GIS settlement prediction method and early warning system

Abstract

The invention belongs to the technical field of settlement monitoring, and particularly relates to a GIS settlement prediction method and an early warning system. The method comprises the following steps: S1, acquiring settlement monitoring data through a settlement monitoring system; s2, inputting the settlement monitoring data obtained in S1 into a trained settlement prediction model to obtain settlement prediction data; the settlement prediction model comprises a prediction model which takes GIS settlement data collected by a settlement monitoring system in a set time period as input and takes a GIS settlement trend actually measured within a certain time period after the set time period as output; the settlement prediction data comprises the GIS settlement trend. According to the method, the relationship between the GIS historical settlement data and the GIS settlement data at the future moment is determined by establishing the settlement prediction model, the settlement data at the future moment is predicted through the historical settlement data, advanced prediction of GIS settlement is achieved, and settlement early warning is conducted according to the output result of the settlement prediction model. The GIS settlement early warning method and device solve the technical problem that in the prior art, advanced prediction of GIS settlement cannot be achieved, and settlement early warning is not timely.

Description

Technical Field

[0001] The invention belongs to the technical field of settlement monitoring, and in particular relates to a GIS settlement prediction method and an early warning system. Background Art

[0002] As the number of GIS equipment put into operation continues to grow, the detection, monitoring and diagnosis of various common faults in operation have become a research hotspot. Many scientific research institutions and equipment manufacturing companies at home and abroad have actively carried out related research work, achieved certain results, and completed the development of some detection and monitoring devices.

[0003] At present, the monitoring equipment used in foundation settlement monitoring at home and abroad include: laser settlement deflectometer, measuring robot, settlement monitoring system, etc. The laser settlement deflectometer test system consists of multiple laser settlement deflectometers installed at different measuring points, adjacent to each other, one of which is installed at a fixed point as a reference point, and the components of each measuring point are connected in series. By measuring the height difference between the measuring point sensor and the adjacent point sensor, the settlement change relative to the reference point is calculated.

[0004] The measuring robot, also known as the automatic total station, is a measuring platform that integrates automatic target recognition, automatic aiming, automatic angle and distance measurement, automatic target tracking, and automatic recording. It includes eight parts: coordinate system, manipulator, transducer, computer and controller, closed-circuit control sensor, decision making, target capture, and integrated sensor. The telescope can rotate around the longitudinal and transverse axes of the instrument and search for targets within 360° horizontal and 180° vertical ranges; the video imaging system composed of image sensors can automatically track and accurately aim at the target through image generation, image acquisition, and image processing under the control of computers and controllers, thereby obtaining information such as the length, thickness, width, orientation, 2D and 3D coordinates of an object or a part of an object, and then obtaining the shape of the object and its changes over time.

[0005] The settlement monitoring system is a geotechnical engineering monitoring device, which consists of a series of settlement monitoring sensors, and the instruments are connected by connecting pipes. The liquid storage tank and one set of settlement monitoring sensors are placed at a stable horizontal base point, and other settlement monitoring sensors are placed at different positions with roughly the same elevation. When other settlement monitoring sensors rise and fall relative to the liquid storage tank, the pressure of the settlement monitoring sensor will decrease or increase. By measuring the change in pressure, the lifting and lowering deformation of the measured point relative to the base point can be calculated.

[0006] The above-mentioned monitoring system is mainly used to realize settlement monitoring of GIS, but cannot realize settlement monitoring and early warning of GIS. The existing Chinese invention patent application with application publication number CN117109523A and application publication date 2023.11.24 discloses a settlement monitoring and early warning method and system for GIS equipment. The method first determines the monitoring abnormal fluctuation data based on the monitoring data at different time points, and corrects the above-mentioned monitoring abnormal fluctuation data according to the temperature and humidity information of the monitoring point and the service life and design life of the monitoring sensor, and performs settlement early warning based on the size relationship between the corrected abnormal fluctuation data and the preset threshold. Although the above-mentioned technical scheme realizes settlement early warning, its settlement early warning is based on the abnormal settlement that has occurred, and it is impossible to realize the advance prediction of settlement, which makes the settlement early warning untimely, which may lead to serious safety accidents. Summary of the invention

[0007] The purpose of the present invention is to provide a GIS settlement prediction method and early warning system to solve the technical problem that the prior art cannot achieve advance prediction of GIS settlement, resulting in untimely settlement early warning.

[0008] In order to solve the above technical problems, the present invention provides a technical solution of a GIS settlement prediction method: a GIS settlement prediction method, the method comprising: S1. Obtaining settlement monitoring data through a settlement monitoring system; S2, inputting the settlement monitoring data obtained in S1 into the trained settlement prediction model to obtain settlement prediction data; The settlement prediction model includes a prediction model that takes the GIS historical settlement data collected by the settlement monitoring system in a set time period as input and takes the GIS settlement trend actually measured within a certain period after the set time period as output; the settlement prediction data includes the GIS settlement trend.

[0009] The beneficial effect of the above technical solution is that the technical solution of a GIS settlement prediction method of the present invention belongs to a pioneering invention. The present invention determines the relationship between GIS historical settlement data and GIS settlement data at a future time by establishing a settlement prediction model, so as to predict the settlement data at a future time by using the historical settlement data, thereby realizing the advance prediction of GIS settlement. The present invention solves the technical problem that the prior art cannot realize the advance prediction of GIS settlement, resulting in untimely settlement warning.

[0010] Furthermore, the settlement prediction model also includes a prediction model trained using historical data, which takes GIS historical settlement data collected by the settlement monitoring system in a set time period as input and takes GIS settlement data actually measured within a certain period after the set time period as output; the settlement prediction data also includes GIS settlement displacement.

[0011] Furthermore, the method further comprises: At regular intervals, the latest settlement monitoring data collected by the settlement monitoring system and the GIS settlement trend and / or GIS settlement displacement actually measured within a certain period of time after the collection are added to the training sample sequence of the settlement prediction model, and the earliest data are removed from the training sample sequence; the settlement prediction model is retrained and updated based on the latest training sample sequence.

[0012] Furthermore, the settlement monitoring data acquired by S1 is the data obtained by processing the original monitoring data through wavelet denoising.

[0013] Furthermore, the settlement prediction model is a RBF neural network.

[0014] The present invention also provides a technical solution for a GIS settlement early warning system: a GIS settlement early warning system, comprising a processor, characterized in that the processor is used to execute a computer program to implement the steps of the GIS settlement prediction method as described above; the processor is also used to perform settlement early warning based on settlement prediction data output by a settlement prediction model.

[0015] The beneficial effect of the above technical solution is that the technical solution of a GIS settlement early warning system of the present invention belongs to an improved invention. The present invention determines the relationship between GIS historical settlement data and GIS settlement data at a future time by establishing a settlement prediction model, so as to predict the settlement data at a future time by using the historical settlement data, realize the advance prediction of GIS settlement, and perform settlement early warning according to the output result of the settlement prediction model. The present invention solves the technical problem that the prior art cannot realize the advance prediction of GIS settlement, resulting in untimely settlement early warning.

[0016] Furthermore, the settlement monitoring system includes a liquid storage tank and at least one differential pressure settlement sensor fixedly connected to the GIS equipment body, the liquid storage tank and the differential pressure settlement sensor are connected in sequence through a connecting pipe, and the liquid storage tank is used to provide a reference pressure for the settlement monitoring system.

[0017] Furthermore, the differential pressure sedimentation sensor includes a wireless communication module for transmitting monitoring data.

[0018] Furthermore, the wireless communication module is a LoRa communication module.

[0019] Furthermore, the differential pressure sedimentation sensor is powered by a lithium-ion battery.

[0020] Furthermore, the settlement monitoring system also includes a data acquisition terminal and a monitoring background running the processor, and the differential pressure settlement sensor sends the monitoring data to the monitoring background through the data acquisition terminal.

[0021] Furthermore, there are at least two pressure differential settlement sensors, and the data acquisition terminal also processes the monitoring data sent by the pressure differential settlement sensors to obtain the relative displacement between the pressure differential settlement sensors; when any relative displacement exceeds a preset threshold, the data acquisition terminal issues an alarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of the GIS settlement prediction method of the GIS settlement early warning system embodiment of the present invention; Figure 2 It is the overall architecture of the settlement monitoring system of the GIS settlement early warning system embodiment of the present invention; Figure 3 This is the communication architecture of the settlement monitoring system of the GIS settlement early warning system embodiment of the present invention. DETAILED DESCRIPTION

[0023] Since the settlement warning in the prior art is based on the abnormal settlement that has occurred, it is impossible to achieve advance prediction of settlement, resulting in untimely settlement warning. The present invention determines the relationship between GIS historical settlement data and GIS settlement data at future times by establishing a settlement prediction model, so as to predict the settlement data at future times through historical settlement data, achieve advance prediction of GIS settlement, and perform settlement warning according to the output results of the settlement prediction model. The present invention solves the technical problem that the prior art cannot achieve advance prediction of GIS settlement, resulting in untimely settlement warning.

[0024] GIS settlement early warning system implementation example: A GIS settlement early warning system includes a settlement monitoring system, such as Figure 2 As shown, the settlement monitoring system consists of several pressure differential settlement monitoring sensors, a liquid storage tank, a data acquisition terminal and a monitoring background. Several pressure differential settlement monitoring sensors and the liquid storage tank are connected in sequence through connecting pipes. The liquid tank and one set of settlement monitoring sensors are placed at a stable horizontal base point, and other settlement monitoring sensors are placed at different positions on the GIS equipment body with roughly the same elevation. When other settlement monitoring sensors rise and fall relative to the liquid storage tank, the pressure of the settlement monitoring sensor will decrease or increase. When settlement occurs, the corresponding sensor pressure changes, and the rise and fall deformation of the measured point relative to the base point is reflected by measuring the change in pressure.

[0025] Among them, a computer program capable of implementing the GIS settlement prediction method described below is run in the processor of the monitoring background. The flow chart of the GIS settlement prediction method is as follows: Figure 1 As shown: S1. Obtain settlement monitoring data.

[0026] The settlement monitoring data is obtained through the settlement monitoring system. First, the original monitoring data is denoised using the denoising principle of wavelet analysis. Compared with the subsequent neural network prediction output preprocessing process, the wavelet denoising process provides more reliable real input data samples for the neural network prediction results, thereby obtaining a more ideal prediction value and improving the accuracy of the entire processing process.

[0027] S2. Input the settlement monitoring data obtained in S1 into the trained settlement prediction model to obtain settlement prediction data.

[0028] The settlement prediction model includes a prediction model that takes the GIS historical settlement data collected by the settlement monitoring system during a set time period as input and takes the GIS settlement trend actually measured within a certain period of time after the set time period as output.

[0029] In this embodiment, the settlement prediction model also includes a settlement displacement prediction model trained using historical data, which takes the GIS historical settlement data collected by the settlement monitoring system in a set time period as input and takes the GIS settlement data actually measured within a certain period after the set time period as output.

[0030] Accordingly, the settlement prediction data include GIS settlement trend and GIS settlement displacement.

[0031] That is, the settlement monitoring data of the past period of time is used to predict the settlement situation in the future period of time through the settlement prediction model, so as to obtain the future settlement trend and realize the advance prediction of settlement.

[0032] In the present invention, the GIS settlement trend refers to whether the GIS equipment will settle, that is, whether the GIS equipment will continue to drop, or maintain the current position unchanged, or the GIS equipment will rise with the foundation.

[0033] Specifically, in this embodiment, the settlement prediction model is a radial basis function (RBF) neural network.

[0034] In other implementations, the settlement prediction model may be a BP (Back Progation) neural network, a LSTM (Long Short-Term Memory) neural network, or other machine learning or deep learning algorithms.

[0035] A settlement prediction model based on radial basis function network is established, and the neural network prediction is performed through the denoised data according to the established RBF network model. The mean square error value of the RBF neural network performance is set to 0.001 to measure the accuracy requirement of the neural network; the learning rate is set to 0.08 to make the network relatively stable; the number of nodes in the hidden layer is set to 18; and the training sample is 360 sets of data. In the process of training the network model, the present invention adopts the method of automatic update and iteration of sample data, and uses the monitoring data of the past 3 months as the training sample for settlement prediction.

[0036] Specifically, in this embodiment, the differential pressure settlement monitoring sensor sends data every 6 hours, and there are 5 differential pressure settlement monitoring sensors. The settlement prediction model predicts the GIS settlement trend and GIS settlement displacement within the next 6 hours (i.e. a certain period of time) based on the GIS settlement displacement collected by the settlement monitoring system in the past 3 months (i.e. a set time period).

[0037] The present invention mainly uses time series data analysis and evolution trend analysis to construct an RBF neural network model. The measured settlement data is analyzed, and the characteristics reflected in different stages of settlement are also different. In the early stage of busbar equipment installation, a certain amount of settlement will occur due to the influence of the foundation and the equipment body. From the data analysis level, the characteristic is that the settlement rate is relatively fast. After a period of time, a relatively stable working condition is slowly reached. The biggest factor causing settlement is the deformation of the busbar caused by thermal expansion and contraction. The cumulative settlement amount changes tend to be gentle and is greatly affected by the temperature difference.

[0038] Therefore, in this embodiment, at regular intervals, the latest settlement monitoring data collected by the settlement monitoring system and the GIS settlement data actually measured within a certain period of time after the collection are added to the training sample sequence of the settlement prediction model, and the earliest data are removed from the training sample sequence; the settlement prediction model is retrained and updated according to the latest training sample sequence.

[0039] The present invention automatically and continuously puts the settlement monitoring data collected by the settlement monitoring system into a sample sequence through a program, and removes the data with the longest history, so as to better reflect the real-time status of the development of different stages of settlement.

[0040] By analyzing the long-term settlement monitoring data of a power station bus, the prediction accuracy was verified by comparing the measured data with the predicted data. The average error between the predicted value and the measured value of the traditional RBF neural network was 0.199mm, and the average error between the predicted value and the measured value of the RBF neural network using automatic sample update was 0.125mm. Through the comparison and verification of the measured data, the present invention achieved better prediction effect and improved the prediction accuracy of the settlement trend.

[0041] Functional features of differential pressure settlement monitoring sensor: 1) The sensor has a built-in computing chip that automatically converts the measured pressure data and directly outputs the monitored physical quantity without manual conversion; 2) The sensor can automatically perform real-time temperature compensation to improve the adaptability of the sensor under different climatic conditions and the accuracy of monitoring data; 3) Can be directly connected to the bus for network monitoring and data collection.

[0042] The data acquisition terminal is used to automatically collect and save sensor data, and centrally process the data to calculate the relative displacement between sensors. When the relative displacement exceeds the threshold, the warning function is triggered. At the same time, the data acquisition terminal uploads the data to the monitoring background using the IEC61850 protocol to achieve remote real-time monitoring.

[0043] Low-power LoRa communication is used between the sensor and the data acquisition terminal. Combined with the characteristics of settlement monitoring, which has a small amount of uploaded data and low real-time requirements, full-life monitoring of foundation settlement can be achieved.

[0044] (1) Installation method of settlement monitoring system: The basic settlement monitoring device includes a settlement monitoring sensor, a liquid storage tank, a connecting pipe adapter, a water-gas isolation device, a ventilation pipe, a connecting pipe, raw tape, etc.

[0045] 1) Sensor installation Use clamp installation. First determine the installation position of the settlement monitoring sensor, use the clamp to cover the main equipment body, stand the sensor upright, with the exhaust valve facing upwards and the transparent observation window facing yourself, close to the clamp, and finally tighten the clamp.

[0046] 2) Installation of liquid storage tank The liquid storage tank is fixed to the side of the equipment body with a clamp, or fixed by setting a support rod near the equipment. After the foundation settlement monitoring device is installed, a protective box must be installed.

[0047] 3) Connecting pipe installation Lay the connecting pipes in sequence according to the length of the pipeline path between the sensors, connect the liquid storage tanks in series, and use the three-in-one insulation pipe as the connecting pipe material. After swelling with hot water, connect it to the liquid level connection port of the liquid storage tank through the adapter, and then lock the nut. When laying the connecting pipe, make sure that the pipe is straightened and try not to bend the pipe. When encountering a cable trench during substation construction, a φ70mm hole can be drilled with a water drill to allow the pipe to pass through and be arranged along the cable trench and fixed with a pipe clamp to ensure that the liquid can flow smoothly. The optimal length of the connecting pipe between the settlement monitoring sensors needs to be calculated to prevent the level pipe from being insufficient when settlement occurs, resulting in looseness at the connection between the level pipe and the liquid storage tank, resulting in leakage.

[0048] 4) System filling In order to achieve the anti-corrosion effect, distilled water and formaldehyde solution are used as the liquid working medium in the connecting pipe. If the ambient temperature of the substation is lower than 0℃, the antifreeze of the corresponding label should be added according to the lowest temperature in the local working environment. Before adding liquid, the liquid level tube and liquid storage tank should be soaked and cleaned with alcohol. Open the liquid port of the tail end settlement monitoring sensor. Liquid addition is generally performed from the liquid storage tank. Add liquid to the 2 / 3 position of the liquid storage tank, let it stand for about half an hour, then open the valve, and continuously observe the liquid level height in the liquid storage tank through the liquid level observation window. When the liquid level height is lower than the lowest position of the liquid storage tank observation tube, close the valve, add liquid to the liquid storage tank again, add liquid to the 2 / 3 position of the liquid storage tank, let it stand for about half an hour, then open the valve, repeat the above steps until liquid flows out of the tail end of the settlement monitoring system, then seal the tail end liquid port, check whether there is air in the liquid level tube and sensor, if there is air, press the sensor exhaust valve until the air is completely discharged.

[0049] 5) Ventilation pipe installation The purpose of ventilation is to keep all liquid surface pressures consistent. The entire air pressure balance system should be interconnected and connected to the atmosphere only at one end (one of the two ends).

[0050] 6) Data acquisition terminal and cable installation It can be installed in the control cabinet around the equipment body, or placed in a separate cabinet. Lay the communication cables in the order of the pipeline paths between the measuring points. The data acquisition terminal is connected to the observation room through cables or wireless transmission.

[0051] 7) Instrument and pipeline protection The settlement monitoring system is a precision measuring instrument. A large temperature difference will cause the liquid in the instrument to expand and contract, which will lead to measurement deviation. Therefore, the instrument and the connecting pipe need to be insulated. At the same time, in order to prevent the communication cable from being exposed to the outside and easily damaged, it is also necessary to strengthen the protection. If the settlement monitoring system is used outdoors, a special metal instrument protection box can be made and filled with foam material to act as an insulation layer. Then put the settlement monitoring sensor inside, which can protect the equipment and have a heat insulation function. The connecting pipe can be protected with φ30mm polyvinyl chloride insulation material. At the same time, the communication cable is inserted into a φ25mm PVC pipe to prevent the line from being damaged.

[0052] (2) Wireless communication module like Figure 3 As shown, the present invention designs a LoRaWAN module in the differential pressure settlement monitoring sensor, supporting the LoRAWANV1.0.2 protocol. The module is based on LoRa spread spectrum modulation technology, which can achieve ultra-long outdoor communication distance of substations, and the open communication distance can reach 6km; on the other hand, the module is based on ultra-low power design, with sleep power consumption as low as 1.6uA, and can work effectively for 10 years when powered by batteries.

[0053] Combined with LoRaWAN gateway, wireless communication from sensor to data acquisition terminal is realized. Choose the best location to install LoRaWAN gateway according to the distance from the sensor. LoRaWAN gateway is a wireless network data acquisition device based on SX1301 digital baseband chip. It mainly realizes data transmission between multiple nodes and remotely distributed sensors and servers, and has the advantages of low cost, standardization, and intelligence. The biggest feature of LoRaWAN gateway is its low power consumption and ultra-long-distance transmission. The chip receiving current is less than 6.5mA, the lowest can be less than 4.2mA, and the sleep current is 0.9~2.35mA, which truly realizes low-power chip; there are also chips that can reach an ultra-long transmission distance of 6km.

[0054] GIS settlement prediction method embodiment: A GIS settlement prediction method, the method comprising: S1, obtaining settlement monitoring data through a settlement monitoring system; S2, inputting the settlement monitoring data obtained in S1 into a trained settlement prediction model to obtain settlement prediction data; the settlement prediction model comprises a prediction model that takes GIS historical settlement data collected by the settlement monitoring system in a set time period as input and takes the GIS settlement trend actually measured within a certain period after the set time period as output; the settlement prediction data comprises GIS settlement trend. The specific GIS settlement prediction method has been introduced in sufficient detail in the above-mentioned GIS settlement early warning system embodiment, and will not be repeated here.

[0055] The present invention has the following characteristics: The present invention applies the settlement monitoring sensor widely used in geotechnical engineering monitoring to the settlement monitoring of the foundation of the GIS busbar equipment in the substation. Combined with the actual operating conditions of the substation GIS, the present invention studies the installation method of the sensor, the application in the substation environment, etc. On this basis, a wireless communication module is added to the sensor to perform wireless data transmission, and an online monitoring device that can effectively monitor the uneven settlement of the GIS busbar foundation is developed.

[0056] The present invention establishes an RBF neural network model, sets and debugs model parameters, automatically updates samples using recent monitoring data, obtains a network model suitable for GIS busbar foundation uneven settlement prediction, and predicts settlement data. Monitoring and early warning of GIS busbar foundation uneven settlement has great practical significance.

[0057] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments without creative work, or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A GIS settlement prediction method, characterized in that: The method includes: S1. Obtaining settlement monitoring data through a settlement monitoring system; S2, inputting the settlement monitoring data obtained in S1 into the trained settlement prediction model to obtain settlement prediction data; The settlement prediction model includes a prediction model that takes the GIS historical settlement data collected by the settlement monitoring system in a set time period as input and takes the GIS settlement trend actually measured within a certain period after the set time period as output; the settlement prediction data includes the GIS settlement trend.

2. The GIS settlement prediction method according to claim 1, characterized in that: The settlement prediction model also includes a prediction model trained using historical data, which takes the GIS historical settlement data collected by the settlement monitoring system in a set time period as input and takes the GIS settlement data actually measured within a certain period after the set time period as output; the settlement prediction data also includes GIS settlement displacement.

3. The GIS settlement prediction method according to claim 2, characterized in that: The method further includes: At regular intervals, the latest settlement monitoring data collected by the settlement monitoring system and the GIS settlement trend and / or GIS settlement displacement actually measured within a certain period of time after the collection are added to the training sample sequence of the settlement prediction model, and the earliest data are removed from the training sample sequence; the settlement prediction model is retrained and updated based on the latest training sample sequence.

4. The GIS settlement prediction method according to claim 1 or 2, characterized in that: The settlement monitoring data obtained by S1 is the data after the original monitoring data is processed by wavelet denoising.

5. The GIS settlement prediction method according to claim 1 or 2, characterized in that: The settlement prediction model is a RBF neural network.

6. A GIS settlement early warning system, comprising a processor, characterized in that: The processor is used to execute a computer program to implement the steps of the GIS settlement prediction method as described in any one of claims 1 to 5; the processor is also used to issue a settlement warning based on the settlement prediction data output by the settlement prediction model.

7. The GIS subsidence early warning system according to claim 6, characterized in that: The settlement monitoring system includes a liquid storage tank and at least one differential pressure settlement sensor fixedly connected to the GIS equipment body. The liquid storage tank and the differential pressure settlement sensor are connected in sequence through a connecting pipe. The liquid storage tank is used to provide a reference pressure for the settlement monitoring system.

8. The GIS subsidence early warning system according to claim 7, characterized in that: The differential pressure sedimentation sensor includes a wireless communication module for transmitting monitoring data.

9. The GIS subsidence early warning system according to claim 8, characterized in that: The wireless communication module is a LoRa communication module.

10. The GIS subsidence early warning system according to claim 7, 8 or 9, characterized in that: The differential pressure sedimentation sensor is powered by a lithium-ion battery.

11. The GIS subsidence early warning system according to claim 7, 8 or 9, characterized in that: The settlement monitoring system further comprises a data acquisition terminal and a monitoring background running the processor, and the differential pressure settlement sensor sends the monitoring data to the monitoring background via the data acquisition terminal.

12. The GIS subsidence early warning system according to claim 11, characterized in that: There are at least two differential pressure settlement sensors, and the data acquisition terminal also processes the monitoring data sent by the differential pressure settlement sensors to obtain the relative displacement between the differential pressure settlement sensors; when any relative displacement exceeds a preset threshold, the data acquisition terminal issues an alarm.

Images