CN117486029A - Sensor network-based elevator energy consumption real-time monitoring method and system - Google Patents
Sensor network-based elevator energy consumption real-time monitoring method and system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
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Abstract
The invention discloses a sensor network-based elevator energy consumption real-time monitoring method and system, and particularly relates to the technical field of energy consumption monitoring. The abnormal condition of wireless transmission between the sensor and the energy consumption analysis center can be found and responded in time, the accuracy of elevator energy consumption data and the stability of a system are ensured, and therefore the energy efficiency and the management level of a building are improved; the future risk of wireless transmission between the sensor and the energy consumption analysis center is predicted, the trend of transmission problems can be found in time, the reliability of energy consumption data is improved, and a more stable foundation is provided for elevator energy consumption analysis.
Description
Technical Field
The invention relates to the technical field of energy consumption monitoring, in particular to a sensor network-based elevator energy consumption real-time monitoring method and system.
Background
The elevators are 'large consumers' in building equipment, the consumed energy of the elevators generally accounts for 5% -15% of the total energy consumption of the whole building, the reserved quantity of the elevators in China is over 490 ten thousands, the electricity consumption of each elevator is calculated by 40 degrees per day, and the electricity consumption of each elevator exceeds 715 hundred million degrees per year. At present, the related research on the energy consumption of the elevator mainly comprises an elevator energy consumption testing method and the classification of elevator energy efficiency grades, and the research on the remote monitoring means of the elevator energy consumption is also required to be enhanced. The existing elevator remote monitoring system at home and abroad focuses on the monitoring of elevator safety faults, and the monitoring of elevator energy consumption is still to be further perfected.
Monitoring the energy consumption data of the elevator through a sensor with a wireless communication function in a wireless sensor network, sending the energy consumption data to an energy consumption analysis center in real time, and analyzing the energy consumption condition of the elevator; however, the stability of the process of sending the elevator energy consumption data to the energy consumption analysis center by the sensor is not evaluated, so that the energy consumption analysis center can analyze the unreliable elevator energy consumption data, thereby affecting the accurate judgment of the elevator energy consumption, and obtaining an inaccurate conclusion, thereby affecting the accurate judgment of the elevator energy consumption condition.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides a sensor network-based elevator energy consumption real-time monitoring method and system to solve the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a sensor network-based elevator energy consumption real-time monitoring method comprises the following steps:
step S1: judging whether connection is established between the sensor and the energy consumption analysis center through heartbeat signals;
step S2: when connection is established between the sensor and the energy consumption analysis center, analyzing the time sequence consistency condition of the sensor data, and evaluating the abnormal degree of the acquisition of the elevator energy consumption data; analyzing the output state of the sensor, and evaluating the output performance of the sensor;
step S3: judging the influence of the magnetic field intensity of the elevator on wireless transmission between the sensor and the energy consumption analysis center; when the influence of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center reaches a trigger condition, evaluating the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center;
Step S4: comprehensively analyzing the abnormal degree of the acquisition of the elevator energy consumption data, the output performance of the sensor and the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center, and evaluating the stability of the wireless transmission between the sensor and the energy consumption analysis center;
step S5: and analyzing the stability of the wireless transmission between the sensor and the energy consumption analysis center within a period of time, and early warning the fault risk of the wireless transmission between the sensor and the energy consumption analysis center in advance.
In a preferred embodiment, in step S1, a heartbeat timeout threshold is set; when the energy analysis center receives the heartbeat signal of the sensor in the heartbeat timeout threshold, generating a connection normal signal; and when the energy analysis center does not receive the heartbeat signal of the sensor in the heartbeat timeout threshold, generating a connection failure signal.
In a preferred embodiment, in step S2, when the connection normal signal is generated, a recent data point set is set, and the recent data point set includes n data points actually received by the energy consumption analysis center nearest to the real-time;
acquiring time intervals of n-1 adjacent data points actually received by an energy consumption analysis center, which are included in a recent data point set, numbering the time intervals of each adjacent data point, and marking the number as w;
Acquiring a preset time interval of corresponding sensor acquisition data; analyzing the deviation condition of the time interval of the adjacent data points in the recent data point set and the preset time interval of the corresponding sensor acquisition data, and calculating to obtain a sampling interval deviation value;
when the sampling interval offset value is greater than the deviation evaluation threshold value, generating a time sequence alarm signal;
and when the sampling interval offset value is smaller than or equal to the deviation evaluation threshold value, generating a time sequence normal signal.
In a preferred embodiment, a voltage monitoring interval is set; uniformly setting k monitoring points in time in a voltage monitoring interval, wherein each monitoring point obtains an output voltage value corresponding to a monitoring point of the sensor; k is an integer greater than 1;
acquiring the number of monitoring points of which the output voltage value in the voltage monitoring interval deviates from a preset output voltage range, and marking the ratio of the number of the monitoring points of which the output voltage value in the voltage monitoring interval deviates from the preset output voltage range to the number of the monitoring points in the voltage monitoring interval as a voltage abnormality ratio;
acquiring a time interval between two adjacent monitoring points; acquiring the sum of time intervals between two adjacent monitoring points in a voltage monitoring interval, and marking the ratio of the sum of the time intervals between the two adjacent monitoring points in the voltage monitoring interval and the number of monitoring points, of which the output voltage value in the voltage monitoring interval deviates from a preset output voltage range, as an abnormal output clinging ratio;
Performing unit removal processing on the voltage abnormality ratio and the abnormal output cling ratio, performing weighted summation on the voltage abnormality ratio and the abnormal output cling ratio after the unit removal processing, and calculating the abnormal value of the sensing output.
In a preferred embodiment, in step S3, a magnetic field monitoring interval is set; acquiring the magnetic field intensity around the sensor in the magnetic field monitoring interval, acquiring the time length of the magnetic field intensity around the sensor in the magnetic field monitoring interval being greater than the magnetic field intensity threshold value, and marking the ratio of the time length of the magnetic field intensity around the sensor in the magnetic field monitoring interval being greater than the magnetic field intensity threshold value to the time length corresponding to the magnetic field monitoring interval as the magnetic field influence ratio;
setting a magnetic field influence ratio threshold; when the magnetic field influence ratio is smaller than or equal to a magnetic field influence ratio threshold value, generating a magnetic field influence neglect signal; when the magnetic field influence ratio is larger than the magnetic field influence ratio threshold, a magnetic field influence transmission signal is generated, and the triggering condition of influence of the magnetic field intensity on wireless transmission between the sensor and the energy consumption analysis center is achieved.
In a preferred embodiment, when generating the magnetic field influencing transmission signal, the extent to which the magnetic field strength in the magnetic field monitoring interval is greater than the magnetic field strength threshold value is analyzed, in particular:
Establishing a two-dimensional coordinate system by taking the time length corresponding to the magnetic field monitoring interval as an abscissa and taking the value of the magnetic field intensity in the magnetic field monitoring interval as an ordinate, and obtaining a change curve of the magnetic field intensity in the magnetic field monitoring interval;
and marking the area formed by the straight line corresponding to the magnetic field intensity threshold value and the magnetic field intensity change curve with the magnetic field intensity larger than the magnetic field intensity threshold value in the magnetic field monitoring interval as a magnetic field abnormality degree value.
In a preferred embodiment, in step S4, the sampling interval offset value, the sensing output abnormal value and the magnetic field abnormal level value are normalized, and the sensing energy consumption information early warning coefficient is calculated by the normalized sampling interval offset value, the sensing output abnormal value and the magnetic field abnormal level value;
setting an early warning first threshold value and an early warning second threshold value, wherein the early warning first threshold value is smaller than the early warning second threshold value;
when the early warning coefficient of the sensing energy consumption information is smaller than the early warning first threshold value, generating a sensing transmission normal signal;
when the early warning coefficient of the sensing energy consumption information is larger than or equal to the early warning first threshold value and the early warning coefficient of the sensing energy consumption information is smaller than or equal to the early warning second threshold value, generating a sensing transmission low risk signal;
and when the early warning coefficient of the sensing energy consumption information is larger than the two early warning thresholds, generating a sensing transmission high risk signal.
In a preferred embodiment, in step S5, a failure risk monitoring interval is set; acquiring the duty ratio condition of the sensing transmission low-risk signals in the fault risk monitoring interval:
when the sensing transmission high-risk signal does not exist in the fault risk monitoring interval, marking the ratio of the time length occupied by the sensing transmission low-risk signal in the fault risk monitoring interval to the time length corresponding to the fault risk monitoring interval as the transmission low-risk ratio;
generating a transmission risk signal when the transmission low risk duty cycle is greater than the low risk duty cycle threshold;
and when the transmission low risk ratio is smaller than or equal to the low risk ratio threshold, generating a transmission risk normal signal.
In a preferred embodiment, the elevator energy consumption real-time monitoring system based on the sensor network comprises a connection judging module, a transmission abnormality evaluating module, a magnetic field influence evaluating module, a comprehensive transmission evaluating module and a transmission risk early warning module;
the connection judging module judges whether connection is established between the sensor and the energy consumption analysis center through a heartbeat signal;
when connection is established between the sensor and the energy consumption analysis center, the transmission abnormality assessment module analyzes the time sequence consistency condition of the sensor data and assesses the abnormal degree of the acquisition of the elevator energy consumption data; analyzing the output state of the sensor, and evaluating the output performance of the sensor;
The magnetic field influence evaluation module judges the influence of the magnetic field intensity of the elevator on wireless transmission between the sensor and the energy consumption analysis center; when the influence of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center is large, evaluating the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center;
the comprehensive transmission evaluation module comprehensively analyzes the degree of abnormality in the acquisition of the elevator energy consumption data, the output performance of the sensor and the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center, and evaluates the stability of the wireless transmission between the sensor and the energy consumption analysis center;
the transmission risk early warning module analyzes the stability of wireless transmission between the sensor and the energy consumption analysis center within a period of time, and early warns the fault risk of the wireless transmission between the sensor and the energy consumption analysis center in advance.
The elevator energy consumption real-time monitoring method and system based on the sensor network have the technical effects and advantages that:
1. and carrying out normalization processing on the sampling interval offset value, the sensing output abnormal value and the magnetic field abnormal degree value, and calculating to obtain a sensing energy consumption information early warning coefficient by combining a preset proportionality coefficient. The stability of wireless transmission between the sensor and the energy consumption analysis center is reflected, an early warning first threshold value and an early warning second threshold value are set, and three signals of normal sensing transmission, low risk and high risk are divided according to the magnitude of an early warning coefficient. The abnormal condition of wireless transmission between the sensor and the energy consumption analysis center can be timely found and responded, and real-time transmission quality assessment and risk early warning are provided. The reliability of the whole system is improved, maintenance and optimization measures are timely taken, the accuracy of elevator energy consumption data and the stability of the system are ensured, and therefore the energy efficiency and the management level of the elevator are improved.
2. The low risk signal condition of the sensing transmission of the sensor is monitored, the future risk of wireless transmission between the sensor and the energy consumption analysis center is predicted, and the trend of the transmission problem can be found in time. The method is favorable for taking maintenance and optimization measures in advance, preventing transmission problems and ensuring normal operation of the system. The risk of inaccurate or interrupted data caused by wireless transmission faults between the sensor and the energy consumption analysis center is effectively reduced. This helps improving the reliability of energy consumption data, provides more stable basis for elevator energy consumption analysis.
Drawings
Fig. 1 is a schematic diagram of an elevator energy consumption real-time monitoring method based on a sensor network;
fig. 2 is a schematic structural diagram of the elevator energy consumption real-time monitoring system based on the sensor network.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment 1, fig. 1 shows a real-time monitoring method for elevator energy consumption based on a sensor network, which comprises the following steps:
step S1: and judging whether connection is established between the sensor and the energy consumption analysis center through the heartbeat signal.
Step S2: when connection is established between the sensor and the energy consumption analysis center, analyzing the time sequence consistency condition of the sensor data, and evaluating the abnormal degree of the acquisition of the elevator energy consumption data; and analyzing the output state of the sensor, and evaluating the output performance of the sensor.
Step S3: judging the influence of the magnetic field intensity of the elevator on wireless transmission between the sensor and the energy consumption analysis center; and when the influence of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center reaches a trigger condition, evaluating the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center.
Step S4: and comprehensively analyzing the degree of abnormality in the acquisition of the elevator energy consumption data, the output performance of the sensor and the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center, and evaluating the stability of the wireless transmission between the sensor and the energy consumption analysis center.
Step S5: and analyzing the stability of the wireless transmission between the sensor and the energy consumption analysis center within a period of time, and early warning the fault risk of the wireless transmission between the sensor and the energy consumption analysis center in advance.
In step S1, it is a common means to determine whether a device or sensor is successfully connected, typically in wireless communication, in particular in a sensor network, using heartbeat signals. A heartbeat signal is a small packet of signals sent periodically to indicate that the device is still active. By receiving the heartbeat signal, the receiving end can confirm the existence and connection state of the device.
The specific steps of judging whether the connection between the sensor and the energy consumption analysis center is established or not through the heartbeat signal are as follows:
sensor initialization: at system start-up or sensor deployment, it is ensured that the sensor has properly initialized and configured wireless communication parameters, including communication protocol, frequency, transmission rate, etc.
Heartbeat signal setting: the time interval at which the sensor periodically transmits a heartbeat signal, typically a small data packet, containing information identifying the sensor and confirming the connection status, is set.
Heartbeat signal transmission: the sensor periodically transmits heartbeat signals to the energy consumption analysis center according to the set time interval; this may be a separate data packet or may be information contained in a normal data transmission.
Monitoring by an energy consumption analysis center: the energy consumption analysis center monitors the heartbeat signal received from the sensor at regular time. Ensuring that the system is able to properly receive and parse these signals.
If the heartbeat signal of the sensor is not received within a certain period of time, the system determines that the connection may be interrupted.
Setting a heartbeat timeout threshold, wherein the heartbeat timeout threshold is set according to a time interval of the sensor periodically sending the heartbeat signal to the energy consumption analysis center, and the setting of the heartbeat timeout threshold generally needs to consider the time interval of the sensor periodically sending the heartbeat signal and the requirement on the real-time performance of the system. This value should be long enough to tolerate normal sensor transmission delays while being short enough to trigger the processing mechanism in time when the connection is abnormal. In general, the timeout threshold may be set to twice the time interval of the heartbeat signal or more to ensure that connection anomalies are not misjudged under normal conditions. Specific values need to be adjusted and optimized according to the specific requirements and characteristics of the system.
When the energy consumption analysis center receives the heartbeat signal of the sensor in the heartbeat timeout threshold, a connection normal signal is generated, and the sensor is online at the moment.
And when the energy consumption analysis center does not receive the heartbeat signal of the sensor and generates a connection failure signal, judging that the sensor is not on-line. The energy consumption analysis center immediately arranges professional technicians for inspection and repair according to the generated connection failure signals.
In step S2, the time sequence consistency of the sensor data means that the data is ensured to be transmitted at predetermined time intervals, and the disorder or repetition of the sensor data is avoided.
Typically, the sensor will generate data points at preset time intervals when it collects data; for example, a sensor that generates a data point every second, with a fixed time interval between data points.
If the time interval between the data points actually received by the energy consumption analysis center deviates from the preset time interval, the sensor or the transmission state has some problems, specifically:
the sensor may be subject to technical failures, power supply problems, sampling frequency setting errors, etc., resulting in an unexpected time interval generated when the sensor actually collects data.
Problems may exist in the transmission of data from the sensor to the energy consumption analysis center, such as network delays, packet losses, transmission errors, etc., which may result in the time interval between actually received data points not being consistent with the preset time interval.
Energy consumption analysis center processing delay: the energy consumption analysis center may need to perform processing, cleaning, or other operations after receiving the data, which may introduce a certain time delay affecting the time interval between actual data points.
When the connection normal signal is generated, a recent data point set is set, wherein the recent data point set comprises n data points actually received by the energy consumption analysis centers closest to the real-time.
Acquiring time intervals of n-1 adjacent data points actually received by an energy consumption analysis center, which are included in a recent data point set, numbering the time intervals of each adjacent data point, and marking the number as w;
and acquiring a preset time interval of the corresponding sensor acquisition data, analyzing the time interval of adjacent data points actually received by the energy consumption analysis center and the deviation condition of the preset time interval of the corresponding sensor acquisition data, and evaluating the stability of acquiring the elevator energy consumption data.
Analyzing the deviation condition of the time interval of the adjacent data points in the recent data point set and the preset time interval of the corresponding sensor acquisition data, and calculating to obtain a sampling interval deviation value, wherein the expression is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the sample interval offset value, +.>For the time interval of the w-th neighboring data point in the recent data point set, +.>A preset time interval for acquiring data for the corresponding sensor; n and w are integers greater than 1; the greater the sampling interval offset value, the higher the degree to which the time interval of the adjacent data points actually received by the energy analysis center in the recent data point set deviates from the preset time interval of the corresponding sensor acquisition data, the worse the stability of acquiring the elevator energy consumption data, the instability and the inaccuracy of the elevator energy consumption data can cause the quality reduction of the elevator energy consumption data, thereby affecting the accurate analysis and management of the elevator energy consumption.
n is set according to the actual monitoring requirement of the data points of the sensor, and will not be described herein.
The deviation evaluation threshold is set by a person skilled in the art according to the magnitude of the sampling interval offset value and other practical situations such as a requirement standard for the time sequence consistency of elevator energy consumption data acquisition in practice, and is not repeated here.
When the sampling interval offset value is greater than the deviation evaluation threshold value, generating a time sequence alarm signal; the method has the advantages that the method shows that the acquisition of the elevator energy consumption data has some problems or abnormal conditions, the degree of abnormality in the acquisition of the elevator energy consumption data is high, the tolerable time interval deviation range is exceeded, the time sequence consistency and accuracy of the elevator energy consumption data are affected, accurate elevator energy consumption data cannot be provided for an energy consumption analysis center to analyze, and accordingly the judgment of the elevator energy consumption is affected.
Based on the generated time series alarm signal, the following measures can be taken:
immediately sending a notification to a system administrator or related responsible person indicating the presence of an abnormal condition and providing detailed diagnostic information for quick action.
Remote overhaul: attempts have been made to remotely service or reconfigure the sensor to address the problem of possible offset. This may include recalibrating the sensor, checking the data transmission channel, etc.
If a standby system exists, the switching to the standby system can be considered, so that the data acquisition and transmission can still be normally performed.
When the sampling interval offset value is smaller than or equal to the deviation evaluation threshold value, generating a time sequence normal signal; the method means that the time sequence consistency of the elevator energy consumption data is good, the accuracy of data acquisition is relatively high, the abnormal degree of the elevator energy consumption data acquisition is low, and in this case, the overall effect of the elevator energy consumption monitoring is not obviously negatively influenced or negatively influenced within an acceptable range, and no adverse effect is represented.
If the power supply of the sensor is unstable, the power consumption of the sensor can be increased when the sensor transmits data, and even if the sensor cannot normally transmit data in some cases, the wireless transmission between the sensor and the energy consumption analysis center can be greatly influenced.
Analyzing the output state of the sensor:
the output of the sensor is typically an electrical signal associated with its measurement target, and the stability of this output signal is critical to accurately measuring and transmitting data. If the circuit or the signal processing unit of the sensor has a problem, the output voltage may be unstable, which affects the performance of the sensor, and thus adversely affects the wireless transmission between the sensor and the energy consumption analysis center.
Setting a voltage monitoring interval, wherein the time length corresponding to the voltage monitoring interval is fixed, and the range of the voltage monitoring interval changes along with the change of time, namely the end point of the voltage monitoring interval is always a real-time point.
K monitoring points are uniformly arranged in time in the voltage monitoring interval, and each monitoring point obtains an output voltage value corresponding to the monitoring point of the sensor. k is an integer greater than 1.
When the output voltage value deviates from a preset output voltage range, the sensor is indicated to have a possible problem or abnormal working state; the occasional occurrence of an output voltage value that deviates from the preset output voltage range may be within an acceptable range for good transmission, but the occurrence of multiple voltage values that deviate from the preset output voltage range over a period of time is indicative of possible performance problems with the sensor.
The preset output voltage range is set by a person skilled in the art according to the safety standard output voltage of the operation of the sensor, and is related to the model of the sensor, and the like, and is not repeated herein.
The method comprises the steps of obtaining the number of monitoring points, in which output voltage values deviate from a preset output voltage range, in a voltage monitoring interval, and marking the ratio of the number of the monitoring points, in which the output voltage values deviate from the preset output voltage range, in the voltage monitoring interval to the number of the monitoring points in the voltage monitoring interval as a voltage abnormality ratio.
And acquiring the time interval between two adjacent monitoring points. The smaller the time interval between the monitoring points of the adjacent two output voltage values deviating from the preset output voltage range, the more closely the output voltage value of the sensor deviates from the preset output voltage range, the sum of the time intervals between the adjacent two monitoring points in the voltage monitoring interval is obtained, and the ratio of the sum of the time intervals between the adjacent two monitoring points in the voltage monitoring interval and the number of the monitoring points of the output voltage value deviating from the preset output voltage range in the voltage monitoring interval is marked as an abnormal output clinging ratio; the smaller the abnormal output cling ratio is, the more closely the output voltage value of the sensor deviates from the preset output voltage range is, and the more adverse effect on the output performance of the sensor is.
Abnormal voltageThe unit removal processing is carried out on the ratio and the abnormal output clinging ratio, the weighted summation is carried out on the voltage abnormal ratio and the abnormal output clinging ratio after the unit removal processing, and the abnormal value of the sensing output is calculated, wherein the expression is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For sensing output outliers +.>The voltage abnormality ratio and the abnormal output cling ratio are respectively +.>The weight coefficients are respectively the voltage abnormality ratio and the abnormal output cling ratio.
The greater the sensed output anomaly, the poorer the output performance of the sensor, and the greater the adverse effect on the wireless transmission between the sensor and the energy consumption analysis center.
In step S3, the elevator itself generates electromagnetic interference, if the deviation from normal reflects the operation condition of the elevator itself is poor, which not only indicates that the elevator has problems (electrical, mechanical or other faults), but also the electromagnetic interference generated by the motor, the electric control equipment and the like in the elevator system can influence the signal transmission of the sensor, and the elevator may cause larger electromagnetic noise during operation to influence the communication quality of the sensor. The magnetic field generated by the elevator may interfere with the operating frequency of the sensor, causing communication interference such that the sensor cannot communicate normally.
The magnetic field intensity around the sensor is measured by placing a magnetic field measuring instrument (a magnetic field instrument, a magnetometer, or a magnetic field measuring instrument) based on the position of the sensor, and the magnetic field intensity is a measured value of the magnetic field measuring instrument.
Setting a magnetic field monitoring interval, wherein the time length corresponding to the magnetic field monitoring interval is fixed, and the range of the magnetic field monitoring interval changes along with the change of time, namely the end point of the magnetic field monitoring interval is always a real-time point.
The method comprises the steps of obtaining the magnetic field intensity around a sensor in a magnetic field monitoring interval, obtaining the time length of the magnetic field intensity around the sensor in the magnetic field monitoring interval being larger than a magnetic field intensity threshold value, and marking the ratio of the time length of the magnetic field intensity around the sensor in the magnetic field monitoring interval being larger than the magnetic field intensity threshold value to the time length corresponding to the magnetic field monitoring interval as a magnetic field influence ratio. Wherein the magnetic field strength threshold is set according to a required standard for the magnetic field strength that the sensor can normally accept.
When the magnetic field influence is smaller, the influence on the wireless transmission between the sensor and the energy consumption analysis center is smaller, and when the magnetic field influence is larger, the influence on the wireless transmission between the sensor and the energy consumption analysis center is larger.
The influence of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center is set to reach the triggering condition:
the magnetic field influence ratio threshold is set according to a required criterion for the magnetic field strength that can be accepted by the sensor over a period of time.
When the magnetic field influence ratio is smaller than or equal to the magnetic field influence ratio threshold value, a magnetic field influence neglect signal is generated, and at the moment, adverse influence of the magnetic field intensity on wireless transmission between the sensor and the energy consumption analysis center can be ignored.
When the magnetic field influence ratio is larger than the magnetic field influence ratio threshold value, a magnetic field influence transmission signal is generated, and the adverse effect of the magnetic field intensity on the wireless transmission between the sensor and the energy consumption analysis center is considered.
When a magnetic field influence transmission signal is generated, analyzing the degree that the magnetic field intensity in the magnetic field monitoring interval is larger than a magnetic field intensity threshold value, specifically:
and establishing a two-dimensional coordinate system by taking the time length corresponding to the magnetic field monitoring interval as an abscissa and taking the value of the magnetic field intensity in the magnetic field monitoring interval as an ordinate, so as to obtain a change curve of the magnetic field intensity in the magnetic field monitoring interval.
And marking the area formed by the straight line corresponding to the magnetic field intensity threshold value and the magnetic field intensity change curve with the magnetic field intensity larger than the magnetic field intensity threshold value in the magnetic field monitoring interval as a magnetic field abnormality degree value. The greater the value of the degree of abnormality of the magnetic field, the longer the time that the magnetic field strength exceeds the threshold value, the greater the degree of electromagnetic abnormality, the greater the adverse effect on the wireless transmission between the sensor and the energy consumption analysis center.
The area calculation related to the magnetic field abnormality degree value is mature in the prior art, for example, a mathematical integration method can be used, and details are not repeated here.
In step S4, the sampling interval offset value, the sensing output abnormal value and the magnetic field abnormal level value are normalized, and the sensing energy consumption information early warning coefficient is calculated by the normalized sampling interval offset value, the sensing output abnormal value and the magnetic field abnormal level value.
For example, the invention can calculate the early warning coefficient of the sensing energy consumption information by adopting the following formula:the method comprises the steps of carrying out a first treatment on the surface of the Wherein,the sensor is respectively a sensing energy consumption information early warning coefficient, a sensing output abnormal value and a magnetic field abnormal degree value; />Preset proportionality coefficients of sampling interval offset value, sensing output abnormal value and magnetic field abnormal degree value respectively, and +.>Are all greater than 0.
The sensing energy consumption information early warning coefficient reflects the stability of wireless transmission between the sensor and the energy consumption analysis center, and the larger the sensing energy consumption information early warning coefficient is, the worse the stability of wireless transmission between the sensor and the energy consumption analysis center is, and the greater the possibility of failure of the wireless transmission between the sensor and the energy consumption analysis center is.
Setting an early warning first threshold value and an early warning second threshold value, wherein the early warning first threshold value is smaller than the early warning second threshold value; the fact that the first early warning threshold value is smaller than the second early warning threshold value is set by a person skilled in the art according to the magnitude of the early warning coefficient of the sensing energy consumption information and other actual conditions such as the safety requirement standard of wireless transmission between the sensor and the energy consumption analysis center, and the like is not repeated here.
When the early warning coefficient of the sensing energy consumption information is smaller than the early warning first threshold value, generating a sensing transmission normal signal; at the moment, the wireless transmission between the sensor and the energy consumption analysis center is normal, and no fault occurs.
When the early warning coefficient of the sensing energy consumption information is larger than or equal to the early warning first threshold value and the early warning coefficient of the sensing energy consumption information is smaller than or equal to the early warning second threshold value, generating a sensing transmission low risk signal; at this time, the wireless transmission between the sensor and the energy consumption analysis center has lower fault risk, and the normal wireless transmission between the sensor and the energy consumption analysis center is not influenced temporarily.
When the early warning coefficient of the sensing energy consumption information is larger than the early warning two thresholds, generating a sensing transmission high risk signal; the risk that the wireless transmission between the sensor and the energy consumption analysis center has failed or is faulty is high. And according to the generated sensing transmission high-risk signal, arranging a professional technician to check and maintain the wireless transmission between the sensor and the energy consumption analysis center.
In step S5, when there are more sensing transmissions of low risk signals for a period of time for a single sensor, it is also predicted that there is a greater risk of failure of the wireless transmission between the sensor and the energy consumption analysis center.
Setting a fault risk monitoring interval, wherein the time length corresponding to the fault risk monitoring interval is fixed, and the range of the fault risk monitoring interval changes along with the change of time, namely the end point of the fault risk monitoring interval is always a real-time point.
Acquiring the duty ratio condition of the sensing transmission low-risk signals in the fault risk monitoring interval:
when the high risk signal of the sensing transmission does not exist in the fault risk monitoring interval (if the high risk signal of the sensing transmission is generated, the fault is already generated or the risk of the fault is higher, and measures are taken), the ratio of the time length occupied by the low risk signal of the sensing transmission in the fault risk monitoring interval to the time length corresponding to the fault risk monitoring interval is marked as the low risk transmission ratio.
When the transmission low risk duty ratio is larger than the low risk duty ratio threshold value, a transmission risk danger signal is generated, at the moment, the duty ratio of the transmission low risk signal is serious in a fault risk monitoring interval, at the moment, the risk of faults of wireless transmission between the sensor and the energy consumption analysis center is high in the future, professional technicians are arranged to check the wireless transmission between the sensor and the energy consumption analysis center in advance according to the generated transmission risk danger signal, the checked problems are repaired, measures are taken in advance to avoid the occurrence of problems of the wireless transmission between the sensor and the energy consumption analysis center, and accordingly the reliability of the acquisition of elevator energy consumption data is guaranteed.
And when the transmission low risk ratio is smaller than or equal to the low risk ratio threshold, generating a transmission risk normal signal without taking measures.
The low risk ratio threshold is set by those skilled in the art according to the size of the transmission low risk ratio and other requirements standards for wireless transmission between the sensor and the energy consumption analysis center, and will not be described in detail herein.
It is noted that the sensors mentioned in the present invention are used for measuring energy consumption data related to the elevator, such as electricity consumption, running time, load, etc.; these sensors typically include electrical energy meters, accelerometers, current sensors, etc., with wireless communication capabilities to transmit the collected data to an energy consumption analysis center.
The energy consumption analysis center is a centralized data processing and analysis center for receiving, storing and analyzing elevator energy consumption data collected from the sensors. The center is typically equipped with specialized software and hardware for real-time monitoring, data visualization, and energy consumption analysis strategies. At the energy consumption analysis center, engineers or operators can use the analysis results to learn about the energy consumption patterns of the elevator, find potential energy saving opportunities, and perform operational optimization and maintenance planning.
Embodiment 2 of the present invention differs from embodiment 1 in that the present embodiment describes a sensor network-based elevator energy consumption real-time monitoring system.
Fig. 2 shows a schematic structural diagram of the elevator energy consumption real-time monitoring system based on the sensor network, which comprises a connection judging module, a transmission abnormality evaluating module, a magnetic field influence evaluating module, a comprehensive transmission evaluating module and a transmission risk early warning module.
The connection judging module judges whether connection is established between the sensor and the energy consumption analysis center through the heartbeat signal.
When connection is established between the sensor and the energy consumption analysis center, the transmission abnormality assessment module analyzes the time sequence consistency condition of the sensor data and assesses the abnormal degree of the acquisition of the elevator energy consumption data; and analyzing the output state of the sensor, and evaluating the output performance of the sensor.
The magnetic field influence evaluation module judges the influence of the magnetic field intensity of the elevator on wireless transmission between the sensor and the energy consumption analysis center; and when the influence of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center reaches a trigger condition, evaluating the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center.
The comprehensive transmission evaluation module comprehensively analyzes the degree of abnormality in the acquisition of the elevator energy consumption data, the output performance of the sensor and the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center, and evaluates the stability of the wireless transmission between the sensor and the energy consumption analysis center.
The transmission risk early warning module analyzes the stability of wireless transmission between the sensor and the energy consumption analysis center within a period of time, and early warns the fault risk of the wireless transmission between the sensor and the energy consumption analysis center in advance.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, may be located in one place, or may be distributed over multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. The elevator energy consumption real-time monitoring method based on the sensor network is characterized by comprising the following steps of:
step S1: judging whether connection is established between the sensor and the energy consumption analysis center through heartbeat signals;
step S2: when connection is established between the sensor and the energy consumption analysis center, analyzing the time sequence consistency condition of the sensor data, and evaluating the abnormal degree of the acquisition of the elevator energy consumption data; analyzing the output state of the sensor, and evaluating the output performance of the sensor;
Step S3: judging the influence of the magnetic field intensity of the elevator on wireless transmission between the sensor and the energy consumption analysis center; when the influence of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center reaches a trigger condition, evaluating the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center;
step S4: comprehensively analyzing the abnormal degree of the acquisition of the elevator energy consumption data, the output performance of the sensor and the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center, and evaluating the stability of the wireless transmission between the sensor and the energy consumption analysis center;
step S5: and analyzing the stability of the wireless transmission between the sensor and the energy consumption analysis center within a period of time, and early warning the fault risk of the wireless transmission between the sensor and the energy consumption analysis center in advance.
2. The elevator energy consumption real-time monitoring method based on the sensor network according to claim 1, wherein the method comprises the following steps: in step S1, a heartbeat timeout threshold is set; when the energy analysis center receives the heartbeat signal of the sensor in the heartbeat timeout threshold, generating a connection normal signal; and when the energy analysis center does not receive the heartbeat signal of the sensor in the heartbeat timeout threshold, generating a connection failure signal.
3. The elevator energy consumption real-time monitoring method based on the sensor network according to claim 2, wherein the method comprises the following steps: in step S2, when a connection normal signal is generated, a recent data point set is set, where the recent data point set includes n data points actually received by the energy consumption analysis center nearest to the real-time;
acquiring time intervals of n-1 adjacent data points actually received by an energy consumption analysis center, which are included in a recent data point set, numbering the time intervals of each adjacent data point, and marking the number as w;
acquiring a preset time interval of corresponding sensor acquisition data; analyzing the deviation condition of the time interval of the adjacent data points in the recent data point set and the preset time interval of the corresponding sensor acquisition data, and calculating to obtain a sampling interval deviation value;
when the sampling interval offset value is greater than the deviation evaluation threshold value, generating a time sequence alarm signal;
and when the sampling interval offset value is smaller than or equal to the deviation evaluation threshold value, generating a time sequence normal signal.
4. The elevator energy consumption real-time monitoring method based on the sensor network according to claim 3, wherein the elevator energy consumption real-time monitoring method based on the sensor network is characterized by comprising the following steps of: setting a voltage monitoring interval; uniformly setting k monitoring points in time in a voltage monitoring interval, wherein each monitoring point obtains an output voltage value corresponding to a monitoring point of the sensor; k is an integer greater than 1;
Acquiring the number of monitoring points of which the output voltage value in the voltage monitoring interval deviates from a preset output voltage range, and marking the ratio of the number of the monitoring points of which the output voltage value in the voltage monitoring interval deviates from the preset output voltage range to the number of the monitoring points in the voltage monitoring interval as a voltage abnormality ratio;
acquiring a time interval between two adjacent monitoring points; acquiring the sum of time intervals between two adjacent monitoring points in a voltage monitoring interval, and marking the ratio of the sum of the time intervals between the two adjacent monitoring points in the voltage monitoring interval and the number of monitoring points, of which the output voltage value in the voltage monitoring interval deviates from a preset output voltage range, as an abnormal output clinging ratio;
performing unit removal processing on the voltage abnormality ratio and the abnormal output cling ratio, performing weighted summation on the voltage abnormality ratio and the abnormal output cling ratio after the unit removal processing, and calculating the abnormal value of the sensing output.
5. The elevator energy consumption real-time monitoring method based on the sensor network according to claim 4, wherein the elevator energy consumption real-time monitoring method based on the sensor network is characterized in that: in step S3, a magnetic field monitoring section is set; acquiring the magnetic field intensity around the sensor in the magnetic field monitoring interval, acquiring the time length of the magnetic field intensity around the sensor in the magnetic field monitoring interval being greater than the magnetic field intensity threshold value, and marking the ratio of the time length of the magnetic field intensity around the sensor in the magnetic field monitoring interval being greater than the magnetic field intensity threshold value to the time length corresponding to the magnetic field monitoring interval as the magnetic field influence ratio;
Setting a magnetic field influence ratio threshold; when the magnetic field influence ratio is smaller than or equal to a magnetic field influence ratio threshold value, generating a magnetic field influence neglect signal; when the magnetic field influence ratio is larger than the magnetic field influence ratio threshold, a magnetic field influence transmission signal is generated, and the triggering condition of influence of the magnetic field intensity on wireless transmission between the sensor and the energy consumption analysis center is achieved.
6. The elevator energy consumption real-time monitoring method based on the sensor network according to claim 5, wherein the elevator energy consumption real-time monitoring method based on the sensor network is characterized in that: when a magnetic field influence transmission signal is generated, analyzing the degree that the magnetic field intensity in the magnetic field monitoring interval is larger than a magnetic field intensity threshold value, specifically:
establishing a two-dimensional coordinate system by taking the time length corresponding to the magnetic field monitoring interval as an abscissa and taking the value of the magnetic field intensity in the magnetic field monitoring interval as an ordinate, and obtaining a change curve of the magnetic field intensity in the magnetic field monitoring interval;
and marking the area formed by the straight line corresponding to the magnetic field intensity threshold value and the magnetic field intensity change curve with the magnetic field intensity larger than the magnetic field intensity threshold value in the magnetic field monitoring interval as a magnetic field abnormality degree value.
7. The elevator energy consumption real-time monitoring method based on the sensor network according to claim 6, wherein the method comprises the following steps: in step S4, normalizing the sampling interval offset value, the sensing output abnormal value and the magnetic field abnormal degree value, and calculating a sensing energy consumption information early warning coefficient through the normalized sampling interval offset value, the sensing output abnormal value and the magnetic field abnormal degree value;
Setting an early warning first threshold value and an early warning second threshold value, wherein the early warning first threshold value is smaller than the early warning second threshold value;
when the early warning coefficient of the sensing energy consumption information is smaller than the early warning first threshold value, generating a sensing transmission normal signal;
when the early warning coefficient of the sensing energy consumption information is larger than or equal to the early warning first threshold value and the early warning coefficient of the sensing energy consumption information is smaller than or equal to the early warning second threshold value, generating a sensing transmission low risk signal;
and when the early warning coefficient of the sensing energy consumption information is larger than the two early warning thresholds, generating a sensing transmission high risk signal.
8. The elevator energy consumption real-time monitoring method based on the sensor network according to claim 7, wherein the elevator energy consumption real-time monitoring method based on the sensor network is characterized in that: in step S5, a failure risk monitoring section is set; acquiring the duty ratio condition of the sensing transmission low-risk signals in the fault risk monitoring interval:
when the sensing transmission high-risk signal does not exist in the fault risk monitoring interval, marking the ratio of the time length occupied by the sensing transmission low-risk signal in the fault risk monitoring interval to the time length corresponding to the fault risk monitoring interval as the transmission low-risk ratio;
generating a transmission risk signal when the transmission low risk duty cycle is greater than the low risk duty cycle threshold;
and when the transmission low risk ratio is smaller than or equal to the low risk ratio threshold, generating a transmission risk normal signal.
9. The elevator energy consumption real-time monitoring system based on the sensor network is used for realizing the elevator energy consumption real-time monitoring method based on the sensor network as set forth in any one of claims 1 to 8, and is characterized in that: the system comprises a connection judging module, a transmission abnormality evaluating module, a magnetic field influence evaluating module, a comprehensive transmission evaluating module and a transmission risk early warning module;
the connection judging module judges whether connection is established between the sensor and the energy consumption analysis center through a heartbeat signal;
when connection is established between the sensor and the energy consumption analysis center, the transmission abnormality assessment module analyzes the time sequence consistency condition of the sensor data and assesses the abnormal degree of the acquisition of the elevator energy consumption data; analyzing the output state of the sensor, and evaluating the output performance of the sensor;
the magnetic field influence evaluation module judges the influence of the magnetic field intensity of the elevator on wireless transmission between the sensor and the energy consumption analysis center; when the influence of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center is large, evaluating the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center;
the comprehensive transmission evaluation module comprehensively analyzes the degree of abnormality in the acquisition of the elevator energy consumption data, the output performance of the sensor and the influence degree of the magnetic field strength on the wireless transmission between the sensor and the energy consumption analysis center, and evaluates the stability of the wireless transmission between the sensor and the energy consumption analysis center;
The transmission risk early warning module analyzes the stability of wireless transmission between the sensor and the energy consumption analysis center within a period of time, and early warns the fault risk of the wireless transmission between the sensor and the energy consumption analysis center in advance.
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