet of Things terminal automatically adjusts the internal signal processing parameters after receiving the instruction, and completes the signal quality optimization. In this process, the communication gateway will continuously collect the signal quality data and connection state data of the Internet of Things terminal, if the adjusted received signal strength is stable at-60dBm and the connection state data is 1, it indicates that the regulation is effective, and the operation data of the measurement switch node can be transmitted to the edge processing unit through the optimized signal link, and then transmitted to the center server through the special communication channel, to ensure the integrity of the collected data.

[0094] When the regulation type is emergency regulation, the regulation object of the remote control module becomes the calibration device, and the working mode thereof needs to be adjusted to a state with zero state data. For example, if the measurement switch abnormal risk coefficient of the target area reaches 0.9 (greater than or equal to 0.8), the strategy generation module determines that the emergency regulation period is entered, the configuration analysis unit determines to enable the backup acquisition device, and the measurement switch node stops acquisition. At this time, the remote control module needs to regulate the calibration device to avoid interference of the calibration process on the emergency acquisition data. In specific operation, the center server sends a stop working instruction to the calibration device through a special communication channel, and the calibration device receives the instruction, closes the internal calibration circuit and signal output interface, and makes the output calibration state data zero. For example, the calibration device originally outputs a calibration voltage value of 5V, switches to standby mode after receiving the instruction, and the output voltage drops to 0V. At the same time, the calibration state data collected by the communication gateway changes from 1 (normal mode) to 0 (abnormal mode), indicating that the calibration device has stopped working. At this time, the backup acquisition device acquires data of the target area with a specific supplementary acquisition amount (such as 50 switch nodes), and the acquisition data is transmitted to the center server through a wireless transmission link or a special communication channel. The remote control module needs to monitor the signal attenuation of the backup acquisition device, and if it is found that signal attenuation causes data distortion, the transmission effect can be optimized by adjusting the transmission power of the backup acquisition device or switching the communication link.

[0095] In actual application, the regulation process of the remote control module needs to follow strict timing and logic. For example, in the pre-warning regulation scene, after the signal quality adjustment value of the Internet of Things terminal is set to a specific parameter, the center server will first send a pre-adjustment instruction, wait for the Internet of Things terminal to return an acknowledgement signal, and then send a formal regulation instruction, to avoid regulation failure due to missing instructions. Assuming that a certain Internet of Things terminal does not respond in time due to temporary interruption of the communication link after receiving the signal strength adjustment instruction, the center server will resend the instruction within a set time (such as 5 seconds), and if there is no response after three times of retransmission, it is determined that the Internet of Things terminal may be in an abnormal mode, triggering a fault pre-warning process to notify maintenance personnel to troubleshoot the equipment fault.

[0096] The regulation of the calibration device in the emergency regulation needs to be synchronized with the start of the backup acquisition device. For example, when the center server sends a stop working instruction to the calibration device, it will also send a start instruction to the backup acquisition device, to ensure that the backup acquisition device can immediately take over the data acquisition task after the measurement switch node stops acquisition, avoiding a data acquisition blank period. After the backup acquisition device starts, if signal attenuation causes a large deviation of the voltage effective value of the acquisition data, the remote control module can adjust the signal transmission parameters of the backup acquisition device through the center server, such as increasing the signal amplification gain or switching to a communication frequency band with stronger anti-interference capability, to ensure the accuracy of the emergency acquisition data.

[0097] In addition, the remote control module also needs to have a real-time feedback mechanism for the regulation results. For example, in the early warning regulation, after adjusting the signal quality of the Internet of Things terminal, the center server will continue to receive the signal quality data collected by the communication gateway. If it is found that the adjusted signal strength still does not reach the expected value (such as the adjusted received signal strength is only improved to -65dBm, which does not reach the target of -60dBm), the signal quality adjustment value will be recalculated, and the regulation instruction will be sent again until the signal quality meets the requirements. In emergency regulation, if the state data of the calibration device does not become zero in time (such as 10 seconds after the instruction is sent, the calibration state data collected by the communication gateway is still 1), the remote control module will check whether the communication link is smooth or whether the calibration device has a hardware failure, and take appropriate measures according to the check results, such as restarting the calibration device or switching to a backup calibration channel.

[0098] The interaction between the remote control module and other modules is also crucial. For example, when the strategy generation module analyzes the collected resource configuration, it will pass the regulation type and signal attenuation parameters to the remote control module, which will determine the regulation object and regulation parameters according to these parameters; the information fusion processing module will feed back the real-time risk coefficient and untraceable coefficient to the remote control module, providing a basis for regulation decision-making. Assuming that the risk coefficient of a target area rises from 0.6 to 0.85, the remote control module will switch from early warning regulation to emergency regulation according to the instructions of the strategy generation module, stop the measurement switch node collection, start the backup collection equipment, and regulate the calibration device to stop working. The entire process needs to be completed within a set time (such as 30 seconds) to ensure the timeliness of emergency response.

[0099] Through close cooperation with the strategy generation module, the remote control module implements precise device regulation for different regulation types, realizes remote management of the signal quality of the Internet of Things terminal and the working mode of the calibration device, and ensures the stable operation of the data collection system under different risk levels. Its implementation process covers multiple links such as instruction sending, device response, state monitoring, and result feedback, and needs to dynamically adjust the regulation strategy in combination with the specific scene to adapt to the running state changes of the measurement switch in the target area.

[0100] Example 5:

[0101] Building a three-dimensional data collection network for the measurement switch in the target area needs to be optimized and deployed in combination with the actual characteristics of the target area and configured with redundant communication links. Taking an industrial park as an example, the implementation method is described in detail. There are multiple workshops in the park, the number and distribution density of switch nodes in each workshop are different, and there are environmental conditions such as electromagnetic interference, so it is necessary to build a collection network that covers all main switch nodes and key monitoring areas.

[0102] According to the characteristics of the park topology, such as the layout of the workshop, the location of the building, and the distribution density of the switch, the deployment position of the measurement switch node is determined. For example, in workshop A, there are 30 switch nodes inside, mainly distributed near the production line equipment, and this workshop is a key production area, which needs to ensure that all switch nodes are covered by the collection network. By analyzing the workshop floor plan, 2 measurement switch nodes are deployed in the dense area of switch node distribution (such as the middle section of the production line), and 1 is deployed in the edge area, and the signal coverage range (such as radius 50 meters) of the node is used for overlapping coverage design to ensure no collection blind area. At the same time, considering the environmental condition characteristics, there is strong electromagnetic interference in this workshop, so the wireless transmission link equipment with strong anti-interference ability is selected, and the measurement switch node is installed away from the interference source (such as large motor) to reduce signal attenuation.

[0103] For the communication link of the communication gateway, the edge processing unit and the center server, a redundant configuration is adopted to form a double-link communication architecture. The center server is deployed in the network room of the park, and a communication gateway and an edge processing unit are installed near workshop A. The main link uses an optical fiber communication channel, and the standby link uses a wireless microwave communication channel. When the main link fails due to fiber damage or other reasons, the communication link automatically switches to the standby link to ensure uninterrupted data transmission. For example, on a certain day, the main optical fiber link was cut off due to construction accident, and the standby wireless microwave link was immediately started, and the switch opening and closing state, current amplitude and other data collected by the edge processing unit were transmitted to the center server through the standby link, ensuring the normal operation of the system.

[0104] When optimizing the deployment position of the measurement switch node, the power supply mode and maintenance convenience of the node also need to be considered. For example, the measurement switch nodes deployed in workshop A are powered by industrial power adapters and installed on wall brackets for easy maintenance, avoiding node offline due to power supply problems. At the same time, a unique identification number is set for each measurement switch node to facilitate management and data tracing in the center server.

[0105] The redundant configuration of the communication link needs to be tested and debugged. After building the double-link architecture, the bandwidth, delay, packet loss rate and other parameters of the main link and standby link are tested. For example, the bandwidth of the main optical fiber link is 1000 Mbps, the delay is less than 1 ms, and the packet loss rate is 0; the bandwidth of the standby wireless microwave link is 100 Mbps, the delay is less than 5 ms, and the packet loss rate is less than 1%. Ensure that the standby link can meet the data transmission requirements when the main link fails. At the same time, set the link switching threshold, such as when the packet loss rate of the main link exceeds 5% continuously, automatically switch to the standby link, and display the link switching state in the monitoring interface of the center server.

[0106] In key monitoring areas such as the power distribution station in the park, in addition to deploying conventional measurement switch nodes, redundant nodes are added for double coverage. There are 10 high-voltage switch nodes in the power distribution station, which are key monitoring objects. Two measurement switch nodes are deployed in this area, and each node can independently collect the operation data of all high-voltage switches. When one of the nodes fails, the other node can still work normally, ensuring the continuity of switch data collection in the power distribution station.

[0107] The installation location of the communication gateway needs to consider the communication distance with the Internet of Things terminal, calibration device and edge processing unit. In Workshop A, the communication gateway is installed in the central position of the workshop, connected with the nearby Internet of Things terminal (such as environmental monitoring equipment) and calibration device through industrial bus, and the communication distance is controlled within 50 meters to reduce the signal loss in the process of industrial bus transmission. The edge processing unit is connected with the measurement switch node through the wireless transmission link, and the node position is adjusted according to the wireless signal strength to ensure the signal strength above -70dBm, ensuring the stability of data transmission.

[0108] The central server as the core of the collection network needs to have high availability and scalability. In the network room of the park, the central server adopts dual-machine hot standby architecture, when the main server fails, the standby server automatically takes over the business, ensuring the system runs uninterruptedly 7x24 hours. At the same time, the central server reserves multiple special communication channel interfaces, which is convenient for the access of new measurement switch nodes, communication gateways and other devices when the park is expanded in the future.

[0109] During the construction of the collection network, overall debugging and optimization are also needed. For example, after the deployment in Workshop A is completed, test instructions are sent through the central server to check the communication status and data collection accuracy of each measurement switch node. For nodes with poor signal quality, adjust their installation position or replace the antenna to improve the signal strength. Pressure test the communication link to simulate a large amount of data transmission scenario to ensure that the dual-link architecture can still work stably under high load.

[0110] In addition, corresponding protection measures are taken according to different environmental conditions. For example, in the humid Workshop B, the measurement switch node and the communication gateway use waterproof and moisture-proof shell to prevent the equipment from being damaged due to humidity; in the high-temperature Workshop C, the equipment is installed in a well-ventilated position and additional cooling devices are added to avoid affecting the performance of the equipment due to high temperature.

[0111] Through the above steps, the three-dimensional measurement switch data collection network in the target area is completed, realizing full coverage of all main switch nodes and key monitoring areas in the park, and improving the reliability and stability of the system through the redundant configuration of the communication link, providing a solid foundation for subsequent data collection, information fusion processing, etc.

[0112] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, and does not necessarily require or imply any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0113] While embodiments of the present application have been shown and described with reference to particular embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. A metrology switch data acquisition and analysis system, characterized by: The system comprises a data acquisition network construction module, an information fusion processing module, a strategy generation module and a remote control module; The data acquisition network construction module constructs a three-dimensional data acquisition network of the measuring switch of the target area based on the measuring switch nodes, the communication gateway, the edge processing unit, the central server, the calibration device and the Internet of Things terminal deployed in the target area; The information fusion processing module analyzes the operating characteristic distribution of the measuring switch of the target area according to the switch state data and the electrical parameter data collected by the measuring switch nodes, and determines the abnormal risk level of the measuring switch of the target area in real time according to the analysis result; The strategy generation module is used to define the early warning control period and the emergency control period of the target area, and analyze the acquisition resource configuration of the target area in combination with the historical operation record of the measuring switch nodes; The remote control module remotely controls the working mode of the calibration device and the Internet of Things terminal according to the acquisition resource configuration of the target area analyzed by the strategy generation module; The information fusion processing module comprises a state anomaly detection unit, a parameter traceability unit and a risk level determination unit; The state anomaly detection unit calculates the total quantity of the switch that can be effectively collected and the total quantity of the switch actually operated in the target area according to the switch state data and the electrical parameter data collected by the measuring switch nodes, and determines whether there is a state anomaly in the target area according to the calculation result; The parameter traceability unit calculates the real-time parameter untraceability coefficient of the target area according to the determination result of the state anomaly detection unit on whether there is a state anomaly in the target area; The risk level determination unit determines the real-time abnormal risk level of the measuring switch of the target area according to the real-time operating characteristic value of the switch in the target area. When there is a state anomaly in the target area, the difference between the collectable operating total quantity and the actual operating total quantity is calculated, and the ratio of the difference to the collectable operating total quantity is the parameter untraceability coefficient.

2. The metrology switch data acquisition and analysis system of claim 1, wherein, The output ends of the Internet of Things terminal and the calibration device are connected to the input end of the communication gateway through an industrial bus, and the communication gateway is used to collect the signal quality data, the connection state data of the Internet of Things terminal and the calibration state data of the calibration device, the signal quality data including the received signal strength and the transmission power, and the connection state data being a numerical value 1 or a numerical value 0, when the connection state data is the numerical value 1, indicating that the Internet of Things terminal or the calibration device is in a normal mode, and when the connection state data is the numerical value 0, indicating that the Internet of Things terminal or the calibration device is in an abnormal mode; The output end of the measuring switch node is connected to the input end of the edge processing unit through a wireless transmission link, and the edge processing unit is used to collect the operating data of the measuring switch node, including the switch opening and closing state, the current amplitude and the voltage effective value; The output ends of the edge processing unit and the communication gateway are connected to the input end of the central server through a special communication channel, and the output end of the central server is connected to the input end of the Internet of Things terminal and the input end of the calibration device through a special communication channel.

3. The metrology switch data acquisition and analysis system of claim 1, wherein: The specific method for the state anomaly detection unit to calculate the total amount of switch operation that can be effectively collected in the target area and the total amount of switch operation actually operated in the target area is: A collection time point is randomly selected, and switch state data and electrical parameter data collected by the measurement switch node are collected at an interval length; the time point and the interval length jointly constitute a temporary collection time period; The sum of the cumulative value of the on-off state of the measurement switch node at the time point and the cumulative value of the current amplitude is calculated, and the product of the sum, the collection coverage range of the measurement switch node at the time point, and the interval length is calculated to obtain the total amount of switch operation that can be effectively collected in the target area in the time period; The product of the three parameters of the voltage effective value, the environmental temperature and humidity, and the collection coverage range collected by the measurement switch node at the time point is calculated, and the product of the product and the interval length is calculated to obtain the total amount of switch operation actually operated in the target area in the time period; If the total amount of switch operation that can be effectively collected is greater than the total amount of switch operation actually operated, it is considered that the target area has a state anomaly in the time period; If the total amount of switch operation that can be effectively collected is less than or equal to the total amount of switch operation actually operated, it is considered that the target area does not have a state anomaly in the time period.

4. The metrology switch data acquisition and analysis system of claim 3, wherein: The specific method for the parameter traceability unit to calculate the real-time parameter untraceability coefficient of the target area is: When the target area has a state anomaly in the time period, the difference between the collectable operation total amount and the actual operation total amount is calculated, and the ratio between the difference and the collectable operation total amount is calculated to obtain the parameter untraceability coefficient of the target area in the time period, which is greater than 0 and less than 1; When the target area does not have a state anomaly in the time period, the parameter untraceability coefficient of the target area in the time period is 0.

5. The metrology switch data acquisition and analysis system of claim 4, wherein: The specific method for the risk level determination unit to determine the real-time measurement switch anomaly risk level of the target area is: The parameter untraceability coefficient of the target area in the time period is taken as the operation characteristic value of the target area in the time period, and the historical operation characteristic values of the target area in the historical time period are marked in a coordinate system, wherein the current time value represents the current time; In the coordinate system, the nearest boundary time point of the historical time period is searched, and the specific searching method is that the nearest boundary time point is taken as a specific time point, the operation characteristic value of the target area in the interval length before the specific time point is 0, and the operation characteristic value of the target area in the time period from the specific time point to the current time value is greater than 0; The interval length between the specific time point and the current time value is calculated, and the ratio between the interval length and the parameter continuous anomaly limit length is calculated to obtain the measurement switch anomaly risk coefficient of the target area at the current time value.

6. The metrology switch data acquisition and analysis system of claim 5, wherein: The strategy generation module includes a regulation type identification unit and a configuration analysis unit; The control type identification unit identifies the control type of the target area at the current time value according to the real-time measurement switch abnormal risk coefficient of the target area; when the risk coefficient is greater than 0 and less than 0.8, the control type of the target area at the current time value is identified as early warning control; when the risk coefficient is greater than or equal to 0.8 and less than or equal to 1, the control type of the target area at the current time value is identified as emergency control. The configuration analysis unit analyzes the collection resource configuration situation of the target area in the current time period according to the control type of the target area at the current time value and the operation record of the measurement switch node in the historical time period.

7. The metrology switch data acquisition and analysis system of claim 6, wherein: The specific method for the configuration analysis unit to analyze the collection resource configuration situation of the target area is as follows: When the identified control type is early warning control: the total amount of switch operation that can be collected in the historical time period of the target area is calculated, the ratio between the total amount of switch operation actually operated by the target area in the historical time period and the interval length is calculated to obtain the signal quality adjustment value of the Internet of Things terminal at the current time value, wherein the signal attenuation represents the signal attenuation generated in the process of transmitting the operation data from the measurement switch node to the center server; the total amount of switch operation that can be collected in the current time period of the target area is the product of the signal quality adjustment value of the Internet of Things terminal at the current time value and the interval length, at the current time value, the supplementary collection amount of the target area by the backup collection device is 0, and the collection amount of the target area by the measurement switch node is the total amount of switch operation that can be collected in the current time period; When the identified control type is emergency control: at the current time value, the supplementary collection amount of the target area by the backup collection device is a specific value, and the collection amount of the target area by the measurement switch node is 0, wherein the signal attenuation represents the signal attenuation generated in the process of transmitting the collection data from the backup collection device to the center server.

8. The metrology switch data acquisition and analysis system of claim 7, wherein: When the identified control type is early warning control, the remote control module remotely controls the signal quality of the Internet of Things terminal through the center server at the current time value, and the signal quality value after the control is the signal quality adjustment value; When the identified control type is emergency control, the remote control module remotely controls the working mode of the calibration device through the center server at the current time value, and the state data of the calibration device after the control is zero.

9. The metrology switch data acquisition and analysis system of claim 1, wherein: The specific steps for the control unit to construct the measurement switch data three-dimensional collection network of the target area include: optimizing the deployment position of the measurement switch node according to the topological characteristics, switch distribution density and environmental condition characteristics of the target area to ensure that the collection network covers all main switch nodes and key monitoring areas in the target area; redundantly configuring the communication links of the communication gateway, edge processing unit and center server to form a dual-link communication architecture of the main link and the backup link.

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